Over the past two years dozens of newsrooms around the world have crafted policies and guidelines on how their editorial staff can or should — or cannot or should not — use artificial intelligence tools.

Those documents are tacit acknowledgement that AI, particularly generative AI like chatbots that can produce images and news stories at a keystroke, may fundamentally change how journalists do their work and how the public thinks about journalism.

Generative AI tools are based on large language models, which are trained on huge amounts of existing digital text often pulled from the web. Several news organizations are suing generative AI maker OpenAI for copyright infringement over the use of their news stories to train AI chatbots. Meanwhile, The Atlantic and Vox Media have signed licensing deals allowing OpenAI access to their archives.

Despite the litigation, some news organizations have used generative AI to create news stories, including the Associated Press for simple coverage of company earnings reports and college basketball game previews.

But others that have dabbled in AI-generated content have faced scrutiny for publishing confusing or misleading information, and the utility of generative AI in journalism is not obvious to everyone.

“The reality is that AI models can often prepare a decent first draft,” Julia Angwin, longtime tech reporter and newsroom leader, wrote recently in a New York Times op-ed. “But I find that when I use AI, I have to spend almost as much time correcting and revising its output as it would have taken me to do the work myself.”

To gain insight on what the future of AI and journalism might look like — and where the industry’s biggest challenges are — I reached out to Angwin, who has reported for The Wall Street Journal and ProPublica and in 2020 launched the award-winning nonprofit newsroom The Markup, which, among other things, covered recent AI developments.

In early 2023 Angwin left The Markup and founded Proof News, a nonprofit news outlet that uses the scientific method to guide its investigations. Angwin is also a 2023-2024 Walter Shorenstein Media and Democracy Fellow at Harvard Kennedy School’s Shorenstein Center on Media, Politics and Public Policy, where The Journalist’s Resource is housed.

Social media creators and trust in news

During her time at the Shorenstein Center, Angwin interviewed a panel of social media creators to find out what journalists can learn from how creators and influencers share information and build trust with audiences. This summer, Angwin will publish a discussion paper on the findings.

One important way social media creators build trust is by directly engaging with their audiences, she found.

At the same time, some news organizations have turned away from direct audience engagement online.

“Newsrooms have, for all sorts of legitimate reasons, turned off the comments section because it’s hard to moderate,” Angwin says. “It also does mean that there’s a feeling from the audience that traditional news is less accountable, that it’s less responsive.”

AI in journalism

Angwin is not optimistic that generative AI will be useful to journalists, though AI tools are “totally legit and accepted” for reporting that includes statistical analysis, she says. But Angwin points to several concerns for the future, including that the use of copyrighted content to train generative AI systems could disincentivize journalists from doing important work.

Here are a few other highlights from our conversation about journalistic trust and the future of AI in journalism:

• The news business isn’t ready. Competing in an information ecosystem with generative AI that creates plausible sounding (but sometimes untrue) text is a new frontier for news organizations, which will have to be even more attentive in showing audiences the evidence behind their reporting.
• To gain trust, journalists need to acknowledge what they don’t know. It’s OK for journalists not to know everything about a topic they’re covering or story they’re pursuing. In published work, be upfront with audiences about what you know and areas you’re still reporting.   
• When covering AI tools, be specific. Journalists covering AI topics need to know the types of AI tools out there — for example, generative versus statistical versus facial recognition. It’s important to clearly explain in your coverage which technology you are talking about.

The interview below has been edited for length and clarity.

Clark Merrefield: Some commentators have said AI is going to fundamentally change the internet. At this point it would be impossible to disentangle journalism and the internet. How would you characterize this moment, where AI is here and being used in some newsrooms? Is journalism ready?

Julia Angwin: Definitely I’d say we’re not ready. What we’re not ready for is the fact that there are basically these machines out there that can create plausible sounding text that has no relationship to the truth.

AI is inherently not about facts and accuracy. You’ll see that in the tiny disclaimer at the bottom of ChatGPT or any of those tools. They are about word associations. So for a profession that writes words that are meant to be factual, all of a sudden you’re competing in the marketplace — essentially, the marketplace of information — with all these words that sound plausible, look plausible and have no relationship to accuracy.

There’s two ways to look at it. One is we could all drown in the sea of plausible sounding text and lose trust in everything. Another scenario is maybe there will be a flight to quality and people will actually choose to go back to these mainstream legacy brand names and be like, “I only trust it if I saw it, you know, in the Washington Post.”

I suspect it’s not going to be really clear whether it’s either — it’s going to be a mix. In an industry that’s already under a lot of pressure financially — and, actually, just societally because of the lack of trust in news.

[AI] adds another layer of challenge to this already challenging business.

CM: In a recent investigation you found AI chatbots did a poor job responding to basic questions from voters, like where and when to vote. What sorts of concerns do you have about human journalists who are pressed for time — they’re on deadline, they’re doing a thousand things — passing along inaccurate, AI-generated content to audiences?

JA: Our first big investigation [at Proof News] was testing the accuracy of the leading AI models when it came to questions that voters might ask. Most of those questions were about logistics. Where should I vote? Am I eligible? What are the rules? When is the deadline for registration? Can I vote by text?

Or, in one ZIP code, it said there’s no place for you to vote, which is obviously not true.

Llama, the Meta [AI] model, had this whole thing, like, here’s how you vote by text: There’s a service in California called Vote by Text and here’s how you register for it. And it had all these details that sounded really like, “Oh, my gosh! Maybe there is a vote-by-text service!”

There is not! There is no way to vote by text!

Having experts involved made it easier to really be clear about what was accurate and what was not. The ones I’ve described were pretty clearly inaccurate, but there were a lot of edge cases where I would have probably been like, “Oh, it seems good,” and the election officials were like, “No.”

You kind of already have to know the facts in order to police them. I think that is the challenge about using [AI] in the newsroom. If you already knew the answer, then maybe you should have just written the sentence yourself. And if you didn’t, it might look really plausible, and you might be tempted to rely on it. So I worry about the use of these tools in newsrooms.

CM: And this is generative AI we’re talking about, right?

JA: Yes, and I would like to say that there is a real difference between generative AI and other types of AI. I use other types of AI all the time, like in data analysis — decision trees and regressions. And there’s a lot of statistical techniques that sort of technically qualify as AI and are totally legit and accepted.

Generative AI is just a special category and made of writing text, creating voice, creating images, where it’s about creation of something that humans used to only be able to create. And that is where I think we have a special category of risk.

CM: If you go to one of these AI chatbots and ask, “What time do I need to go vote and where do I vote?” it’s not actually searching for an answer to those questions, it’s just using the corpus of words that it’s based on to create an answer, right?

JA: Exactly. Most of these models are trained on data sets that might have data up until 2021 or 2022, and it’s 2024 right now. Things like polling places can change every election. It might be at the local school one year, and then it’s going to be at city hall the next year. There’s a lot of fluidity to things.

We were hoping that the models would say, “Actually, that’s not something I can answer because my data is old and you should go do a search, or you should go to this county elections office.” Some of the models did do that. ChatGPT did it more consistently than the rest. But, surprisingly, none of them really did it that consistently despite some of the companies having made promises that they were going to redirect those types of queries to trusted sources.

The problem is that these models, as you described them, they’re just these giant troves of data basically designed to do this are-these-words-next-to-each-other thing. When they rely on old data, either they were pulling up old polling places or they’re making up addresses. It was actually like they made up URLs. They just kind of cobbled together stuff that looked similar and made up things a lot of the time.

CM: You write in your founder’s letter for Proof News that the scientific method is your guide. Does AI fit in at all into the journalism that Proof News is doing and will do?

JA: The scientific method is my best answer to try to move on from the debate in journalism about objectivity. Objectivity has been the lodestar for journalism for a long time, and there’s a lot of legitimate reasons that people wanted to have a feeling of fairness and neutrality in the journalism that they’re reading.

Yet it has sort of devolved into what I think Wesley Lowry best describes as a performative exercise about whether you, as an individual reporter, have biases. The reality is we all have biases. So I find the scientific method is a really helpful answer to that conundrum because it’s all about the rigor of your processes.

Basically, are your processes rigorous enough to overcome the inherent bias that you have as a human? That’s why I like it. It’s about setting up rigorous processes.

Proof is an attempt to make that aspect the centerpiece. Using the scientific method and being data driven and trying to build large sample sizes when we can so that we have more robust results will mean we will do data analysis with statistical tools that will qualify as AI, for sure. There’s no question that will be in our future, and I’ve done that many times in the past.

I think that is fine — as I think it’s important to disclose those things. But those tools are well accepted in academia and research. Whenever I use tools like that, I always go to experts in the field, statisticians, to review my work before publishing. I feel comfortable with the use of that type of AI.

I do not expect to be using generative AI [at Proof News]. I just don’t see a reason why we would do it. Some of the coders that we work with, sometimes they use some sort of AI copilot to check their work to see if there’s a way to enhance it. And that, I think, is OK because you’re still writing the code yourself. But I don’t expect to ever be writing a headline or a story using generative AI.

CM: What is a realistic fear now that we’re adding AI to the mix of media that exists on the internet?

JA: Generative AI companies, which are all for-profit companies, are scraping the internet and grabbing everything, whether or not it is truly publicly available to them.

I am very concerned about the disincentive that gives for people to contribute to what we call the public square. There’s so many wonderful places on the internet, like Wikipedia, even Reddit, where people share information in good faith. The fact that there’s a whole bunch of for-profit companies hoovering up that information and then trying to monetize it themselves, I think that’s a real disincentive for people to participate in those public squares. And I think that makes a worse internet for everyone.

As a journalist, I want to contribute my work to the public. I don’t want it to be behind a paywall. Proof is licensed by Creative Commons, so anyone can use that information. That is the best model, in my opinion. And yet, it makes you pause. Like, “Oh, OK, I’m going to do all this work and then they’re going to make money off of it?” And then I’m essentially an unpaid worker for these AI companies.

CM: You’re a big advocate of showing your work as a journalist. When AI is added to that mix, does that imperative become even more critical? Does it change at all?

JA: It becomes even more urgent to show your work when you’re competing with a black box that creates plausible text but doesn’t show how it got that text.

Trust is declining — not just in journalistic institutions but in government, in corporations. We are in an era of distrust. This is where I take lessons from the [social media] creators because they don’t assume anyone trusts them. They just start with the evidence. They say, here’s my evidence and put it on camera. We have to get to a level of elevating all the evidence, and being really, really clear with our audiences.

CM: That’s interesting to go down to the story level, because that’s fundamentally what journalism is supposed to be about. The New York Times of the world built their reputation on the trust of their stories and also can lose it based on that, too.

JA: A lot of savvy readers have favorite reporters who they trust. They might not trust the whole institution, but they trust a certain reporter. That’s very similar to the creator economy where people have certain creators they trust, some they don’t.

We’re wired as humans to be careful and choose with our trust. I guess it’s not that natural to have trust in that whole institution. I don’t feel like it’s a winnable battle, at least not for me, to rebuild trust in giant journalistic institutions. But I do think there’s a way to build trust in the journalistic process. And so I want expose that process, make that process as rigorous as possible and be really honest with the audience.

And what that means, by the way, is be really honest about what you don’t know. There’s a lot of false certainty in journalism. Our headlines can be overly declarative. We tend to try to push our lead sentences to the max. What is the most declarative thing we can say? And that is driven a little bit by the demands of clickbait and engagement.

But that overdetermination also alienates the audience when they realize that there’s some nuance. One of the big pieces of our ingredients label is the limitations. What do we not know? What data would we need to make a better determination? And that’s where you go back to science, where everything is iterative — like, the idea is there’s no perfect truth. We’re all just trying to move towards it, right? And so we build on each other’s work. And then we admit that we need someone to build on ours, too.

CM: Any final thoughts or words of caution as we enter this brave new world of generative AI and journalism, and how newsrooms should be thinking about this?

JA: I would like it if journalists could work a little harder to distinguish different types of AI. The reality is there are so many kinds of AI. There’s the AI that is used in facial recognition, which is matching photos against known databases, and that’s a probability of a match.

There’s then the generative AI, which is the probability of how close words are to each other. There’s statistical AI, which is about predicting how a regression is trying to fit a line to a data set and see if there’s a pattern.

Right now everything is conflated into AI generally. It’s a little bit like talking about all vehicles as transportation. The reality is a train is really different than a truck, which is really different than a passenger car, which is really different than a bicycle. That’s kind of the range we have for AI, too. As we move forward journalists should start to distinguish a little bit more about those differences.

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As artificial intelligence infiltrates virtually every aspect of life, more states in the U.S. are seeking to regulate (or at least monitor) its use. Many are passing legislation, issuing policy rules or forming committees to inform those decisions. In some cases, that includes health insurance, where AI holds great promise to speed and improve administration but also brings potential for peril, including racial bias and omissions inherent in formulas used to determine coverage approvals.

Meanwhile, major health insurers Humana, Cigna, and UnitedHealth all face lawsuits alleging  that the companies improperly developed algorithms that guided AI programs to deny health care. The suit against Cigna followed a ProPublica story revealing “how Cigna doctors reject patients’ claims without opening their files.” The class action suits against United Health and Humana followed an investigative series by STAT, in which reporters revealed that multiple major health insurers had used secret internal rules and flawed algorithms to deny care.

Journalists should pay attention to guardrails governments are seeking to erect to prevent problematic use of AI — and whether they’ll ultimately succeed as intended. Both federal and state governments report they are working to prevent discrimination, a broad concern as AI systems become more sophisticated and help administrators make decisions, including what’s covered by a policy. Proposed state legislation and regulatory guidelines aim to require health insurance companies to be more transparent about how their systems were created, what specific data sets are fed into those systems and how the algorithms that instruct a program’s decision-making are created.

We’ve created this guide to help journalists understand the nascent regulatory landscape, including proposed state laws; which regulators are compiling and issuing guidelines; and what researchers have learned so far.                                       

Government efforts to regulate AI use among health insurers

Who regulates health insurers, and how, depends largely on the type of health insurance itself. Congress and the Biden administration are stepping up efforts to form a blueprint for AI use, including in health insurance.

State-level resolutions and legislation

For local journalists, this complex landscape provides an avenue rich with potential reporting opportunities.

According to the National Conference of State Legislatures, at least 40 states introduced or passed legislation aimed at regulating AI in the 2024 legislative session through March 17, with at least half a dozen of these actions tied to health care. Six states, Puerto Rico and the Virgin Islands adopted resolutions or enacted new laws.

That’s on top of 18 states and Puerto Rico’s adoption of resolutions or legislation tied to AI in 2023, according to data from the NCSL. Many states are modeling regulations to include guidance from the National Association of Insurance Commissioners (NAIC) issued in December 2023.

The Colorado Division of Insurance, for example, is mulling how to apply new rules adopted by the state legislature in 2021, which are designed to be a check for consumers on AI-generated decisions. It was the first state to target AI use in insurance, according to Bloomberg.

Colorado’s insurance commissioners have so far issued guidance for auto and life insurers under the statute. In recent months, commissioners held hearings and called for written comments to help form its approach to applying the new rules to health insurers, according to materials on the agency’s website.

Colorado’s legislation seeks to hold “insurers accountable for testing their big data systems – including external consumer data and information sources, algorithms, and predictive models — to ensure they are not unfairly discriminating against consumers on the basis of a protected class.”  In Colorado, protected class includes race, color, religion, national origin/ancestry, sex, pregnancy, disability, sexual orientation including transgender status, age, marital status and familial status, according to the state’s Civil Rights Division.

There isn’t yet a firm timeline for finalizing these rules for health insurance because the agency is still early in the process as it also works on life insurance, Vincent Plymell, the assistant commissioner for communications and outreach at the Colorado Division of Insurance, told The Journalist’s Resource.

In California, one bill sponsored by the California Medical Association would “require algorithms, artificial intelligence, and other software tools used for utilization review or utilization management decisions” be “fairly and equitably applied.” Earlier language that would have mandated a licensed physician supervise AI use for decisions to “approve, modify, or deny requests by providers” was struck from the bill.

In Georgia, a bill would require coverage decisions using AI be “meaningfully reviewed” by someone with authority to override them. Illinois,  New York,  Pennsylvania, and Oklahoma are also among states that introduced legislation tied to health care, AI and insurance.

Several states including Maryland, New York, Vermont and Washington state have issued guidance bulletins for insurers modeled after language crafted by the NAIC. The model bulletin, issued in December 2023, aims to set “clear expectations” for insurers when it comes to AI. The bulletin also has standard definitions for AI-related terms, like machine learning and large language models.

A group of NAIC members is also developing  a survey of health insurers on the issue.      

One concern insurers have is that rules may be different across states, Avi Gesser, a data security partner at the law firm Debevoise & Plimpton LLP, told Bloomberg Law.

“It would be a problem for some insurers if they had to do different testing for their algorithm state-by-state,” Gesser said in a November 2023 article. “Some insurers may say, ‘Well, maybe it’s not worth it—maybe we won’t use external data, or maybe we won’t use AI.’”

It’s useful for journalists to read published research to learn more about how artificial intelligence, insurers and health experts are approaching the issue technically, politically and legally. To help, we’ve curated and summarized several studies and scholarly articles on the topic.      

Research Roundup:    

Responsible Artificial Intelligence in Healthcare: Predicting and Preventing Insurance Claim Denials for Economic and Social Wellbeing
Marina Johnson, Abdullah Albizri and Antoine Harfouche. Information Systems Frontiers, April 2021.

The study: The authors examine AI models to help hospitals identify and prevent denials of patient insurance claims, aiming to cut the costs of appeals and reduce patient emotional distress. They examine six different kinds of algorithms to recommend the best model for predicting claim rejections and test it in a hospital. The authors use “white box” and “glass box” models, which reveal more data and mechanisms in an AI program than “black box” models, to develop what they label a Responsible Artificial Intelligence recommendation for an AI product to solve this problem.

In developing the proposed solution, the authors take into account five principles: transparency, justice, a no-harm approach, accountability and privacy.

To develop their proposal, the researchers used a dataset of 57,458 claims from a single hospital submitted to various insurance companies. They caution that their experiment involved using data from a single hospital.

The findings      
The solution the authors propose seeks to identify, in part, errors in coding and billing, medical needs, and mismatched codes for services and procedures to a patient’s diagnosis. Once flagged by the system the error can be fixed before submitting to an insurance company. That may spare the insured patient from going through the appeals process. The technical solution proposed by the authors “delivers a high accuracy rate” at about 83%, they write.

They recommend future research use data from insurance companies in which “many providers submit claims, providing more generalizable results.”

The authors write: “Insured patients suffering from a medical condition are overburdened if they have to deal with an appeal process for a denied claim. An AI solution similar to the one proposed in this study can prevent patients from dealing with the appeal process.”

Fair Regression for Health Care Spending
Anna Zink and Sherri Rose. Biometrics, September 2020.

The study: In this study, the authors examine and suggest alternative methods to predict spending in health insurance markets so insurers can provide fair benefits for enrollees in a plan, while more accurately gauging their financial risk. The authors examine “undercompensated” groups, people who are often underpaid by health insurance formulas, including people with mental illness or substance abuse disorders. They then suggest new tools and formulas for including these groups in regression analysis used to calculate fair benefits for enrollees. Regression analysis is a way of parsing variables in data to glean the most important factors in determining risk, what the impact is and how robust those factors are in calculations used to predict fair benefits and coverage.   

The findings: In their analysis, the authors use a random sample of 100,000 enrollees from the IBM MarketScan Research database in 2015 to predict total annual expenditures for 2016. Almost 14% of the sample were coded with a diagnosis of mental health and substance abuse disorder. When insurance companies “underpredict” spending for groups like these, “there is evidence that insurers adjust the prescription drugs, services, and providers they cover” and alter a plans’ benefit design “to make health plans less attractive for enrollees in undercompensated groups.”

The authors propose technical changes to formulas used to calculate these risks to produce what they find are more inclusive results for underrepresented groups, in this case those categorized as having mental health and substance use disorders. One of their suggested changes meant a 98% reduction in risk that insurers would be undercompensated, likely leading to an improvement in coverage for that group. It only increased insurer risk tied to predicting cost for enrollees without mental health and substance use disorders by about 4%, or 0.5 percentage points. The results could lead to “massive improvements in group fairness.”

The authors write: “For many estimators, particularly in our data analysis, improvements in fairness were larger than the subsequent decreases in overall fit. This suggests that if we allow for a slight drop in overall fit, we could greatly increase compensation for mental health and substance use disorders. Policymakers need to consider whether they are willing to sacrifice small reductions in global fit for large improvements in fairness.”

Additional reading: The authors outline this and two other studies tied to the topic in a November 2022 policy brief for the Stanford University Human Centered Artificial Intelligence group.

The Imperative for Regulatory Oversight of Large Language Models (or Generative AI) in Healthcare
Bertalan Meskó and Eric J. Topol. NPJ Digital Medicine, July 2023.

The article: In this article, the authors argue a new regulatory category should be created specifically for large language models in health care because they are different from previous artificial intelligence mechanisms in scale, capabilities and impact. LLMs can also adapt their responses in real-time, they note. The authors outline categories regulators could create to harness — and help control — LLMs.

By creating specific prescriptions for managing LLMs, regulators can help gain the trust of patients, physicians and administrators, they argue.

The findings: The authors write that safeguards should include ensuring:
• Patient data used for training LLMs are “fully anonymized and protected” from breaches, a “significant regulatory challenge” because violations could run afoul of privacy laws like the Health Insurance Portability and Accountability Act (HIPAA.)
• Interpretability and transparency for AI-made decisions, a “particularly challenging” task for “black box” models that use hidden and complex algorithms.
• Fairness and safeguards against biases. Biases can find their way into LLMs like Chat GPT-4 during model training that uses patient data, leading to “disparities in healthcare outcomes.”
• Establishing data ownership, something that’s hard to define and regulate.
• Users don’t become over-reliant on AI models, as some AI models can “hallucinate” and yield errors.

The authors write: “LLMs offer tremendous promise for the future of healthcare, but their use also entails risks and ethical challenges. By taking a proactive approach to regulation, it is possible to harness the potential of AI-driven technologies like LLMs while minimizing potential harm and preserving the trust of patients and healthcare providers alike.”

Denial—Artificial Intelligence Tools and Health Insurance Coverage Decisions
Michelle M. Mello and Sherri Rose, JAMA Health Forum, March 7, 2024

The article: In this Forum article, the authors, both professors of health policy,   call for national policy guardrails for AI and algorithmic use by health insurers. They note investigative journalism helped bring incidents to light in cases tied to Medicare Advantage as well as congressional hearings and class-action lawsuits against major health insurance companies.

The authors highlight and describe class-action suits against UnitedHealthcare and Humana that allege the companies pressured managers to discharge patients prematurely based on results from an AI algorithm. They also note Cigna, another insurance firm, is facing a class action suit alleging it used another kind of algorithm to deny claims at an average of 1.2 seconds each.
Algorithms can now be trained “at an unprecedented scale” using datasets such as Epic’s Cosmos, which represents some 238 million patients, the authors note.  But even developers may not know the mechanics behind — or why — an AI algorithm makes a recommendation.

The authors write: “The increased transparency that the CMS, journalists, and litigators have driven about how insurers use algorithms may help improve practices and attenuate biases. Transparency should also inspire recognition that although some uses of algorithms by insurers may be ethically unacceptable, others might be ethically obligatory. For example, eliminating the use of (imperfect) algorithms in health plan payment risk adjustment would undermine equity because adjusting payments for health status diminishes insurers’ incentive to avoid sicker enrollees. As the national conversation about algorithmic governance and health intensifies, insurance-related issues and concerns should remain in the foreground.”

Additional resources for journalists

• This research review and tip sheet from The Journalist’s Resource offers a primer, definitions and foundational research on racial bias in AI in health care. 

• The Association of Health Care Journalists  on its website features a guide to how health insurance works in each state. Created by Georgetown University’s Center on Health Insurance Reforms and supported by the Robert Wood Johnson Foundation, the guide provides useful statistics and resources that give journalists an overview of how health insurance works in each state. That includes a breakdown of different kinds of insurance that serve the population in each place, including how many people are covered by Medicare, Medicaid and employer-backed insurance. The guide can help inform journalist’s questions about the health insurance landscape locally, says      Joe Burns, the beat leader for health policy (including insurance) at AHCJ.

• The National Association of Insurance Commissioners has a map of which states adopt its model bulletin as well as a page documenting the work of its Big Data and Artificial Intelligence working group.

• Congress.gov features a clickable map of state legislature websites.

•The National Conference of State Legislatures tracks bills on AI for legislative sessions in each state. This list is current as of March 2024.

• The National Center for State Courts links to the websites of state-level courts.

• Here’s the Office of the National Coordinator for Health Information Technology’s final rule and fact sheets under the 21st Century Cures Act.

• Here’s a video and transcripts of testimony from a Feb. 8 U.S. Senate Finance hearing titled “Artificial Intelligence and Health Care: Promises and Pitfalls.”    

• KFF, formerly called the Kaiser Family Foundation, maintains a map showing what percentage of each state’s population is covered by health insurance.

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In a call with a long-time source, what stood out most to STAT reporters Bob Herman and Casey Ross was just how viscerally frustrated and angry the source was about an algorithm used by insurance companies to decide how long patients should stay in a nursing home or rehab facility before being sent home.­

The STAT stories had a far-reaching impact:

• The U.S. Senate Committee on Homeland Security and Government Affairs took a rare step of launching a formal investigation into the use of algorithms by the country’s three largest Medicare Advantage insurers.
• Thirty-two House members urged the Centers for Medicare and Medicaid Services to increase the oversight of algorithms that health insurers use to make coverage decisions.
• In a rare step, CMS launched its own investigation into UnitedHealth. It also stiffened its regulations on the use of proprietary algorithms and introduced plans to audit denials across Medicare Advantage plans in 2024.
• Based on STAT’s reporting, Medicare Advantage beneficiaries filed two class-action lawsuits against UnitedHealth and its NaviHealth subsidiary, the maker of the algorithm, and against Humana, another major health insurance company that was also using the algorithm. 
• Amid scrutiny, UnitedHealth renamed NaviHealth.

The companies never allowed an on-the-record interview with their executives, but they acknowledged that STAT’s reporting was true, according to the news organization.

Ross and Herman spoke with The Journalist’s Resource about their project and shared the following eight tips.

1. Search public comments on proposed federal rules to find sources.

Herman and Ross knew that the Centers for Medicare and Medicaid Services had put out a request for public comments, asking stakeholders within the Medicare Advantage industry how the system could improve.

There are two main ways to get Medicare coverage: original Medicare, which is a fee-for-service health plan, and Medicare Advantage, which is a type of Medicare health plan offered by private insurance companies that contract with Medicare. Medicare Advantage plans have increasingly become popular in recent years.

Under the Social Security Act, the public has the opportunity to submit comments on Medicare’s proposed national coverage determinations. CMS uses public comments to inform its proposed and final decisions. It responds in detail to all public comments when issuing a final decision.

The reporters began combing through hundreds of public comments attached to a proposed Medicare Advantage rule that was undergoing federal review. NaviHealth, the UnitedHealth subsidiary and the maker of the algorithm, came up in many of the comments, which include the submitters’ information.

“These are screaming all-caps comments to federal regulators about YOU NEED TO SOMETHING ABOUT THIS BECAUSE IT’S DISGUSTING,” Ross says.

“The federal government is proposing rules and regulations all the time,” adds Herman, STAT’s business of health care reporter. “If someone’s going to take the time and effort to comment on them, they must have at least some knowledge of what’s going on. It’s just a great tool for any journalist to use to figure out more and who to contact.”

The reporters also found several attorneys who had complained in the comments. They began reaching out to them, eventually gaining access to confidential documents and intermediaries who put them in touch with patients to show the human impact of the algorithm.

2. Harness the power of the reader submission box.

At the suggestion of an editor, the reporters added a reader submission box at the bottom of their first story, asking them to share their own experiences with Medicare Advantage denials.

The floodgates opened. Hundreds of submissions arrived.

By the end of their first story, Herman and Ross had confidential records and some patients, but they had no internal sources in the companies they were investigating, including Navihealth. The submission box led them to their first internal source.

The journalists also combed through LinkedIn and reached out to former and current employees, but the response rate was much lower than what they received via the submission box.

The submission box “is just right there,” Herman says. “People who would want to reach out to us can do it right then and there after they read the story and it’s fresh in their minds.”

3. Mine podcasts relevant to your story.

The reporters weren’t sure if they could get interviews with some of the key figures in the story, including Tom Scully, the former head of the Centers for Medicare and Medicaid Services who drew up the initial plans for NaviHealth years before UnitedHealth acquired it.

But Herman and another colleague had written previously about Scully’s private equity firm and they had found a podcast where he talked about his work. So Herman went back to the podcast — where he discovered Scully had also discussed NaviHealth.

The reporters also used the podcast to get Scully on the phone for an interview.

“So we knew we had a good jumping off point there to be like, ‘OK, you’ve talked about NaviHealth on a podcast, let’s talk about this,’” Herman says. “I think that helped make him more willing to speak with us.”

4. When covering AI initiatives, proceed with caution.

“A source of mine once said to me, ‘AI is not magic,’” Ross says. “People need to just ask questions about it because AI has this aura about it that it’s objective, that it’s accurate, that it’s unquestionable, that it never fails. And that is not true.”

AI is not a neutral, objective machine, Ross says. “It’s based on data that’s fed into it and people need to ask questions about that data.”

He suggests several questions to ask about the data behind AI tools:

• Where does the data come from?
• Who does it represent?
• How is this tool being applied?
• Do the people to whom the tool is being applied match the data on which it was trained? “If racial groups or genders or age of economic situations are not adequately represented in the training set, then there can be an awful lot of bias in the output of the tool and how it’s applied,” Ross says.
• How is the tool applied within the institution? Are people being forced to forsake their judgment and their own ability to do their jobs to follow the algorithm?

5. Localize the story.

More than half of all Medicare beneficiaries have Medicare Advantage and there’s a high likelihood that there are multiple Medicare Advantage plans in every county across the nation.

“So it’s worth looking to see how Medicare Advantage plans are growing in your area,” Herman says.

Finding out about AI use will most likely rely on shoe-leather reporting of speaking with providers, nursing homes and rehab facilities, attorneys and patients in your community, he says. Another source is home health agencies, which may be caring for patients who were kicked out of nursing homes and rehab facilities too soon because of a decision by an algorithm.

The anecdote that opens their first story involves a small regional health insurer in Wisconsin, which was using NaviHealth and a contractor to manage post-acute care services, Ross says.

“It’s happening to people in small communities who have no idea that this insurer they’ve signed up with is using this tool made by this other company that operates nationally,” Ross says.

There are also plenty of other companies like NaviHealth that are being used by Medicare Advantage plans, Herman says. “So it’s understanding which Medicare Advantage plans are being sold in your area and then which post-acute management companies they’re using,” he adds.

Some regional insurers have online documents that show which contractors they use to evaluate post-acute care services.

6. Get familiar with Medicare’s appeals databases

Medicare beneficiaries can contest Medicare Advantage denials through a five-stage process, which can last months to years. The appeals can be filed via the Office of Medicare Hearings and Appeals.

“Between 2020 and 2022, the number of appeals filed to contest Medicare Advantage denials shot up 58%, with nearly 150,000 requests to review a denial filed in 2022, according to a federal database,” Ross and Herman write in their first story. “Federal records show most denials for skilled nursing care are eventually overturned, either by the plan itself or an independent body that adjudicates Medicare appeals.”

There are several sources to find appeals data. Be mindful that the cases themselves are not public to protect patient privacy, but you can find the number of appeals filed and the rationale for decisions.

CMS has two quality improvement organizations, or QIOs, Livanta and Kepro, which are required to file free, publicly-available annual reports, about the cases they handle, Ross says.

Another company, Maximus, a Quality Improvement Contractor, also files reports on prior authorization cases it adjudicates for Medicare. The free annual reports include data on raw numbers of cases and basic information about the percentage denials either overturned or upheld on appeal, Ross explains.

CMS also maintains its own database on appeals for Medicare Part C (Medicare Advantage plans) and Part D, which covers prescription drugs, although the data is not complete, Ross explains.

7. Give your editor regular updates.

“Sprinkle the breadcrumbs in front of your editors,” Ross says.

“If you wrap your editors in the process, you’re more likely to be able to get to the end of [the story] before they say, ‘That’s it! Give me your copy,’” Ross says.

8. Get that first story out.

“You don’t have to know everything before you write that first story,” Ross says. “Because with that first story, if it has credibility and it resonates with people, sources will come forward and sources will continue to come forward.”

Read the stories

Denied by AI: How Medicare Advantage plans use algorithms to cut off care for seniors in need

How UnitedHealth’s acquisition of a popular Medicare Advantage algorithm sparked internal dissent over denied care

UnitedHealth pushed employees to follow an algorithm to cut off Medicare patients’ rehab care

UnitedHealth used secret rules to restrict rehab care for seriously ill Medicare Advantage patients

The post How they did it: STAT reporters expose how ailing seniors suffer when Medicare Advantage plans use algorithms to deny care appeared first on The Journalist's Resource.

Last month, a robocall impersonating U.S. President Joe Biden went out to New Hampshire voters, advising them not to vote in the state’s presidential primary election.  The voice, generated by artificial intelligence, sounded quite real.

“Save your vote for the November election,” the voice stated, falsely asserting that a vote in the primary would prevent voters from being able to participate in the November general election.  

The robocall incident reflects a growing concern that generative AI will make it cheaper and easier to spread misinformation and run disinformation campaigns. The Federal Communications Commission last week issued a ruling to make AI-generated voices in robocalls illegal.

Deepfakes already have affected other elections around the globe. In recent elections in Slovakia, for example, AI-generated audio recordings circulated on Facebook, impersonating a liberal candidate discussing plans to raise alcohol prices and rig the election. During the February 2023 Nigerian elections, an AI-manipulated audio clip falsely implicated a presidential candidate in plans to manipulate ballots. With elections this year in over 50 countries involving half the globe’s population, there are fears deepfakes could seriously undermine their integrity.  

Media outlets including the BBC and the New York Times sounded the alarm on deepfakes as far back as 2018. However, in past elections, including the 2022 U.S. midterms, the technology did not produce believable fakes and was not accessible enough, in terms of both affordability and ease of use, to be “weaponized for political disinformation.” Instead, those looking to manipulate media narratives relied on simpler and cheaper ways to spread disinformation, including mislabeling or misrepresenting authentic videos, text-based disinformation campaigns, or just plain old lying on air.  

As Henry Ajder, a researcher on AI and synthetic media writes in a 2022 Atlantic piece, “It’s far more effective to use a cruder form of media manipulation, which can be done quickly and by less sophisticated actors, than to release an expensive, hard-to-create deepfake, which actually isn’t going to be as good a quality as you had hoped.” 

As deepfakes continually improve in sophistication and accessibility, they will increasingly contribute to the deluge of informational detritus. They’re already convincing. Last month, The New York Times published an online test inviting readers to look at 10 images and try to identify which were real and which were generated by AI, demonstrating first-hand the difficulty of differentiating between real and AI-generated images. This was supported by multiple academic studies, which found that “faces of white people created by AI systems were perceived as more realistic than genuine photographs,” New York Times reporter Stuart A. Thompson explained.

Listening to the audio clip of the fake robocall that targeted New Hampshire voters, it is difficult to distinguish from Biden’s real voice.  

The jury is still out on how generative AI will impact this year’s elections. In a December blog post on GatesNotes, Microsoft co-founder Bill Gates estimates we are still “18-24 months away from significant levels of AI use by the general population” in high-income countries. In a December post on her website “Anchor Change,” Katie Harbath, former head of elections policy at Facebook, predicts that although AI will be used in elections, it will not be “at the scale yet that everyone imagines.” 

Beware the “Liar’s Dividend”

It may, therefore, not be deepfakes themselves, but the narrative around them that undermines election integrity. AI and deepfakes will be firmly in the public consciousness as we go to the polls this year, with their increased prevalence supercharged by outsized media coverage on the topic. In her blog post, Harbath adds that it’s “the narrative of what havoc AI could have that will have the bigger impact.” 

There are already notable examples of the liar’s dividend in political contexts. In recent elections in Turkey, a video tape surfaced showing compromising images of a candidate. In response, the candidate claimed the video was a deepfake when it was, in fact, real.

In April 2023, an Indian politician claimed that audio recordings of him criticizing members of his party were AI-generated. But a forensic analysis suggested at least one of the recordings was authentic.  

Kaylyn Jackson Schiff, Daniel Schiff, and Natalia Buen, researchers who study the impacts of AI on politics, carry out experiments to understand the impacts of the liar’s dividend on audiences. In an article forthcoming in the American Political Science Review, they note that in refuting authentic media as fake, bad actors will either blame their political opposition or “an uncertain information environment.”

Their findings suggest that the liar’s dividend becomes more powerful as people become more familiar with deepfakes. In turn, media consumers will be primed to dismiss legitimate campaign messaging. It is therefore imperative for the public to be confident that we can differentiate between real and manipulated media. 

Journalists have a crucial role to play in responsible reporting on AI. Widespread news coverage of the Biden robocalls and recent Taylor Swift deepfakes demonstrate that distorted media can be debunked, due to the resources of governments, technology professionals, journalists, and, in the case of Swift, an army of superfans.

This reporting should be balanced with a healthy dose of skepticism on the impact of AI in this year’s elections. Self-interested technology vendors will be prone to overstate its impact. AI may be a stalking horse for broader dis- and misinformation campaigns exploiting worsening integrity issues on these platforms. 

How lawmakers are trying to combat the problem

Lawmakers across states have introduced legislation to combat election-related AI-generated dis- and misinformation. These bills would require disclosure of the use of AI for election-related content in Alaska, Florida, Colorado, Hawaii, South Dakota, Massachusetts, Oklahoma, Nebraska, Indiana, Idaho and Wyoming. Most of the bills would require that information to be disclosed within specific time frames before elections. A bill in Nebraska would ban all deepfakes within 60 days of an election.

However, the introduction of these bills does not necessarily mean they will become law. Furthermore, their enforceability could be challenged on the grounds of free speech, based on positioning AI-generated content as satire. Moreover, penalties would only occur after the fact or be evaded by foreign entities.  

Social media companies hold the most influence in limiting the spread of false content, being able to detect and remove it from their platforms. However, the policies of major platforms, including Facebook, YouTube, and TikTok state they will only remove manipulated content for cases of “egregious harm” or if it aims to mislead people about voting processes. This is in line with a general relaxation in moderation standards, including repeals of 17 policies at the former three companies related to hate speech, harassment and misinformation in the last year. 

Their primary response to AI-generated content will be to label it as ‘AI-generated.’ For Facebook, YouTube and TikTok, this will apply to all AI-generated content, whereas for X (formally Twitter), these labels will apply to content identified as “misleading media,” as noted in recent policy updates.

This puts the onus on users to recognize these labels, which are not yet rolled out and will take time to adjust to. Furthermore, AI-generated content may evade the detection of already overstretched moderation teams and not be removed or labeled, creating false security for users. Moreover, with the exception of X (formerly Twitter)’s policy these labels do not specify whether a piece of content is harmful, only that it is AI-generated.

A deepfake made purely for comedic purposes would be labeled, but a manually altered video spreading disinformation might not. Recent recommendations from the oversight board of Meta, the company formerly known as Facebook, advise that “instead of focusing on how a distorted image, video or audio clip was created, the company’s policy should focus on the harm manipulated posts can cause.”  

The continued emergence of deepfakes is worrying, but they represent a new weapon in the arsenal of disinformation tactics deployed by bad actors rather than a new frontier. The strategies to mitigate the damage they cause are the same as before – developing and enforcing responsible platform design and moderation, underpinned by legal mandates where feasible, coupled with journalists and civic society holding the platforms accountable. These strategies are now more important than ever. 

The post How AI deepfakes threaten the 2024 elections appeared first on The Journalist's Resource.

In July 2022, just a few newsrooms around the world had guidelines or policies for how their journalists and editors could use digital tools that run on artificial intelligence. One year later, dozens of influential, global newsrooms had formal documents related to the use of AI.

In between, artificial intelligence research firm OpenAI launched ChatGPT, a chatbot that can produce all sorts of written material when prompted: lines of code, plays, essays, jokes and news-style stories. Elon Musk and Sam Altman founded OpenAI in 2015, with multibillion dollar investments over the years from Microsoft.

Newsrooms including USA Today, The Atlantic, National Public Radio, the Canadian Broadcasting Corporation and the Financial Times have since developed AI guidelines or policies — a wave of recognition that AI chatbots could fundamentally change the way journalists do their work and how the public thinks about journalism.

Research posted during September 2023 on preprint server SocArXiv is among the first to examine how newsrooms are handling the proliferating capabilities of AI-based platforms. Preprints have not undergone formal peer review and have not been published in an academic journal, though the current paper is under review at a prominent international journal according to one of the authors, Kim Björn Becker, a lecturer at Trier University in Germany and a staff writer for the newspaper Frankfurter Allgemeine Zeitung.

The analysis provides a snapshot of the current state of AI policies and documents for 52 news organizations, including newsrooms in Brazil, India, North America, Scandinavia and Western Europe.

Notably, the authors write that AI policies and documents from commercial news organizations, compared with those that receive public funding, “seem to be more fine-grained and contain significantly more information on permitted and prohibited applications.”

Commercial news organizations were also more apt to emphasize source protection, urging journalists to take caution when, for example, using AI tools for help making sense of large amounts of confidential or background information, “perhaps owing to the risk legal liability poses to their business model,” they write.

Keep reading to learn what else the researchers found, including a strong focus on journalistic ethics across the documents, as well as real world examples of AI being used in newsrooms — plus, how the findings compare with other recent research.

AI guidance and rules focus on preserving journalistic values

AI chatbots are a type of generative AI, meaning they create content when prompted. They are based on large language models, which themselves are trained on huge amounts of existing text. (OpenAI rivals Google and Meta in the past year have announced their own large language models).

So, when you ask an AI chatbot to write a three-act play, in the style of 19^th century Norwegian playwright Henrik Ibsen, about the struggle for human self-determination in a future dominated by robots, it is able to do this because it has processed Ibsen’s work along with the corpus of science fiction about robots overtaking humanity.

Some news organizations for years have used generative AI for published stories, notably the Associated Press for simple coverage of earnings reports and college basketball game previews. Others that have dabbled in AI-generated content have come under scrutiny for publishing confusing or misleading information.

The authors of the recent preprint paper analyzed the AI policies and guidelines, most of them related to generative AI, to understand how publishers “address both expectations and concerns when it comes to using AI in the news,” they write.

The most recent AI document in the dataset is from NPR, dated July 2023. The oldest is from the Council for Mass Media, a self-regulatory body of news organizations in Finland, dated January 2020.

“One thing that was remarkable to me is that the way in which organizations dealt with AI at this stage did exhibit a very strong sense of conserving journalistic values,” says Becker. “Many organizations were really concerned about not losing their credibility, not losing their audience, not trying to give away what makes journalism stand out — especially in a world where misinformation is around in a much larger scale than ever before.”

Other early adopters include the BBC and German broadcaster Bayerischer Rundfunk, “which have gained widespread attention through industry publications and conferences,” and “have served as influential benchmarks for others,” the authors write.

Many of the documents were guidelines — frameworks, or best practices for thinking about how journalists interact with and use AI, says Christopher Crum, a doctoral candidate at Oxford University and another co-author. But a few were prescriptive policies, Crum says.

Among the findings:

• Just over 71% of the documents mention one or more journalistic values, such as public service, objectivity, autonomy, immediacy — meaning publishing or broadcasting news quickly — and ethics.
• Nearly 70% of the AI documents were designed for editorial staff, while most of the rest applied to an entire organization. This would include the business side, which might use AI for advertising or hiring purposes. One policy only applied to the business side.
• And 69% mentioned AI pitfalls, such as “hallucinations,” the authors write, in which an AI system makes up facts.
• About 63% specified the guidelines would be updated at some point in the future — 6% of those “specified a particular interval for updates,” the authors write — while 37% did not indicate if or when the policies would be updated.
• Around 54% of the documents cautioned journalists to be careful to protect sources when using AI, with several addressing the potential risk of revealing confidential sources when feeding information into an AI chatbot.
• Some 44% allow journalists to use AI to gather information and develop story ideas, angles and outlines. Another 4% disallow this use, while half do not specify.
• Meanwhile, 42% allow journalists to use AI to alter editorial content, such as editing and updating stories, while 6% disallow this use and half do not specify.
• Only 8% state how the AI policies would be enforced, while the rest did not mention accountability mechanisms.

How the research was conducted

The authors found about two-thirds of the AI policy documents online and obtained the remainder through professional and personal contacts. About two-fifths were written in English. The authors translated the rest into English using DeepL, a translation service based on neural learning, a backbone of AI.

They then used statistical software to break the documents into five-word blocks, to assess their similarity. It’s a standard way to linguistically compare texts, Crum says. He explains that the phrase “I see the dog run fast” would have two five-word blocks: “I see the dog run,” and “see the dog run fast.”

If one document said, “I see the dog run fast” while another said, “I see the dog run quickly,” the first block of five words would be the same, the second block different — and the overall similarity between the documents would be lower than if the sentences were identical.

As a benchmark for comparison, the authors performed the same analysis on the news organizations’ editorial guidelines. The editorial guidelines were a bit more similar than the AI guidelines, the authors find.

“Because of the additional uncertainty in the [AI] space, the finding is that the AI guidelines are coalescing at a slightly lower degree than existing editorial guidelines,” Crum says. “The potential explanation might be, and this is speculative and not in the paper, something along the lines of, editorial guidelines have had more time to coalesce, whereas AI guidelines at this stage, while often influenced by existing AI guidelines, are still in the nascent stages of development.”

The authors also manually identified overarching characteristics of the documents relating to journalistic ethics, transparency and human supervision of AI. About nine-tenths of the documents specified that if AI were used in a story or investigation, that had to be disclosed.

“My impression is not that organizations are afraid of AI,” Becker says. “They encourage employees to experiment with this new technology and try to make some good things out of it — for example, being faster in their reporting, being more accurate, if possible, finding new angles, stuff like that. But at the same time, indicating that, under no circumstances, shall they pose a risk on journalistic credibility.”

AI in the newsroom is evolving

The future of AI in the newsroom is taking shape, whether that means journalists primarily using AI as a tool in their work, or whether newsrooms become broadly comfortable with using AI to produce publicly facing content. The Journalist’s Resource has used DALL.E 2, an OpenAI product, to create images to accompany human-reported and written research roundups and articles.

Journalists, editors and newsroom leaders should, “engage with these new tools, explore them and their potential, and learn how to pragmatically apply them in creating and delivering value to audiences,” researcher and consultant David Caswell writes in a September 2023 report for the Reuters Institute for the Study of Journalism at Oxford. “There are no best practices, textbooks or shortcuts for this yet, only engaging, doing and learning until a viable way forward appears. Caution is advisable, but waiting for complete clarity is not.”

The Associated Press in 2015 began using AI to generate stories on publicly traded firms’ quarterly earnings reports. But recently, the organization’s AI guidelines released during August 2023 specify that AI “cannot be used to create publishable content and images for the news service.”

The AP had partnered with AI-content generation firm Automated Insights to produce the earnings stories, The Verge reported in January 2015. The AP also used Automated Insights to generate more than 5,000 previews for NCAA Division I men’s basketball games during the 2018 season.

Early this year, Futurism staff writer Frank Landymore wrote that tech news outlet CNET had been publishing AI-generated articles. Over the summer, Axios’ Tyler Buchanan reported USA Today was pausing its use of AI to create high school sports stories after several such articles in The Columbus Dispatch went viral for peculiar phrasing, such as “a close encounter of the athletic kind.”

And on Nov. 27, Futurism published an article by Maggie Harrison citing anonymous sources alleging that Sports Illustrated has recently been using AI-generated content and authors, with AI-generated headshots, for articles on product reviews.

Senior media writer Tom Jones of the Poynter Institute wrote the next day that the “story has again unsettled journalists concerned about AI-created content, especially when you see a name such as Sports Illustrated involved.”

The Arena Group, which publishes Sports Illustrated, posted a statement on X the same day the Futurism article was published, denying that Sports Illustrated had published AI-generated articles. According to the statement, the product review articles produced by a third-party company, AdVon Commerce, were “written and edited by humans,” but “AdVon had writers use a pen or pseudo name in certain articles to protect author privacy — actions we strongly condemn — and we are removing the content while our internal investigation continues and have since ended the partnership.”

On Dec. 11, the Arena Group fired its CEO. Arena’s board of directors “met and took actions to improve the operational efficiency and revenue of the company,” the company said in a brief statement, which did not mention the AI allegations. Several other high level Arena Group executives were also fired last week, including the COO, according to the statement.

Many of the 52 policies reviewed for the preprint paper take a measured approach. About half caution journalists against feeding unpublished work into AI chatbots. Many of those that did were from commercial organizations.

For example, reporters may obtain voluminous government documents, or have hundreds of pages of interview notes or transcripts and may want to use AI to help make sense of it all. At least one policy advised reporters to treat anything that goes into an AI chatbot as published — and publicly accessible, Becker says.

Crum adds that the research team was “agnostic” in its approach — not for or against newsrooms using AI — with the goal of conveying the current landscape of newsroom AI guidelines and policies.

Themes on human oversight in other recent research

Becker, Crum and their coauthor on the preprint, Felix Simon, a communication researcher and doctoral student at Oxford, are among a growing body of scholars and journalists interested in informing how newsrooms use AI.

In July, University of Amsterdam postdoctoral researcher Hannes Cools and Northwestern University communications professor Nick Diakopoulos published an article for the Generative AI in the Newsroom project, which Diakopoulos edits, examining publicly available AI guidelines from 21 newsrooms.

Cools and Diakopoulos read the documents and identified themes. The guidelines generally stress the need for human oversight. Cools and Diakopoulos examined AI documents from many of the same newsrooms as the preprint authors, including the CBC, Insider, Reuters, Nucleo, Wired and Mediahuis, among others.

“At least for the externally facing policies, I don’t see them as enforceable policies,” says Diakopoulos. “It’s more like principal statements: ‘Here’s our goals as an organization.’

As for feeding confidential material into AI chatbots, Diakopoulos says that the underlying issue is about potentially sharing that information with a third party — OpenAI, for example — not in using the chatbot itself. There are “versions of generative AI that run locally on your own computer or on your own server,” and those should be unproblematic to use as a journalistic tool, he says.

“There was also what I call hybridity,” Diakopoulos says. “Kind of the need to have humans and algorithms working together, hybridized into human-computer systems, in order to keep the quality of journalism high while also leveraging the capabilities of AI and automation and algorithms for making things more efficient or trying to improve the comprehensiveness of investigations.”

For local and regional newsrooms interested in developing their own guidelines, there may be little need to reinvent the wheel. The Paris Charter, developed among 16 organizations and initiated by Reporters Without Borders, is a good place to start for understanding the fundamental ethics of using AI in journalism, Diakopoulos says.

Examples of AI-related newsroom guidelines

Click the links for examples of media organizations that have created guides for journalists on using AI to produce the news. Has your newsroom posted its AI guidelines online? Let us know by sending a link to clark_merrefield@hks.harvard.edu.

Associated Press | Bayerischer Rundfunk | BBC | Council of Europe | Financial Times | The Guardian | Insider | New York Times | Radio Television Digital News Association | Wired

The post Researchers compare AI policies and guidelines at 52 news organizations around the world appeared first on The Journalist's Resource.

The rapid spread of health misinformation online since the start of the COVID-19 pandemic has spurred more studies and articles on the topic than ever before. But research and data are lagging on the impact and spread of visual health misinformation, including manipulated photos, videos and charts, according to a commentary published in August 2022 in Science Communication.

Visual misinformation is “visual content that is typically presented alongside text or audio that contributes to false or inaccurate presentations of information,” says Katie Heley, the commentary’s lead author and a cancer prevention fellow in the Health Communication and Informatics Research Branch of the National Cancer Institute.

With rapid advances in technology and proliferation of social media, mobile devices and artificial intelligence, it’s becoming easier to create and spread visual content that’s inaccurate and misleading, Heley and her colleagues write in “Missing the Bigger Picture: The Need for More Research on Visual Health Misinformation.”

Moreover, existing content moderation tools are mostly designed to catch misinformation in texts and not images and videos, making it difficult to catch and stop the spread of visual misinformation.

Researchers are working to better understand how widespread visual misinformation is and how it impacts consumers compared with misinformation spread via the written word. Recent studies, including a 2021 paper published in International Journal of Press/Politics, find that much COVID-19 misinformation contains visual elements, as does misinformation in general.

It’s important to pay attention to visual health misinformation, because failing to do so may undermine efforts to fully understand health misinformation in general and hamper efforts to develop effective solutions to fight it, according to Heley and her colleagues.

Journalists also have an important role. They are encouraged to be mindful of the existence of manipulated health images, videos and charts, and may want to inform their audience about the issue, “so that, hopefully, the public is a little more aware that it’s relatively easy now to edit or fabricate images,” says Heley.

Below is a primer on visual health misinformation and a roundup of relevant and noteworthy research.

Three categories of visual misinformation

Visual misinformation is not a new concept. For decades, researchers, particularly in the fields of communication, journalism and advertising, have been studying visual misinformation types and techniques, including photo manipulation, misleading images and visual deception. But recent advances in technology, including software and apps and proliferation of social media platforms have made image manipulation easier and more accessible. This is especially dangerous in the health space, hampering public health efforts like vaccination campaigns.

“If we just quickly think through the history of photography, once it became available, it became a tool of truth telling, of showing what a war really looked like,” says Stefanie Friedhoff, associate professor of the Practice of Health Services, Policy and Practice at Brown University. “That instinct has been built over almost 100 years, and all of a sudden, we find ourselves in an environment where pictures may or may not tell the truth. We’re just at the onset of understanding how much images are being manipulated and are being used to manipulate people.”

Visual content is manipulated in different ways. In their commentary, Heley and her colleagues break visual misinformation into three categories:

Visual recontextualization is the use of visual content that is untouched, unedited, and generally authentic, but is presented alongside text or audio that provides inaccurate or misleading information or context.

One example the authors write about in their commentary is a video, posted online, that implied athletes were collapsing after being vaccinated against COVID-19. The video depicted real incidents of athletes fainting or falling for reasons other than vaccination, but it was paired with text suggesting these incidents were the result of COVID-19 vaccination, to create a false narrative. Several athletes in the video had not yet been vaccinated against COVID-19 or, if they had been vaccinated, the fainting or falling was found to be unrelated, according to the authors..

Visual recontextualization could also occur with data visualizations, such as a graph presented with a misleading title.

The second category is visual manipulation. It refers to visual content that’s been modified by tools such photo editing software to impact how the image is interpreted. It also includes distorting charts and graphs by manipulating elements such as the axes.

“Of the three categories of visual misinformation, visual manipulations are likely the most wide-ranging in terms of the strategies used (e.g., enhancing image quality and changing image elements) and the goals of the creator,” the authors write.

One example is the fashion industry’s extensive history of manipulating photographs of models to make them appear thinner, a practice now common on social media platforms. The practice could change how viewers perceive realistic body types and how satisfied they are with their own bodies.

Another specific example of visual manipulation is the use of photo-editing techniques to add the fictional label “Center for Global Human Population Reduction” to a picture of the Bill & Melinda Gates Foundation Discovery Center’s signage, which could fool viewers viewers into believing that the center’s stated goal is to support depopulation efforts or to harm the public in some way, the authors write.

Manipulated videos may also be an especially effective tool for health misinformation since the perceived realism of videos generally increases their credibility, the authors note.

The third category, visual fabrication, refers to visual content that’s not real, even though it is likely produced with representations of people, events, or things that make it appear as authentic, legitimate information. Deepfakes fall in this category. Deepfakes are visuals that have been digitally manipulated such that it’s difficult for most viewers to realize that they’re fake.

Visual fabrications include face swapping, lip-syncing or voice synthesis, in which fabricated words or new speech content are merged with video.

The authors note that several deepfake videos portraying political figures such as Former President Barack Obama and Russian President Vladimir Putin have emerged in recent years, showing them giving fictitious addresses. “It is not hard to imagine a video of a public figure being similarly fabricated in the service of health misinformation efforts,” they write. Heley and her colleagues add: “The unique potential of these videos to cause harm — for example, by providing convincing ‘evidence’ to support false claims or co-opting trusted messengers — and the fact that technologies to create deepfakes are becoming more accessible, suggest that visual fabrication merits greater consideration going forward,”

Friedhoff highlighted two other examples of visual misinformation: Memes and well-produced videos that present themselves as “documentaries.”

“We obviously live in the era of the meme,” adds Friedhoff, co-founder and co-director of the Information Futures Lab, a new lab at Brown University working to combat misinformation, outdated communication practices and foster innovation. “A picture is worth a thousand words, or communicates a thousand words, and using that allows those that are trying to spread misinformation to reach people quickly and intuitively.”

There are also documentaries on topics such as COVID-19, which are professionally produced, featuring individuals who say they are experts in the fields, but are in fact messengers of misinformation.

Friedhoff says in many cases, visual misinformation should be called disinformation, because it is intentionally created to manipulate people in a certain way.

“At the same time, we want to be mindful around really distinguishing well-produced manipulative content from honest mistakes, or people discussing their views and issues, which can also come across as misinformation,” says Friedhoff.

Visual misinformation versus visual health misinformation

There are similarities and differences between visual health misinformation and other types of visual misinformation such as political visual misinformation, says Heley.

Visuals may be used as evidence for claims in all kinds of misinformation.

But in misinformation about health and medicine, well-known icons, images and aesthetics such as a person in a lab coat, a medical chart or graph, an anatomical drawing or medical illustration, may be used to mislead an audience.

“The use of images such as these, that are so strongly associated with health, science and medicine, may provide a scientific frame and lend legitimacy to false claims,” says Heley.

Visual content may serve several functions within health misinformation messages, Heley and colleagues write. They include implying inaccurate connections between verbal and visual information; misrepresenting or impersonating authoritative institutions; leveraging visual traditions and conventions of science to suggest the information presented is evidence-based; and providing visual evidence to support a false claim.

A growing threat

Heley and her colleagues list several reasons why it’s important to pay attention to visual misinformation:

• Visual content is ubiquitous on social media, especially on platforms like Instagram and TikTok.
• Visual content is powerful in reach and influence. It is engaging and frequently shared. “Research also suggests that compared to written content alone, the addition of visual content enhances emotional arousal and, in some cases, persuasive impact — advantages that are concerning in the context of misinformation,” the authors write.
• Visual content is more memorable than messages without visual components. Such content captures the attention better, is better understood, remembered longer and is recalled better.
• Visual content can transcend language and literacy barriers, which “may facilitate the spread of visual misinformation across different cultural and linguistic contexts as well as among individuals with lower literacy levels,” they write.
• Visual manipulations are hard to detect, so people often don’t notice them or easily overlook them and often accept them as reality. “There’s something called the realism heuristic, particularly with photos and videos,” says Heley. “People may accept visuals as reality and so the misinformation may be especially convincing to people or people may treat it with less scrutiny or with a less critical eye to it than text alone, unfortunately.”
• Also, people may not be great at detecting manipulated images, and if they do identify a manipulated imaged, they may not be very good at locating what the manipulation is, says Heley.

Meanwhile, visual misinformation detection tools are either not widely used or aren’t available to and accessible to most people. These tools include reverse image searches, detection software, and technological approaches like blockchain to maintain a record of contextual information about a given photo or video.

“A number of these are promising, but the challenge is that they have limitations, with a big one being that none of them offer necessarily complete detection accuracy,” says Heley. “And for a number of them, they need to really be widely adopted to be effective” in thwarting misinformation.

Also, most content moderation technologies are not yet built to tackle visuals, Friedhoff adds.

“One aspect that’s important is that [visual misinformation] is increasingly being used to circumvent moderation technology, which is often word-based,” she says. “So the [artificial intelligence software] that looks through specific words and find potentially questionable content that then gets pushed in front of human content moderators, that is whole lot harder to do for visual materials.”

Audio misinformation is another growing area of threat, where spoken words are altered and manipulated.

“With audio, we’re now where we were with visual misinformation two or three years ago, because we realize there’s so much audio content,” said Friedhoff. “I’m very sure that we’re going to see more research on that.”

Challenges in research

Research on the spread and impact of visual misinformation, and how to counter it, is lagging behind the problem itself.

Existing research on visual health misinformation, and misinformation in general, mostly focuses on identifying and classifying misinformation on specific platforms and on specific topics. There are studies that have shown the presence of health misinformation on social media platforms. But there’s a notable gap in literature about the role of visual content in health misinformation. And almost no work has been done to study the impact of visual misinformation specifically or to develop specific solutions to counter it, Heley and her colleagues write.

There are several reasons for the dearth in studies.

Compared with the written word, visuals are complex, which makes them more difficult to study and compare.

“So, if you think about even a single image, there are a lot of variables,” says Heley. “What’s the type of image? Is it a graphic? Is it a realistic photo? Is it a cartoon? What are the colors? What’s the setting? Are there people? What are their facial expressions?”

And there is a need for more tools to study visual misinformation.

“With texts, we have automated methods such as natural language processing that help to understand text and spoken words,” says Heley. “We don’t always have comparable tools in the visual space. And a lot of researchers may not necessarily have the expertise in visual methods or access to the technology, such as eye-tracking devices, that they need to conduct this kind of research.”

In their commentary, Heley and her colleagues list several future areas of research, including developing and deploying manipulated visual media detectors.

“More research is needed to understand whether certain populations are more likely to be exposed to visual misinformation (e.g., due to the nature of the platforms they use), whether certain groups are more likely to be misled by visual misinformation (e.g., due to lower levels of health, digital, or graph literacy), and whether greater vulnerability to visual misinformation ultimately contributes to disparities in health outcomes,” they write.

Advice to journalists

Know that visual misinformation is a thing and it’s an important part of people’s information diet at this point, Friedhoff says.

When reporting on communities, try to find out what people’s information diet is and what type of content they’ve seen a lot of, she advises. “How can we interpret the moment that we’re in if we’re not connected to the kinds of stories and misinformation that people see?”

Heley encourages journalists to be aware of visual misinformation and to stay as vigilant as possible by using tools such as Google reverse image search to verify content.

“I think visual misinformation will continue to be a concern,” says Heley. “I don’t know exactly how it will change and what shape it will take but I think all of the indications around visual content and then the changes in technology are pointing” toward an increase in visual misinformation.

Research roundup

Beyond (Mis)Representation: Visuals in COVID-19 Misinformation
J. Scott Brennen, Felix M. Simon and Rasmus Kleis Nielsen. International Journal of Press/Politics. January 2021.

Researchers find visuals in more than half of the 96 pieces of online misinformation analyzed explicitly served as evidence for false claims, most of which are mislabeled rather than manipulated.

“Scholars would be well served by attending to the ways that visuals — whether taken out of context or manipulated — can work to ground and support false or misleading claims,” they write.

They also identify three distinct functions of visuals in coronavirus misinformation: “Illustration and selective emphasis, serving as evidence, and impersonating authorities.”

Fighting Cheapfakes: Using a Digital Media Literacy Intervention to Motivate Reverse Search of Out-of-Context Visual Misinformation
Sijia Qian, Cuihua Shen and Jingwen Zhang. Journal of Computer-Mediated Communication, November 2022.

The authors designed a digital media literacy intervention that motivates and teaches users to reverse search news images when they encounter news posts on social media. Their study included 597 participants.

They define “cheapfakes” as out-of-context visual misinformation or visual recontextualization, which is the practice of using authentic and untouched images in an unrelated context to misrepresent reality.

Their finding suggests “while exposure to the intervention did not influence the ability to identify accurately attributed and misattributed visual posts, it significantly increased participants’ intention of using reverse image search in the future, which is one of the best visual misinformation detection methods at the moment.”

Visual Mis- and Disinformation, Social Media, and Democracy
Viorela Dan, et al. Journalism & Mass Communication Quarterly, August 2021.

In this essay, the authors write “(Audio)visual cues make disinformation more credible and can help to realistically embed false storylines in digital media ecologies. As techniques for (audio)visual manipulation and doctoring are getting more widespread and accessible to everyone, future research should take the modality of disinformation, its long-term effects, and its embedding in fragmented media ecologies into account.”

Internet Memes: Leaflet Propaganda of the Digital Age
Joshua Troy Nieubuurt. Frontiers in Communication, January 2021.

The article is an exploration of internet memes as the latest evolution of leaflet propaganda used in digital persuasion. “In the past such items were dropped from planes, now they find their way into social media across multiple platforms and their territory is global,” the author writes.

A Picture Paints a Thousand Lies? The Effects and Mechanisms of Multimodal Disinformation and Rebuttals Disseminated via Social Media
Michael Hameleers, et al. Political Communication, February 2020.

Researchers exposed 1,404 U.S. participants to visual disinformation content related to refugees and school shootings. They find “partial evidence that multimodal disinformation was perceived as slightly more credible than textual disinformation.”

“Fact checkers may offer a potential remedy to the uncontrolled spread of manipulated images,” they write. “In line with this, we found that fact checkers can be used to discredit disinformation, which is in line with extant research in the field of misinformation.”

Seeing Is Believing: Is Video Modality More Powerful in Spreading Fake News via Online Messaging Apps?
S. Shyam Sundar, Maria D. Molina and Eugene Cho. Journal of Computer-Mediated Communication, November 2021.

In the study, 180 participants from rural and urban areas in and around Delhi and Patna in India were exposed to fake news via WhatsApp through text, audio or visual messages.

Results show “users fall for fake news more when presented in video form,” the authors write. “This is because they tend to believe what they see more than what they hear or read.”

Images and Misinformation in Political Groups: Evidence from WhatsApp in India
Kiran Garimella and Dean Eckles. Misinformation Review, July 2020.

Researchers collected 2,500 images from public WhatsApp groups in India and find that image misinformation is highly prevalent, making up 13% of all images shared in the groups.

They categorize the image misinformation into three categories: images taken out of context, photoshopped images and memes.

They also developed machine learning models to detect image misinformation. But, “while the results can sometimes appear promising, these models are not robust to [adapt to] changes over time,” they write.

Prevalence of Health Misinformation on Social Media: Systematic Review
Victor Suarez-Lledo and Javier Alvarez-Galvez. Journal of Medical Internet Research, January 2021.

The authors review 69 studies and find “the greatest challenge lies in the difficulty of characterizing and evaluating the quality of the information on social media. Knowing the prevalence of health misinformation and the methods used for its study, as well as the present knowledge gaps in this field will help us to guide future studies and, specifically, to develop evidence-based digital policy action plans aimed at combating this public health problem through different social media platforms.”

A Global Pandemic in the Time of Viral Memes: COVID-19 Vaccine Misinformation and Disinformation on TikTok
Corey Basch, et al. Human Vaccine & Immunotherapeutics, March 2021.

Researchers identified 100 trending videos with the hashtag #covidvaccine, which together had 35 million views. In total, 38 videos “Discouraged a Vaccine” and 36 “Encouraged a Vaccine.”

“While a slightly larger number of posts discouraged versus encouraged a COVID-19 vaccine, the more troubling aspect of the discouraging posts was the display of a parody/meme of an adverse reaction, even before the vaccine was being distributed to the public. We believe this reflects a deliberate and dangerous effort to communicate anti-vaccination sentiment,” the authors write.

Additional reading

Fighting fake news: 5 free (but powerful) tools for journalists
Faisal Kalim. What’s New in Publishing, July 2019.

These 6 tips will help you spot misinformation online
Alex Mahadevan. Poynter, December 2021.

How to Spot Misinformation Online
A free online course by Poynter Institute.

Fighting Disinformation Online: A Database of Web Tools
December 2019, Rand Corporation.

10 ways to spot online misinformation
H. Colleen Sinclair. The Conversation, September 2020.

Fact checking tools
A collection by the Journalist’s Toolbox, presented by the Society of Professional Journalists.

The Media Manipulation Casebook
The Technology and Social Change project at Shorenstein Center for Media, Politics and Public Policy.

The post Visual health misinformation: A primer and research roundup appeared first on The Journalist's Resource.

Artificial intelligence has come to stay in the health care industry. The term refers to a constellation of computational tools that can comb through vast troves of data at rates far surpassing human ability, in a way that can streamline providers’ jobs. Some types of AI commonly found in health care already are:

• Machine learning AI, where a computer trains on datasets and “learns” to, for example, identify patients who would do well with a certain treatment.
• Natural language processing AI, which can identify the human voice, and might transcribe a doctor’s clinical notes.
• Rules-based AI, where computers train to act in a specific way if a particular data point shows up–these kinds of AI are commonly used in electronic medical records to perhaps flag a patient who has missed their last two appointments.

Regardless of the specific type of AI, these tools are generally capable of making a massive, complex industry run more efficiently. But several studies show it can also propagate racial biases, leading to misdiagnosis of medical conditions among people of color, insufficient treatment of pain, under-prescription of life-affirming medications, and more. Many patients don’t even know they’ve been enrolled in healthcare algorithms that are influencing their care and outcomes.

A growing body of research shows a paradox, however. While some algorithms do indeed exacerbate inequitable medical care, other algorithms can actually close such gaps.

Popular press tends to cover AI in medicine only when something goes wrong. While such reports are critical in holding institutions to account, they can also paint the picture that when AI enters health care, trouble is always around the corner. If done correctly, AI can actually make health care fairer for more people.

Historically, much of the research in the medical sciences and in the biological sciences has relied on subject pools of white—often male—people of European ancestry. These foundational studies on everything from normal internal body temperature to heart disease become the stuff of textbooks and trainings that doctors, nurses, and other health care professionals engage with as they move up the professional ladder.

However, those studies offer a limited, one-size-fits-all view of human health that opens the door to racial bias—which patients get treated and how. The most easily graspable example of this type of knowledge gone wrong is consulting images of white skin to diagnose dermatological diseases across all skin types, when such diseases may manifest in unique ways depending on the pigmentation of someone’s skin.

When AI is trained by data that lack diversity, then it is more likely to mimic the same racial bias that healthcare professionals can themselves exhibit. A poorly structured AI training dataset is no better (and in fact is sometimes worse) than a human with a medical degree predicated on lessons learned about the health of primarily white patients.

On the flipside, when AI is trained on datasets that include information from a diverse population of patients, it can help move the health care field away from deep-seated biases.

Below are summaries of some of the research on the intersection of AI and race.  

Dissecting Racial Bias in an Algorithm Used to Manage the Health of Populations

Ziad Obermeyer, Brian Powers, Christine Vogeli, and Sendhil Mullainathan. Science, October 2019.

What the researchers focused on: This study dove into how a nationally circulated health care algorithm perpetuated the under-serving of Black patients as compared with white patients. Such algorithms have the potential to do immense harm, by replicating the same racial biases in play by humans, but at an even more massive scale, the authors write.

What they found: Commercially applied risk-prediction algorithms are among the most common types of AI the health care industry currently uses. They’re applied to the care of some 200 million Americans every year. In this study, researchers show one unnamed algorithm assigned Black patients the same level of health risk as white patients, when in reality the Black patients were sicker.

The researchers learned that the machine-learning algorithm had trained itself to to see health care costs as a proxy for a patient’s level of health, when in reality it is reflective of the health care industry’s inequitable investment in some patient populations over others.

In other words, the algorithm assumed that because it cost hospitals less to care for Black patients, Black patients were healthier and required less care. However, hospital costs are lower for Black patients even when they are sicker than white patients, because hospitals funnel fewer resources toward the care of sick Black patients. The researchers suggest that training the algorithm not to equate cost with health would undo this tripwire.   

What researchers did with their findings: “After completing the analyses described above, we contacted the algorithm manufacturer for an initial discussion of our results,” the authors write. “In response, the manufacturer independently replicated our analyses on its national dataset of 3,695,943 commercially insured patients. This effort confirmed our results—by one measure of predictive bias calculated in their dataset, Black patients had 48,772 more active chronic conditions than White patients, conditional on risk score—illustrating how biases can indeed arise inadvertently.”

Researchers then began experimenting with solutions with the algorithm manufacturer and have already made improvements in the product.

“Of course, our experience may not be typical of all algorithm developers in this sector,” they write. “But because the manufacturer of the algorithm we study is widely viewed as an industry leader in data and analytics, we are hopeful that this endeavor will prompt other manufacturers to implement similar fixes.”

AI Recognition of Patient Race in Medical Imaging: A Modelling Study

Judy Wawira Gichoya; et al. The Lancet: Digital Health, May 2022.

What the researchers focused on: Previous research has shown that AI can be trained to detect a person’s race from medical images, even though human experts who are looking at the images aren’t able to tell the patient’s race just from looking at those images. The authors wanted to find out more about AI’s ability to recognize a patient’s race from medical images.           They analyzed a total of 680,201 chest X-rays across 3 datasets where Black patients comprised 4.8% to 46.8% of the subjects, white patients 42.1% to 64.1%, Asian patients 3.6% to 10.8%; 458,317 chest CTs also across 3 datasets where Black patients comprised 9.1% to 72% of the subjects, white patients 28% to 90.9% and Asian patients were unrepresented; 691 digital radiography X-rays where Black patients comprised 48.2% of the subjects, white patients 51.8%, and Asian patients were unrepresented; 86,669 breast mammograms where Black patients comprised 50.4% of the subjects, white patients 49.6% and Asian patients were unrepresented; and 10,358 lateral c-spine X-rays where Black patients comprised 24.8% of the subjects, white patients 75.2%, and Asian patients were unrepresented. The images themselves contained no racial information and represented different degrees of image clarity, full and cropped views and other variations.   

What they found: The deep learning model was able to identify a patient’s race accurately from medical images that contained no identifiable racial information. Researchers thought perhaps the model was learning to do this by matching known health outcomes with racial information.

There is “evidence that Black patients have a higher adjusted bone mineral density and a slower age-adjusted annual rate of decline in bone mineral density than White patients,” the researchers write, so they thought perhaps they could trick the model by cropping out parts of medical images that showed such characteristic bone density information. Even still, the model was able to identify the patient’s race from the images. “This finding is striking as this task is generally not understood to be possible for human experts,” the authors write.

How they explain it: “The results from our study emphasize that the ability of AI deep learning models to predict self-reported race is itself not the issue of importance. However, our finding that AI can accurately predict self-reported race, even from corrupted, cropped, and noised medical images, often when clinical experts cannot, creates an enormous risk for all model deployments in medical imaging,” the researchers write. “The regulatory environment in particular, while evolving, has not yet produced strong processes to guard against unexpected racial recognition by AI models; either to identify these capabilities in models or to mitigate the harms that might be caused.”

An Algorithmic Approach to Reducing Unexplained Pain Disparities in Underserved Populations

Emma Pierson; et al. Nature Medicine, January 2021.

What the researchers focused on: Previous research has shown Black patients are more likely than white patients to have their pain dismissed and untreated. One example is knee pain due to osteoarthritis. Researchers wanted to find out if an AI could undo biases in how knee pain is diagnosed and treated.

What they found: The researchers used a deep learning model trained on X-rays of osteoarthritis in the knee of 2,877 patients —18% of whom were Black, 38% were low-income, and 39% were non-college graduates  —  to predict the level of pain a patient would be expected to have based on the progression of their osteoarthritis. The model was better at assigning pain levels to underserved patients than human radiologists. The researchers conclude that the model was able to predict pain even when the imaging did not necessarily show the expected level of disease severity. That’s because patients of color are more likely than white patients to have “factors external to the knee” that influence their level of pain, such as work conditions and higher stress, the researchers write. In other words, the same level of osteoarthritis severity can result in very different levels of pain depending on the patient population, and evaluating a patient without that context can lead to underdiagnosis for underserved patients. In this case, an AI could solve an issue that persists because of human racial bias.

How they explain it: “In addition to raising important questions regarding how we understand potential sources of pain, our results have implications for the determination of who receives arthroplasty for knee pain … Consequently, we hypothesize that underserved patients with disabling pain but without severe radiographic disease could be less likely to receive surgical treatments and more likely to be offered non-specific therapies for pain. This approach could lead to overuse of pharmacological remedies, including opioids, for underserved patients and contribute to the well-documented disparities in access to knee arthroplasty.”

Other academic studies, reports and commentaries to consider:

The Algorithm Bias Playbook

Ziad Obermeyer, Rebecca Nissan, Michael Stern, Stephanie Eaneff, Emily Joy Bembeneck, and Sendhil Mullainathan. Center for Applied Artificial Intelligence, The University of Chicago Booth School of Business. June 2021. Jonathan Huang, Galal Galal, Mozziyar Etemadi and Mahesh Vaidyanathan. JMIR Medical Informatics, May 2022.

Evaluation and Mitigation of Racial Bias in Clinical Machine Learning Models: Scoping Review

Jonathan Huang, Galal Galal, Mozziyar Etemadi and Mahesh Vaidyanathan. JMIR Medical Informatics, May 2022.

Systemic Kidney Transplant Inequities for Black Individuals: Examining the Contribution of Racialized Kidney Function Estimating Equations

L. Ebony Boulware, Tanjala S. Purnell and Dinushika Mohottige. JAMA Network Open, January 2021

Hidden in Plain Sight – Reconsidering the Use of Race Correction in Clinical Algorithms

Darshali A. Vyas, Leo G. Eisenstein and David S. Jones. New England Journal of Medicine. August 2020

Challenging the Use of Race in the Vaginal Birth after Cesarian Section Calculator

Darshali A. Vyas, David S. Jones, Audra R. Meadows, Khady Diouf, Nawal M. Nour and Julianna Schantz-Dunn. Women’s Health Issues, April 2019.

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The role of artificial intelligence is growing in health care, yet many patients have no idea their information is coming into contact with algorithms as they move through doctor appointments and medical procedures. While AI brings advancements and benefits to medicine, it can also play a role in perpetuating racial bias, sometimes unbeknownst to the practitioners who depend on it. 

It’s important for journalists to take a nuanced approach to reporting about AI in order to unearth inequity, highlight positive contributions and tell patients’ individual stories in the context of the broader research.

For insight on how to cover the topic with nuance, The Journalist’s Resource spoke with Hilke Schellmann, an independent reporter who covers how AI influences our lives and a journalism professor at New York University, and Mona Sloane, a sociologist who studies AI ethics at New York University’s Center for Responsible AI. Schellmann and Sloane have worked together on crossover projects at NYU, although we spoke to them separately. This tip sheet is a companion piece to the research roundup “Artificial intelligence can fuel racial bias in health care, but can mitigate it, too.”

1. Explain jargon, and wade into complexity.

For beat journalists who regularly cover artificial intelligence, it can feel as though readers should understand the basics. But it’s better to assume audiences aren’t coming into every story with years of prior knowledge. Pausing in the middle of a feature or breaking news to briefly define terms is crucial to carrying readers through the narrative. Doing this is especially important for terms such as “artificial intelligence” that don’t have fixed definitions.

As noted in our research roundup on racial bias in health care algorithms, the term “artificial intelligence” refers to a constellation of computational tools that can comb through vast troves of data at rates far surpassing human ability, in a way that can streamline providers’ jobs. Some types of AI commonly found in health care already are:

• Machine learning AI, where a computer trains on datasets and “learns” to, for example, identify patients who would do well with a certain treatment
• Natural language processing AI, which can identify the human voice, and might transcribe a doctor’s clinical notes
• Rules-based AI, where computers train to act in a specific way if a particular data point shows up–these kinds of AI are commonly used in electronic medical records to perhaps flag a patient who has missed their last two appointments.

Sloane advises journalists to ask themselves the following questions as they report, and to include the answers in their final piece of journalism: Is [the AI you’re describing] a learning- or a rule-based system? Is it computer vision technology? Is it natural language processing? What are the intentions of the system and what social assumptions is it based on?

Another term journalists need to clarify in their work is ‘bias,’ according to Sloane. Statistical bias, for example, refers to a way of selectively analyzing data that may skew the story it tells, whereas social bias might refer to the ways in which perceptions or stereotypes can inform how we see other people. Bias is also not always the same as outright acts of discrimination, although it can very often to lead to them. Sloane says it’s important to be as specific as possible about all of this in your journalism. As journalists work to make these complex concepts accessible, it’s important not to water them down.

The public “and policymakers are dependent on learning about the complex intersection of AI and society by way of journalism and public scholarship, in order to meaningfully and democratically participate in the AI discourse,” says Sloane. “They need to understand complexity, not be distracted from it.”

2. Keep your reporting socially and historically contextualized.

Artificial intelligence may be an emerging field, but it intertwines with a world of deep-seated inequality. In the healthcare setting in particular, racism abounds. For instance, studies have shown health care professionals routinely downplay and under-treat the physical pain of Black patients. There’s also a lack of research on people of color, in various fields such as dermatology.

Journalists covering artificial intelligence should explain such tools within “the long and painful arc of racial discrimination in society and in healthcare specifically,” says Sloane. “This is particularly important to avoid complicity with a narrative that sees discrimination and oppression as purely a technical problem that can easily be ‘fixed.’”

3. Collaborate with researchers.

It’s crucial that journalists and academic researchers bring their relative strengths together to shed light on how algorithms can work to both identify racial bias in healthcare and also to perpetuate it. Schellmann sees these two groups of people as bringing unique strengths to the table that make for “a really mutually interesting collaboration.”

Researchers tend to do their work on much longer deadlines than journalists, and within academic institutions researchers often have access to larger amounts of data than many journalists. But academic work can remain siloed from public view due to esoteric language or paywalls. Journalists excel at making these ideas accessible, including human stories in the narrative, and bringing together lines of inquiry across different research institutions.

But Sloane  does caution that in these partnerships, it is important for journalists to give credit: While some investigative findings can indeed come from a journalist’s own discovery—for example, self-testing an algorithm or examining a company’s data—if an investigation really stands on the shoulders of years of someone else’s research, make sure that’s clear in the narrative. 

“Respectfully cultivate relationships with researchers and academics, rather than extract knowledge,” says Sloane. 

For more on that, see “9 Tips for Effective Collaborations Between Journalists and Academic Researchers.”

4. Place patient narratives at the center of journalistic storytelling.

In addition to using peer-reviewed research on racial bias in healthcare AI, or a journalist’s own original investigation into a company’s tool, it’s also important journalists include patient anecdotes.

“Journalists need to talk to people who are affected by AI systems, who get enrolled into them without necessarily consenting,” says Schellmann.

But getting the balance right between real stories and skewed outliers is important. “Journalism is about human stories, and these AI tools are used upon humans, so I think it’s really important to find people who have been affected by this,” says Schellmann. “What might be problematic [is] if we use one person’s data to understand that the AI tool works or not.”

Many patients are not aware that healthcare facilities or physicians have used algorithms on them in the first place, though, so it may be difficult to find such sources. But  their stories can help raise awareness for future patients about the types of AI that may be used on them, how to protect their data and what to look for in terms of racially biased outcomes.

Including patient perspectives may also be a way to push beyond the recurring framing that it’s simply biased data causing biased AI.

“There is much more to it,” says Sloane. “Intentions, optimization, various design decisions, assumptions, application, etc. Journalists need to put in more work to unpack how that happens in any given context, and they need to add human perspectives to their stories and talk to those affected.”

When you find a patient to speak with, make sure they fully consent to sharing their sensitive medical information and stories with you.

5. Stay skeptical.

When private companies debut new healthcare AI tools, their marketing tends to rely on validation studies that test the reliability of their data against an industry gold standard. Such studies can seem compelling on the surface, but Schellmann says it’s important for journalists to remain skeptical of them. Look at a tool’s accuracy, she advises. It should be 90% to100%. These numbers come from an internal dataset that a company tests a tool on, so “if the accuracy is very, very low on the dataset that a company built the algorithm on, that’s a huge red flag,” she says.

But even if the accuracy is high, that’s not a green flag, per se. Schellmann thinks it’s important for journalists to remember that these numbers still don’t reflect how healthcare algorithms will behave “in the wild.”

A shrewd journalist should also be grilling companies about the demographics represented in their training dataset. For example, is there one Black woman in a dataset that otherwise comprises white men?

“I think what’s important for journalists to also question is the idea of race that is used in healthcare in general,” adds Schellmann. Race is often used as a proxy for something else. The example she gives is using a hypothetical AI to predict patients best suited for vaginal births after cesarean sections (also known as VBACs). If the AI is trained on data that show women of color having higher maternal mortality rates, it may incorrectly categorize such a patient as a bad candidate for a VBAC, when in fact this specific   patient is a healthy candidate. Maternal mortality outcomes are the product of a complex web of social determinants of health—where someone lives, what they do for work, what their income bracket is, their level of community or family support, and many other factors—in which race can play a role; but race alone does not shoehorn a person into such outcomes.

The post 5 tips for covering racial bias in health care AI appeared first on The Journalist's Resource.

If you work for a boss who has all the emotional intelligence of a computer, consider that someday your boss might actually be a computer. That’s not a dystopic fantasy – for some workers, it’s reality.

Take ridesharing. Millions of cab rides in the U.S. have started with someone pulling out their smartphone, plopping a pin on a map and waiting for a driver to show up and take them on their way. Uber started the mobile ride-hailing revolution a decade ago, and some transportation network company users have probably never called a cab the old fashioned way.

Those who have called for a cab will remember talking to a dispatcher who coordinated their pickup. The dispatcher performed a middle manager function, communicating between drivers and passengers, allocating labor resources according to the company’s goals. Then Uber and Lyft came along and automated the dispatcher’s job. The automation of leadership isn’t likely to stop with the personal transportation revolution, according to a new paper in Computers in Human Behavior, which outlines a framework for further explorations into the changing nature of traditional human-to-human workplace hierarchies.

The paper proposes a conceptual framework of computers-as-leaders that can inform research, and a Leadership-TAM, or technology acceptance model. Researchers have used such models to predict “individual adoption and use of technology” and they have been “successfully applied to a broad range of different technologies,” the authors write.

Academic theories around automated leadership can help policymakers and the public understand the real consequences of computer bosses, but those theories are lagging behind real-world implementation, according to the paper. The authors assume workers need to perceive their automated leaders as being useful and workers need to be able to interact with automated leaders in an effortless way.

“Technology philosophers say algorithms cannot be leaders because they are not legal persons and they cannot be sanctioned and they don’t have inherent moral sentiments and feelings and this is, like, full stop,” says social scientist Jenny Wesche, visiting professor at Humboldt University of Berlin and one of the authors. “Although I agree a computer is not a legal person, it’s important to be open to this paradigm in order to see the people who are working under the leadership of algorithms. If you say machines cannot be leaders you ignore the people who are already working in such situations.”

Computers: from paperweight to the corner office

Researchers typically frame this discussion around what they call “human-computer interaction.” When computers first entered workplaces they were viewed as tools. They were much more sophisticated than, say, a hammer. But, like a hammer, an early computer was a mere paperweight if it didn’t have a human telling it what to do. “The computer is a moron,” Peter Drucker wrote in a classic McKinsey Quarterly essay in 1967.

Around the turn of the 21^st century, researchers started to explore computers as team players, with computers and humans acting in cooperation, even as peers. Almost twenty years on, with exponentially greater processing capabilities and artificial learning, the algorithm-as-boss is here for many workers, particularly those in food service and the gig economy.

“Computers are becoming intelligent entities and are already making decisions that seriously influence human work and life,” write Wesche and her co-author, University of Fribourg psychologist Andredas Sonderegger.

Major chains in the service sector often use automated scheduling, and so do some hospital systems. A computer, not a shift leader, might tell your favorite barista when to show up in the morning. A few years ago, reporting revealed that the scheduling algorithm Starbucks used was creating havoc for some workers, who were sent scrambling for child care in order to make shifts at odd hours.

“Even if these algorithms maybe are not fully autonomous they nevertheless have a big impact on workers’ lives,” Wesche says. “It is more drastic in the gig economy because, from my personal view, the workers there are quite exchangeable.”

For example, Wesche says that a transportation network company may not be particularly invested in career advancement for drivers who use their smartphone application, “and this is different from most traditional companies, especially in higher-skill jobs.”

What’s a leader?

Organizational psychologists have examined from many angles how human leaders and subordinates interact. But when technology is added to the mix, the scholarship tends to focus on how computers can help human leaders and teams – less so the idea of computers as, “active agents in leadership and team processes,” the authors write.

Some researchers put personal management styles – charismatic or inspirational, for example – at the core of being a leader. Leadership is, “a shared human process,” workplace researchers Wilfred Drath and Charles Palus wrote in the mid-1990s. Other definitions of leadership, like that put forward by University of Albany psychologist Gary Yukl, are more functional and have to do with one individual guiding another in structuring activities and relationships. Workers with bosses who primarily do things that can be quantified – scheduling, establishing goals and priorities, monitoring job performance – are those seemingly likely to encounter automated leaders.

“I think we will also in traditional companies see increasingly that functions will be automated, because it is much more efficient,” says Wesche. “But the question is, how do we design it?”

That design may need to account for some lost human element. Social exchange theory observes that workplace relationships can become something more than a financial transactional. Professional relationships can develop beyond an employee being productive for an organization in exchange for monetary or other compensation. Mutual trust between people builds over time, and this can play out in positive social ways. If a barista’s grandmother dies their longtime boss might empathize, approve time off without a fuss, and trust the employee will be back to work when they’re ready – and, perhaps, be open to taking an early shift in a pinch.

“It’s important to do research on the way that humans, with their need for social contact, can interact with a computer leader so that they can in fact flourish at work, they can perform well and at the same time develop personally and experience well-being,” Wesche says.

Hey Alexa, how’s my TPS report?

Humans are arguably already quite comfortable interacting, at least on a basic level, with non-anthropomorphic algorithms. A growing body of research, for example, is exploring how adults and children interact with smart speakers. Alexa, the voice of the Amazon Echo, is backed by sophisticated algorithms and it reverberates from a body that has zero resemblance to the human form – but people can still form real connections with it.

“So my daughter thinks she knows Alexa’s habits and she can understand Alexa even if I can’t,” one parent recounted in 2017 conference paper that analyzed 278,000 Alexa commands. “It’s kind of creepy. As I say it out loud it’s totally weird that my daughter is friends with a tower that sits on my counter.”

Advanced economies are complex and some workers may never interact, at least directly, with an automated leader. For them, the concept of computers-as-bosses may be moot. But for others, like gig workers and food service staff, the future is now — and there are still a lot of unknowns as to how automated leaders might change the lives of their human charges.

“It’s not the question of whether this future will come, or whether functions can be automated, or whether artificial intelligence is taking over more and more functions at work — it’s a question of how it’s going to happen,” Wesche says. “We should be aware of that. I think it is the responsibility of scientists and journalists and politicians to guide this development and set boundaries and discuss standards and discuss development — and not so much discuss whether it is going to come or not.”

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The 17th-century philosopher Rene Descartes once imagined a demon that could make people experience a reality that wasn’t real. Nearly 400 years later, a user on social news platform Reddit conjured realistic pornographic videos featuring Hollywood actresses who weren’t really there.

The user’s handle: deepfake.

Since Vice first profiled that Redditor in late 2017, “deepfake” has come to mean a video that has been digitally manipulated so well that it may be difficult for the average viewer to tell it is fake.

Many deepfakes put someone in a situation that never happened, or show them saying something they never said. Here’s a video that appears to show late-night television host Matt Damon telling viewers he ran out of time for his last guest, Matt Damon:

In the example above, a deepfaker put Damon’s face onto late-night TV host Jimmy Kimmel’s body in an ironic spin on a gag where Kimmel always ran out of time for Damon. Last year, BuzzFeed ran a deepfake app for 12 hours to make a video of former President Barack Obama saying some out-of-character things.

Today, deepfakes are even easier to produce. A deepfake can be made with just a few photographs. Tools and methods for creating realistic but fake audio have also cropped up.

The technology behind deepfakes (which we explore more below) can be used for good. People with disabilities that make it difficult to speak may find new and improved voices. But for now, the potential harm seems likely to outweigh any potential good. Anti-domestic violence advocates warn that deepfakes could be used for intimate partner abuse — by putting an ex-partner in a pornographic video and distributing it, for example.

Top artificial intelligence researchers — never mind journalists — are having a hard time keeping up with deepfake technology, according to a report from early June in the Washington Post. Recent news coverage has centered on how deepfakes might shake up the 2020 presidential election. Reuters is training its journalists to recognize deepfakes. And 77% of Americans want restrictions on publishing and accessing deepfakes, according to the Pew Research Center.

Computer technology often moves much faster than peer-reviewed journals, which can take months or years to publish academic articles. Arxiv, an open-access research repository out of Cornell University, is one place to find current deepfake research. Papers posted there aren’t peer reviewed, but they are vetted by university researchers before they’re published.

Before we get too deep into recent deepfake research, here are five key resources to know about:

• Arxiv-sanity is a search tool good for sifting through Arxiv papers by topic, popularity and publish date.
• The AI Village Slack channel is open to the public and often includes discussions on recent deepfake advances. AI Village is “a community of hackers and data scientists working to educate the world on the use and abuse of artificial intelligence in security and privacy.”
• The Tracer weekly newsletter tracks trends in synthetic media like deepfakes.
• The r/SFWdeepfakes subreddit has examples of deepfakes that do not contain potentially offensive content. Subreddits are pages within Reddit devoted to specific topics.
• Since last year the WITNESS Media Lab — a collaboration with the Google News Initiative — and George Washington University have convened media forensics experts to explore deepfakes, mostly how to detect them. Their research is another valuable starting point.

One last bit of irony: despite the growing body of research on how to identify and combat malignant deepfakes, manipulated videos may not even need to be that good to spread misinformation. So-called cheapfakes — like the doctored video that popped up on social media in May purporting to show a drunk U.S. House Majority Leader Nancy Pelosi — don’t need to be sophisticated to be believed.

“Sometimes we set the bar too high on the effectiveness of media manipulation techniques, expecting that high fidelity equates to impact,” says Joan Donovan, director of the Technology and Social Change Research Project at the Harvard Kennedy School’s Shorenstein Center, where Journalist’s Resource is also housed. “However, small changes to pre-existing video, audio and images present the same challenges for audiences.”

“It’s not that we are fooled, but that we want to believe in the integrity of evidence that images and audio provide as documentation of an event. As they say in Internetland, ‘Pics or it didn’t happen.’”

With that phrase — “integrity of evidence” — in mind, here’s some of the recent research on deepfakes.

Advances in deepfake technology

Generative Adversarial Nets

Ian J. Goodfellow; et. al. Neural Information Processing Systems Conference, 2014.

In this paper, presented at the 2014 NeurIPS Conference, the authors describe generative adversarial networks, or GANs, the technology that makes deepfakes so realistic. This is the paper that started it all.

GANs work by pitting two artificial intelligence computer models against each other. It’s sort of like counterfeiting currency, the authors explain. Imagine a counterfeiter trying to create fake currency that looks real. There are also police, who try to detect the fake currency. The goal is to trick the police.

To produce deepfakes, one computer model acts like the counterfeiter and tries to create an artificial face based on example images. The other model acts like the police and compares the artificial productions to the real images and identifies places where they diverge. The models go back and forth many times until the artificial image is practically identical to the original.

The big breakthrough with GANs is that they allow computers to create. Before GANs, artificial intelligence algorithms could classify images, but had a harder time creating them.

Everybody Dance Now

Chan, Caroline; Ginosar, Shiry; Zhou Tinghui; Efros, Alexei A. Arxiv, August 2018.

In this paper, researchers from the University of California, Berkeley show how motions in a source video can be transferred to target people in another video. The method creates stick figures of the source dancer and then builds the target onto the stick figure. The target appears to perform the moves originally performed in the source video.

The results are imperfect. Target subjects appear jittery and faces are sometimes blurred. Still, this research indicates where deepfake technology is headed. Spoiler alert: it’s not just face-swapping. Realistic motion manipulation is also on the horizon.

A Style-Based Generator Architecture for Generative Adversarial Networks

Karras, Tero; Laine, Samuli; Aila, Timo. Arxiv, March 2019.

What if the person in that picture wasn’t really a person at all? In this paper, researchers from video game company Nvidia improve on techniques that produce convincing images of non-existent people.

Real images of fake people have been around for a few years, but the breakthrough in this paper is that human users controlling the image generator can edit aspects of fake images, like skin tone, hair color and background content.

The authors call their approach “style-based generation.” Using source images, their generator identifies styles such as pose and facial features to produce an image of a fake person. Real people controlling the generator can then change the styles to adjust how the fake person looks. The authors also apply the technique to images of cars and hotel rooms.

Few-Shot Adversarial Learning of Realistic Neural Talking Head Models

Zakharov, Egor; et al. Arxiv, May 2019.

Talking heads used to be tough to fake. Individual faces are complex, and the shape and contour of faces differ widely across people. Just a few years ago, an algorithm might need hundreds or thousands of source images to create a somewhat realistic deepfake.

In this paper, researchers from the Samsung AI [Artificial Intelligence] Center and Skolkovo Institute of Science and Technology, both in Moscow, created talking head videos using an algorithm that learns from just eight original images. The quality of the deepfake improves with more original images, but the authors show that far fewer source images and far less computing power is now needed to produce fake talking head videos.

The authors also show it is possible to animate still images. They bring to life famous headshots of Marilyn Monroe, Salvador Dali, Albert Einstein and others.

Legal status and judicial challenges

Though deepfake technology is advancing quickly, it’s not yet at a point where fake and real videos are totally indistinguishable. Still, it’s not difficult to imagine that the technology may soon speed past the point of no return into a videographic future where fiction and reality converge.

U.S. Rep. Yvette Clarke (D-N.Y.) introduced legislation in June 2019 that would require deepfake producers to include a digital watermark indicating that the video contains “altered audio or visual elements.” Other federal efforts to address deepfakes haven’t gained much traction. U.S. Sen. Ben Sasse (R-Neb.) introduced the Malicious Deep Fake Prohibition Act in December 2018 but it expired without any co-sponsors.

Pornographic Deepfakes — Revenge Porn’s Next Tragic Act

Delfino, Rebecca. Fordham Law Review, forthcoming.

Rebecca Delfino, clinical professor of law at Loyola Law School, Los Angeles, provides a comprehensive overview of federal and state legislation that could be used to combat deepfakes.

“Daily we are inundated in every space both real and cyber with a barrage of truthful and fake information, news, images, and videos, and the law has not kept pace with the problems that result when we cannot discern fact from fiction,” Delfino writes.

While legislation has been introduced, there is no federal law governing creation or distribution of deepfakes. Federal prosecutors may be able to use legislation related to cybercrime, such as the federal cyberstalking statute. There are no state laws either that specifically deal with deepfakes, Delfino finds. At the state level, like on the federal level, laws related to cyberstalking and revenge porn may be used to prosecute people who produce deepfake pornographic videos.

“A federal law criminalizing pornographic deepfakes would provide a strong and effective incentive against their creation and distribution,” Delfino writes. “The slow, uneven efforts to criminalize revenge porn at the state level over the last decade demonstrates that waiting for the states to outlaw deepfakes will be too long of a wait as the technology becomes more sophisticated and more accessible.”

10 Things Judges Should Know about AI

Ward, Jeff. Bolch Judicial Institute at Duke Law, Spring 2019.

Deepfakes may pose new challenges across the American judiciary. What will happen to an institution that relies on factual records, like video evidence, when those records can be easily faked? Imagine a manipulated video showing property damage before an incident allegedly happened, or deepfake audio of a conspiring CEO, writes Jeff Ward, an associate clinical professor of law at Duke University.

“As damaging as any isolated use of such technology may be, the ubiquitous use of hyper-realistic fakes could also threaten something even more fundamental — our ability to trust public discourse and democratic institutions,” Ward concludes.

Combating malignant deepfakes

Exposing DeepFake Videos By Detecting Face Warping Artifacts

Li, Yuezun; Lyu, Siwei. Arxiv, May 2019.

Today’s deepfakes aren’t perfect. With face-swapping — one face placed on top of another — there is some digital transformation and warping that happens, according to the authors, who are researchers from the University at Albany–State University of New York. In that way, deepfakes already leave a kind of watermark that exposes them as not genuine.

The authors describe a method to identify deepfakes that doesn’t require a large number of real and fake images to teach an algorithm subtle differences between the two. Instead, the authors’ method identifies the telltale warping that, for now, is a dead deepfake giveaway.

Protecting World Leaders Against Deep Fakes

Agarwal Shruti; Farid, Hany; Gu, Yuming; He, Mingming; Nagano, Koki; Li, Hao. Workshop on Media Forensics at the Conference on Computer Vision and Pattern Recognition, June 2019.

Remember that deepfake BuzzFeed made of Obama saying out-of-character things? Well, what if instead of BuzzFeed explaining that it was a deepfake, and instead of the fake Obama making tongue-in-cheek remarks, that deepfake had been created by an anonymous individual who instead had put dangerous words in Obama’s mouth?

“With relatively modest amounts of data and computing power, the average person can create a video of a world leader confessing to illegal activity leading to a constitutional crisis, a military leader saying something racially insensitive leading to civil unrest in an area of military activity, or a corporate titan claiming that their profits are weak leading to global stock manipulation,” the authors write.

The authors focus on deepfakes circulating on the web that use likenesses of U.S. politicians such as Obama, President Donald Trump and former U.S. Secretary of State Hillary Clinton. They describe a digital forensics technique to pinpoint deepfakes based on subtle facial movements pulled from real videos of those politicians.

Combating Deepfake Videos Using Blockchain and Smart Contracts

Hasan, Haya R.; Salah, Khaled. IEEE Access, April 2019.

Blockchain may be one way to authenticate videos, according to the authors, researchers from Khalifa University in Abu Dhabi. A blockchain is a digital ledger in which each time something, like a video, is created or altered it is documented in a way that can’t be manipulated. In the framework the authors propose, a video creator can allow others to request to edit, alter or share the video. Any subsequent changes to the video are documented on the blockchain.

“Our solution can help combat deepfake videos and audios by helping users to determine if a video or digital content is traceable to a trusted and reputable source,” the authors conclude. “If a video or digital content is not traceable, then the digital content cannot be trusted.”

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Over the next 15 years, 2 to 3 million Americans who drive for a living – truckers, bus drivers and cabbies – will be replaced by self-driving vehicles, according to a December 2016 White House report on the ascent of artificial intelligence (AI). An estimate by the University of Oxford and Citi, a bank, predicts that 77 percent of Chinese jobs are at risk of automation over roughly the same period.

Millions of people around the world would lose their jobs under these scenarios, potentially sparking mass social unrest and upheaval.

Yet mechanization has always been a feature of modern economies. For example, while American steel output remained roughly even between 1962 and 2005, the industry shed about 75 percent of its workforce, or 400,000 employees, according to a 2015 paper in the American Economic Review. Since 1990, the United States has lost 30 percent (5.5 million) of its manufacturing jobs while manufacturing output has grown 148 percent, according to data from the Federal Reserve Bank of St. Louis (see the chart below).

Machines are besting humans in more and more tasks; thanks to technology, fewer Americans make more stuff in less time. But today economists debate not whether machines are changing the workplace and making us more efficient — they certainly are — but whether the result is a net loss of jobs. The figures above may look dire. But compare the number of manufacturing jobs and total jobs in the chart below. Since 1990, the total non-farm workforce has grown 33 percent, more than accounting for the manufacturing jobs lost.

As we look ahead to a world populated by smart machines that can learn ever more complex tasks, economists agree that retraining people will be required. And — as is the case with global free trade — any big economic shift creates both winners and losers. What they don’t agree on is the degree to which machines will replace people.

Occupations and tasks: Quantifying jobs lost

Robots are easier to manage than people, Hardee’s CEO Andrew Puzder (Donald Trump’s original pick for labor secretary) said in 2016: “They’re always polite, they always upsell, they never take a vacation, they never show up late, there’s never a slip-and-fall, or an age, sex, or race discrimination case.”

According to the 2016 White House report, between 9 and 47 percent of American jobs could be made irrelevant by machines in the next two decades; most of those positions — like jobs at Hardee’s — demand little training.

The 47 percent figure comes from a widely cited 2013 paper by Carl Benedikt Frey and Michael Osborne, both of the University of Oxford. Frey and Osborne ranked 702 jobs based on the “probability of computerization.” Telemarketers, title examiners and hand sewers have a 99 percent chance of being replaced by machines, according to their methodology. Doctors and therapists are the least likely to be supplanted. In the middle, with a 50 percent chance of automatization, are loading machine operators in underground mines, court reporters, and construction workers overseeing installation, maintenance and repair.

In a 2016 paper, the Organization for Economic Cooperation and Development (OECD) — a policy think tank run by 35 rich countries — took a different approach that looks at all the tasks that workers do; taking “account of the heterogeneity of workplace tasks within occupations already strongly reduces the predicted share of jobs that are at a high risk of automation.” The paper found only 9 percent of jobs face high risk of automatization in the U.S. Across all 35 OECD member states, they found a range of 6 to 12 percent facing this high risk of automatization.

Job gains

Are we living in an era so different than past periods of change? Industrialization gutted the skilled artisan class of the 19^th century by automating processes like textile and candle making. The conversion generated so many new jobs that rural people crowded into cities to take factory positions. Over the 20^th century, the ratio of farm jobs fell from 40 percent to 2 percent, yet farm productivity swelled. The technical revolution in the late 20^th century moved workers from factories to new service-industry jobs.

Frey and Osborne argue that this time is different. New advances in artificial intelligence and mobile robotics mean machines are increasingly able to learn and perform non-routine tasks, such as driving a truck. Job losses will outpace the so-called capitalization effect, whereby new technologies that save time actually create jobs and speed up development, they say. Without the capitalization effect, unemployment rates will reach never-before-seen levels. The only jobs that remain will require workers to address challenges that cannot be addressed by algorithms.

Yet many prominent economists argue that this new age will not be so different than previous technological breakthroughs, that the gains will counter the losses.

Take ATMs, for example. Have they killed jobs? No, the number of bank jobs in the U.S. has increased at a healthy clip since ATMs were introduced, Boston University economist James Bessen showed in 2016: “Why didn’t employment fall? Because the ATM allowed banks to operate branch offices at lower cost; this prompted them to open many more branches … offsetting the erstwhile loss in teller jobs.”

In a 2016 working paper for the National Bureau of Economic Research, Daron Acemoglu and Pascual Restrepo — economists at MIT — describe two effects of automation. The technology increases productivity (think about that growth in steel output with fewer workers). This, in turn, creates a greater demand for workers to perform the more complex tasks that computers cannot handle. But that, Acemoglu and Restrepo say, is countered by a displacement effect – the people who are replaced by machines may not have suitable training to take on these more complicated jobs. As the workforce becomes better trained, wages rise. The authors conclude that “inequality increases during transitions, but the self-correcting forces in our model also limit the increase in inequality over the long-run.”

Unlike Frey and Osborne, Acemoglu and Restrepo believe the pace of job creation will keep ahead of the rate of destruction.

At MIT, David Autor agrees. In a 2015 paper for the Journal of Economic Perspectives, Autor argues that “machines both substitute for and complement” workers. Automation “raises output in ways that lead to higher demand” for workers, “raising the value of the tasks that workers uniquely supply.”

Journalists and newsrooms in the U.S. and Europe are the subject of a 2017 case study by Carl-Gustav Linden of the University of Helsinki, in Finland. Though algorithms are able to perform some of the most routine journalistic tasks, such as writing brief statements on earnings reports and weather forecasts, journalists are not disappearing. Rather, Linden finds “resilience in creative and artisanal jobs.”

Education

Stephen Hawking, the eminent Cambridge physicist, warned in a 2016 op-ed for The Guardian that artificial intelligence will leave “only the most caring, creative or supervisory roles remaining” and “accelerate the already widening economic inequality around the world.”

While such dire predictions are common in the mainstream press, economists urge caution.

“Journalists and even expert commentators tend to overstate the extent of machine substitution for human labor and ignore the strong complementarities between automation and labor that increase productivity, raise earnings, and augment demand for labor,” wrote Autor in his 2015 paper.

This must be addressed, the White House stressed in its 2016 review, with investment in education, in retraining for older workers, and by strengthening the unemployment insurance system during periods of change.

Autor is sanguine about the government’s ability to prepare workers for the high-tech jobs of tomorrow. “The ability of the U.S. education and job training system (both public and private) to produce the kinds of workers who will thrive in these middle-skill jobs of the future can be called into question,” he wrote. “In this and other ways, the issue is not that middle-class workers are doomed by automation and technology, but instead that human capital investment must be at the heart of any long-term strategy.”

Economists are basically unanimous: the jobs of the future will require more education and creative skills. (The last time the U.S. faced such a challenge, in the late 19^th century, it invested heavily in high schools for all children.)

Even so, computers appear to be usurping knowledge jobs, too. IBM claims it has designed a computer that is better than a human doctor at diagnosing cancer; in Japan, an insurance company is replacing its underwriters with computers.

Globalization, free trade and robots

Some American politicians often point at free trade deals, specifically with China and Mexico, as job-killing and bad for American workers. But a growing body of research points to machines as the real culprits. For example, a 2015 study published by Ball State University found that between 2000 and 2010, 88 percent of lost manufacturing jobs were taken by robots, while trade was responsible for 13.4 percent of lost jobs.

Machines cost about the same to operate no matter where they are located. If it costs the same to keep a factory in China or Ohio, a firm would probably prefer Ohio. Whether the firm is Chinese or American, in theory there is the rule of law in America to protect its investment. So for journalists, a question is not where these automated workshops of the future will be located. It is where the robots toiling in them will be made.

Citations

• Acemoglu, D.; Resrepo, P. “The Race Between Man and Machine: Implications of Technology for Growth, Factor Shares and Employment.” NBER Working Paper, 2016.
• Arntz, Melanie; Gregory, Terry; Zierahn, Ulrich. “The Risk of Automation for Jobs in OECD Countries: A Comparative Analysis.” Organization for Economic Cooperation and Development, 2016. doi: 10.1787/5jlz9h56dvq7.
• Autor, David H.; Katz, Lawrence F.; Krueger, Alan B. “Computing Inequality: Have Computers Changed the Labor Market?” The Quarterly Journal of Economics, 1998. doi: 10.1162/003355398555874.
• Autor, David H. “Why Are There Still So Many Jobs? The History and Future of Workplace Automation.” Journal of Economic Perspectives, 2015. doi: 10.1257/jep.29.3.3.
• Bessen, James E. “How Computer Automation Affects Occupations: Technology, Jobs, and Skills.” Boston University School of Law, Law and Economics Working Paper, 2016. doi: 10.2139/ssrn.2690435.
• Collard-Wexler, Allan; De Loecker, Jan. “Reallocation and Technology: Evidence from the U.S. Steel Industry.” American Economic Review, 2015. doi: 10.1257/aer.20130090.
• Executive Office of the President. “Artificial Intelligence, Automation, and the Economy.” White House, 2016.
• Frey, Carl Benedikt; Osborne, Michael. “The Future of Employment: How Susceptible Are Jobs to Computerization?” Oxford Martin School working paper, University of Oxford, 2013.
• Linden, Carl-Gustav. “Decades of Automation in the Newsroom: Why Are There Still So Many Jobs in Journalism?” Digital Journalism, 2017. doi: 10.1080/21670811.2016.1160791.

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