1. THERE IS NO SILVER BULLET FOR ADDRESSING HARMS OF AI IN NEWS WHILE STILL ENABLING ITS BENEFITS

Artificial intelligence will impact seemingly every industry, with the news media being no exception. This can be particularly challenging in the news and information space as the use of AI by various actors can sometimes work against informing the public and sometimes work towards it. In either case, new technological developments have added new challenges for separating the real from the fake. And while the media, technology companies and others are making attempts at identifying AI, the group was in agreement that no single solution will be completely effective. In fact, some research finds that current tools, such as labels of AI use, may actually do more harm than good.

So what is being done now to verify or authenticate AI-generated content? To date, the tool that has been furthest developed and has also received the most attention is “watermarking” — a technique where markers get embedded into a piece of content (e.g., image, audio, video, text, etc.) when it is first created. Proponents say watermarks help journalists and the public identify ill-intended changes to content they find online.

Several online platforms have begun implementing their own software and/or rules for asserting the authenticity of content, many of which comply with the Coalition for Content Provenance and Authenticity (C2PA) standard. OpenAI released an update that adds watermarks to images created with its DALL-E 3 and ChatGPT software. Google’s SynthID allows users to embed digital watermarks in content that are then detectable by the software. Other companies like Meta have focused on provenance, with policies that require disclosing content as being generated with AI. Across the technology industry, companies are taking note of provenance and implementing tools to assist users.

While developing these kinds of tools clearly has benefits, the group identified several important considerations and reasons the tools alone should not be seen as a solution in and of themselves:

• Preliminary research finds that labels related to AI in journalism can have an adverse effect on the public. Research examining the impact of content labeling raises several caution flags. First, overly broad labels about false and manipulated information can lead users to discount accurate information, suggesting labels need to be comprehensive and explicit about which content is false. Similarly, additional research on tagging content as AI-generated or enhanced finds, “… on average that audiences perceive news labeled as AI-generated as less trustworthy, not more, even when articles themselves are not evaluated as any less accurate or unfair” and that the effects are more common among people with higher pre-existing levels of trust.
• These types of content labels can also lead to an “implied truth effect” in which false information that is not tagged as such may be interpreted as authentic. Similar findings exist when studying provenance. On the more hopeful side, some findings suggest that the public can appreciate provenance details as long as they are comprehensive — poorly-detailed provenance can lead to users discounting authentic information. To that end, researchers are exploring what specific labels to use in a given context (e.g., AI generated, manipulated, digitally altered, computer generated, etc.) and how users interpret these terms.
• Current labeling techniques don’t differentiate between uses of AI that help inform rather than disinform. Indeed, some content alterations are done to help inform, which current methods of labeling don’t address. One participant shared an  example from Zimbabwe where a newsroom used chatbots to offer information in many more local dialects than Western-trained models could provide. There are also several non-publicly facing innovative AI uses such as fact-checking radio broadcasts or combing through and synthesizing news archives (which offer particular value for local news organizations). The current research suggests that a simple label acknowledging the use of AI risks automatic public rejection of what would otherwise add value to the news product. 
• We need to clarify the intended audience for each label. There is a lot of conversation about the benefit of provenance for the public but, as the group discussed, it may be more crucial for journalists and content creators. Marc Lavallee noted, there is “limited value in any kind of direct consumer-facing watermark or signal” and Richard Gingras said, “the value of provenance is probably more for the world of journalism than it is for the world of consumers.” Consider the example of information cataloged for original artwork. Celeste LeCompte noted, “most of that information is not generally revealed to the public but is rather something that is part of an institutional framework.”
• We need to better understand and articulate the various elements of identification: provenance, watermarking, fingerprinting and detection. In this convening, four similar but distinct forms were discussed. First is provenance, which refers to a “manifest or audit trail of information” to ensure content is “always attributable.” Provenance information is imperfect, however, because it can be input incorrectly or changed by malicious actors. Second is watermarks, a class of identifiers embedded directly into content which is more difficult to remove and considered to be more robust than provenance. A third technique called fingerprinting can serve as a lookup tool, like reverse image searches. Finally, there are what are termed detection methods. These use AI models to detect other AI models, though the development process remains challenging. They are the least robust and, as one participant asserted, more research needs to be done in this area. 

Again, the conclusion is not that watermarks are bad or of no use, but that they need to be fully thought through within the vast array of AI uses and considered as just one tool among many that are needed — which leads to the next takeaway.

2. WE NEED TO EXPERIMENT WITH A NUMBER OF POSSIBLE TOOLS

As we further develop the efficacy of tools like watermarking, participants encouraged further experimentation with additional ways to help identify and explain various uses of AI content. As one participant remarked, “Doing something is better than doing nothing while waiting for the perfect solution.”

A few ideas shared by participants:

• SSL for AI: SSL, or Secure Sockets Layer, was designed to be an encryption tool for e-commerce, but is now used by virtually every website as a privacy and authentication tool. As one participant stated, there’s no “values determination,” just a determination on the content’s origin. Thus, there are no false positives or false negatives. Could publishers and technologists collaborate on something similar here? 
• Accessible Incentive Structures to Adopt Standards: Another idea was to use incentive structures for journalists and other responsible content creators to adopt certain standards and labeling techniques which could eventually become commonly understood. Search Engine Optimization (SEO), the process by which websites strive to rank higher in Google and other search engines, was offered as an example. While not a perfect corollary (with questions about gaming algorithms and the value of information that is not public facing) SEO, as originated, did offer a strong incentive and was pretty easy to adopt. How might something like that work for identifying AI content? And how could we measure its effectiveness? Getting the incentive structure right so that fact-based content gets promoted while “… inauthentic material or material of unknown provenance is lessened is really the place to focus,” suggested Lavallee. “If we get to a point where basically only bad actors are the ones not willing to use a system like this, I think that’s the threshold that we need to get to in order for it to be effective.”
• Training the public: Continued attention on AI literacy and education is important. As the research (cited above) shows, most of the public seems to distrust any use of AI in news content. A more nuanced understanding is important to allow journalists to use AI in ways that help serve the public. One participant shared information about how media and technology education are included in Finland’s national education plans and how news organizations there have also developed training materials for the general public. Information about how AI is used in news needs to be understandable for a non-technical audience but also allow people who would like further details to have access to that information. As such, per Tove Mylläri, “We believe that educating people to be aware and letting them decide themselves also builds trust.” Understanding what types of training materials are most effective is crucial for increasing knowledge about how AI is used in news. 

One important element for any of these tools, especially (but not limited to) those that are public facing, is communication. The publishers and others who implement these tools need to explain to their audiences and the general public what these tools do. It will take time for them to become recognized, commonly understood and utilized. The “nutrition labels” parallel provides a sense of that timeline. Nutrition labels, noted Anna Bulakh, are now generally understood and serve a valuable purpose, but that took experimentation about what kinds of information the public wanted and it took time for shoppers to become accustomed to them. In fact, it remains a work in progress and also carries with it the important consideration of who decides what goes into the label. News consumers are not yet used to “nutrition labels” for content. “Provenance is providing you with a nutritional label of content [so you can] be aware that it is AI manipulated or not AI manipulated,” Bulakh added. Provenance should be understood to mean information about the source of the material (i.e., the publisher, author, and/or editor) as well as what tools or mechanisms might be relevant to assessing the trustworthiness of the content.

3. THE BEST ROLE FOR POLICY: CONSIDER INDUSTRY STANDARDS AS A START, RETHINK REGULATORY STRUCTURES, LEAD BY EXAMPLE

Government policy and regulation can be critical in promoting and safeguarding the public good. They can also have lasting impact and as such must be thoroughly and carefully approached. Any drafted policy should consider its potential impacts both now and in the years to come. To facilitate this, the discussants laid out several insights:

• Established standards can help build effective policy. One part of the conversation focused on creating industry standards that, at least in some areas, can be used to inform effective legislation and regulation (recognizing these two forms of policy have different definitions which can also vary by country). Standards allow for experimentation and adjustment over time, could be developed for different parts of the system and, as past examples have shown, can then develop into effective policy. Consider, for example, the U.S. Department of Agriculture (USDA)’s development of organic food standards in the 1990s. The 1990 Organic Foods Production Act createdthe National Organic Program which was tasked with developing standards for organic food production and handling regulations. The USDA defines organic as a “labeling term that indicates that the food or other agricultural product has been produced through approved methods. The organic standards describe the specific requirements that must be verified by a USDA-accredited certifying agent before products can be labeled USDA organic.” The creation of this standard, remarked one participant, took time to develop but eventually led some consumers to seek out this type of product especially because they believe it follows specific criteria. Media content could follow a similar process. Once trusted standards are implemented, the public may seek content that conforms to those protocols, which would likely incentivize many industry actors to also use the standards. 
• In supporting this approach, former U.S. Representative & Chair of the Congressional Artificial Intelligence Caucus, Jerry McNerney, suggested that we fund AI standards agencies and, once created, enforce those standards through law, adding that it is important to have “the involvement of a wide spectrum of stakeholders,” particularly given the unique ways journalists are incorporating AI into their work.
• It’s time to rethink what structures of “regulation” should look like today.Several participants agreed we need to rethink what the structure of regulation looks like in an AI-incorporated environment in which developments occur rapidly with technology that is complex. One participant rhetorically asked, “do you really understand what you’re trying to accomplish?” It doesn’t mean we should walk away from complicated issues but we should fully think about the most effective approaches.
• Ethan Chumley offered that there “are close parallels and analogies” to cybersecurity perspectives “if we start to view the challenges of media authenticity and trust in images as a security problem.” Regulation will likely require more updates (1) as technologies evolve and (2) as standards are revised.
• This all takes time! Developing broadly adopted standards that can then develop into policy is time consuming. Bulakh suggests that “every new standard would take around 10 years to be accessible” to tool creators, distribution platforms, content creators and consumers. She points to the Coalition for Content Provenance and Authenticity (C2PA), one of the leading global standards, entering year 5, even though it has not been fully adopted. Another example is HTML standards for websites which, similarly, took many years to develop and are still evolving. We have past examples to use as models for timelines which need to be built in — let’s use them! 
• This is not to say that a standards-first approach is right for all areas. There may well be some aspects of managing use of AI that call for some government oversight more immediately —  though we still need to be sure the regulatory structures are effective and that the policies are developed in a way to serve the public long-term.
• One immediately available step is to lead by example. Many nods of agreement occurred when one participant pointed out that if governments want others to adopt and utilize various standards, rules or policies, they need to do so themselves. Governments can, in the immediate term, “start adopting these standards that are already out there in their own datasets,” said Elik Eizenberg, which would likely create momentum for the private market to follow. One of the most powerful tools governments have, Eizenberg added, is to “lead by example.” In the discussion that followed, another participant added that, conversely, many current policies relating to misinformation have built-in exceptions for politicians. 

4. SUCCESSFUL STANDARDS, USES & GUARDRAILS REQUIRE TECHNOLOGY COMPANIES’ ACTIVE PARTICIPATION

“Journalists [and] media houses cannot cope with these issues alone without technology companies,” remarked one participant to the nodding approval of others. Another person added, “Open standards are developed by technologists. It’s [technologists’] job … to come together to provide access to those tools … and make them more accessible.” Media houses can then communicate and “change consumer behavior.” 

Tanit Koch spoke of the importance of technology companies in helping guard against those with bad intentions: “We need tech companies and not only because of the scale and the speed that disinformation can happen and is happening on their platforms, but simply because they have the money and expertise to match the expertise of the bad actors. We definitely and desperately need more involvement by those who feel a sense of responsibility to create open source tools to help the media industry detect what we cannot detect on our own and all of this full well knowing that the dark side may always try to be ahead of us.”

A separate point was raised about whether there was a role for technology companies, and policies guiding them, in helping users better understand certain risks or benefits associated with the use of certain technologies. “When I take a pill, there’s a warning label,” offered Paul Cheung, but these risks are not as clearly defined in the online space. Thus, consumers “have no information to assess whether this is a risk they’re willing to take.” People may use a certain technology tool or piece of software “because it’s free and fun” without knowing the risk level. And those risk levels likely vary, suggesting a model similar to the U.S. Food and Drug Administration (FDA) or Federal Aviation Administration (FAA) may be a better route than a “one-size-fits-all” approach. 

Understanding these risks relates to the discussion on building trust that occurred during CNTI’s first AI convening. As reported following that gathering:

5. RESEARCH, RESEARCH, RESEARCH

To date, research about labeling AI content (and how users engage with labels) is limited. We need much more data to gain a fuller understanding of the best strategies forward, as well as which strategies are likely to fall short, backfire or possibly work in some areas but not in others. To develop a deeper understanding of what, why and how certain policy and technology approaches work better than others, we need to conduct more studies, replicate findings and build theories. Approaches must also reflect geographic diversity by including researchers representing a range of local contexts and communities and by examining how people in diverse, global contexts are similar and/or unique in their interactions with AI-related news content. For example, U.S./European research that focuses solely on strategies to address internet disinformation would not serve well those places where radio is still the largest news medium. We need to provide the resources and support for this work — starting now.

Several researchers in the room noted that the existing literature does not yet fully grasp how users interact with provenance information or how the tools being developed will influence user behavior. 

Felix Simon shared recent preliminary research he’d conducted in collaboration with Benjamin Toff that featured a striking finding: “For our sample of U.S. users, we found, on average, that audiences perceived news labeled as AI-generated as less trustworthy, not more, even when articles themselves were not evaluated as any less accurate or unfair.” Yet, when the authors provided users with the sources used to generate the article, these negative effects diminished. Further research is warranted to better understand how the public interprets labels on AI-generated articles and how to best present this information.

When it comes to watermarks in particular, “we have some directional indications about efficacy but we do have extremely limited data in this area,” offered Matt Perault. Based on the limited data and research, he presented four key research questions that need to be addressed:

1. Will disclaimers and/or watermarks be implemented correctly?
2. Would users actually observe them?
3. Would disclaimers and/or watermarks have a persuasive effect?
4. What effects would watermark and/or disclosure requirements have on competitiveness and innovation?

Answers to these questions will assist in the development of evidence-based policy.

6. BEST STEPS FOR NEWSROOMS: INNOVATE WITH A DEGREE OF CAUTION

Technology has revolutionized the media many times over (e.g., the printing press, radio, television, the internet, social media, streaming, etc.) with AI being the latest example of an innovation that will change how reporters gather and share information and how consumers take it in.

Panelists made a number of workplace recommendations that would help journalists incorporate AI into their reporting and build a better relationship with their audiences.

• Newsrooms need to embrace AI technology, but do it cautiously. They must be willing to experiment with new tools to see what works for them and what helps audiences better understand the news. Offered Charlie Beckett, “The crisis in journalism at the moment is about connecting the ton of good quality stuff out there to the right people in the optimal way that doesn’t make them avoid the news.” It was further noted that quality reporting requires diligent research and thorough questions and that a similar approach should be applied to the exploration and adoption of new technology. Participants discussed a number of existing and developing technologies that could spur newsrooms to adopt the use of AI to support their work (see sidebar).  
• Conversely, concerns remain about how AI models use journalists’ works. Courtney Radsch asserted, “As we see more generative AI content online, less human-created information, the value of journalism, I think, goes up. So we should be thinking about a model that can allow us to have some say over the system.” The complex pros and cons of methods such as licensing or copyright on the nature and effectiveness of the knowledge ecosystem, including that they can hinder broad distribution and reward quantity irrespective of quality, were mentioned but were covered more in depth at CNTI’s first AI Convening, as written about in the summary report.
• Journalists need to apply layers of transparency in their work, just as they expect from the people and organizations they cover. Innovation and transparency are critical to serving local communities and fortifying the journalism industry for the digital age. But remember that “transparency has multiple meanings, each of which must be addressed.” First, journalists must offer the same level of transparency that they demand of others. At a non-technical level, that means explaining why a particular story is being told at that time. But it also means clearly explaining how AI is used to report, create and produce content including attributions and points of reference in the output of AI-driven answer engines (e.g. ChatGPT, etc.). Research has shown that some responses by AI-driven answer engines can be biased by the articulation of the source material used to feed them. News consumers would benefit from understanding related references and attributions which can help audiences rebuild trust in the media.
• Recognize that there is more to AI than the content being produced. Jeff Jarvisshared his thoughts on the ABCs of disinformation, which a 2019 report by Camille François of the Berkman Klein Center for Internet & Society at Harvard University defined as manipulative actors, deceptive behavior and harmful content. Jarvis summarized, “We have to shift to the ABC framework — that is to say actors, behavior and content,” rather than a singular focus on content. While journalists may be able to identify those seeking to sew harm through diligent research and reporting, it is difficult to persuade news consumers that such people act with malicious intent. 
• Another concept offered was that of “fama,” a Latin word dating back to Europe before the printing press that brings together rumor and fame to form the concept of believing something to be true because it was said by someone the listener trusts. Its modern equivalence is believing in news — or conspiracy theories — because they are uttered by a trusted voice. As noted by Deb Ensor, “We don’t often think about our audiences in terms of their behaviors and how they share or trust or value or engage with their information suppliers.” Those with ill intentions then appeal to this behavior with deceptive tools, such as bots and troll farms to spread disinformation. With the advent of AI, these actors have even more tools at their disposal and journalists need partners in technology and research to keep up.

NEXT STEPS & ADDITIONAL INFORMATION

This is the second in a series of convenings CNTI will host on enabling the benefits of AI while also guarding against the harms. It is just one of many initiatives within the larger work CNTI, an independent global policy research center, does to encourage independent, sustainable media, maintain an open internet and foster informed public policy conversations. Please visit our website: www.innovating.news to see more of CNTI’s work and sign up to receive updates, and, as always, please contact us with questions and ideas.

Finally, please see the Appendix below for numerous valuable resources shared by participants of this event as well as other acknowledgements.

APPENDIX

About This Convening

CNTI’s cross-industry convenings espouse evidence-based, thoughtful and challenging conversations about the issue at hand, with the goal of building trust and ongoing relationships along with some agreed-upon approaches to policy. To that end, this convening adhered to a slightly amended Chatham House Rule:

1. Individuals are invited as leading thinkers from important parts of our digital news environment and as critical voices to finding feasible solutions. For the purposes of transparency, CNTI publicly lists all attendees and affiliations present. Any reporting on the event, including CNTI’s reports summarizing key takeaways and next steps, can share information (including unattributed quotes) but cannot explicitly or implicitly identify who said what without prior approval from the individual.
2. CNTI does request the use of photo and video at convenings. Videography is intended to help with the summary report. Any public use of video clips with dialogue by CNTI or its co-hosts requires the explicit, advance consent of the subject.
3. To maintain focus on the discussion at hand, we ask that there be no external posting during the event itself.

To prepare, we asked that participants review CNTI’s Issue Primers on AI in Journalism, Algorithmic Transparency and Journalistic Relevance as well as the report from CNTI’s first convening event. 

Resources Shared by Participants

Participants at our convening event shared a number of helpful resources. Many of these resources are aimed at assisting local newsrooms. We present them in alphabetical order by organization/sponsor below. 

Several news organizations were mentioned for their use of AI in content creation. One that received recognition was the Baltimore Times for its efforts to better connect with their audience through the use of AI. 

Participant Aimee Rinehart shared a blueprint for her CUNY AI Innovation project. This project aims to create a journalism-specific LLM AI model for journalists and newsrooms to use.

A novel radio fact-checking algorithm in Africa, Dubawa Audio Platform, was discussed to show how countering mis- and disinformation can be done in non-Internet-based contexts. The platform was initiated by a Friend of CNTI, the Centre for Journalism Innovation and Development (CJID). The Dubawa project received support from a Google News Initiative grant.  

Information was shared about the Finnish government and academic community’s campaign on AI literacy, Elements of AI. This project aims to raise awareness about the opportunities and risks of AI among people who are strangers to computer science, so they can decide for themselves what’s beneficial and where they want their government to invest. Free educational material also exists for children. Curious readers may also learn about related research here.

The topic of transparency was discussed and a 2022 report by Courtney Radsch for the Global Internet Forum to Counter Terrorism (GIFCT) provides an important overview of transparency across various industries.

A number of participants shared information about technological tools:

• Google’s Pinpoint project helps journalists and researchers explore and analyze large collections of documents. Users can search through hundreds of thousands of documents, images, emails, hand-written notes and audio files for specific words or phrases, locations, organizations and/or names.
• Google’s SynthID is a tool to embed digital watermarks in content to assist users with knowing the authenticity and origin of content. 
• Microsoft’s PhotoDNA creates a unique identifier for photographs using its system. This tool is used by organizations around the world — it has also assisted in the detection, disruption and reporting of child exploitation images. 

Information was shared about Schibsted, a Norwegian media and brand network, organizing the development of a Norwegian large language model (LLM). It will serve as a local alternative to other general LLMs. 

The Tow Center for Digital Journalism at Columbia University released a recent report by Felix Simon titled “Artificial Intelligence in the News: How AI Retools, Rationalizes, and Reshapes Journalism and the Public Arena.” 

A participant shared an innovative use of AI in Zimbabwe in which the AI model has been trained using local dialects. The chatbot is more representative of the users in that region when compared to other general AI language models. 

We appreciate all of our participants for sharing these resources with CNTI.

About CNTI

The Center for News, Technology & Innovation (CNTI), an independent global policy research center, seeks to encourage independent, sustainable media, maintain an open internet and foster informed public policy conversations. CNTI’s cross-industry convenings espouse evidence-based, thoughtful but challenging conversations about the issue at hand, with an eye toward feasible steps forward.

The Center for News, Technology & Innovation is a project of the Foundation for Technology, News & Public Affairs.

Acknowledgements

CNTI sincerely thanks the participants of this convening for their time and insights, and we are grateful to the University of Southern California’s Annenberg Capital Campus, the co-sponsor and host of this AI convening. Special thanks to Adam Clayton Powell III and Judy Kang for their support, and to Steve Waldman for moderating such a productive discussion.

CNTI is generously supported by Craig Newmark Philanthropies, John D. and Catherine T. MacArthur Foundation, John S. and James L. Knight Foundation, the Lenfest Institute for Journalism and Google.

1. BETTER ARTICULATION, CATEGORIZATION AND UNDERSTANDING OF AI IS ESSENTIAL FOR PRODUCTIVE DISCUSSIONS.

Cross-industry experts unanimously agreed on the need to work together to better define AI and articulate clear categories of use. This will enable a common understanding and allow for more productive conversations. Right now, that is not happening. As one participant remarked, “We jump into the pool … and we’re not even talking about the same thing.”

An overarching definition of AI: The participants chose not to spend their limited time together writing a precise definition of AI, but they shared definitions they have found to be useful starting points including those from:

• Council of the EU: “systems developed through machine learning approaches and logic- and knowledge-based approaches.”
• AP Stylebook’s AI chapter: separate definitions for “artificial intelligence,” “artificial general intelligence,” “generative AI,” “large language models” and “machine learning.”
• Melanie Mitchell: “Computational simulation of human capabilities in tightly defined areas, most commonly through the application of machine learning approaches, a subset of AI in which machines learn, e.g., from data or their own performance.”

Within these definitions are several categories of use that must also be clearly differentiated and articulated. They include: 

The scope of AI: AI is not new – in general nor in journalism – and represents a much broader set of technologies than simply generative AI (GAI) or large language models (LLMs). Nevertheless, one participant noted research finding many burgeoning AI newsroom policies narrowly define AI as GAI and LLMs, which “speaks to the challenges of grasping this set of technologies.”

The type of AI use: It is important to differentiate among the various types of AI use related to news. There are uses for news coverage and creation and, within that, questions around the degree of human vs. AI involvement in published content. There are uses that help with newsroom efficiency and cost savings, such as transcription, that don’t necessarily result in public-facing AI output. There are uses for news distribution and access, such as translation. At least one participant remarked on the tendency, when thinking about LLMs, to limit the scope of its definition to text-based GAI rather than comprehensively conceptualize GAI to include other formats such as XtownLA, which uses AI models for reporting on public hearings.

Who the user is: It is critical to specify who the user is at each point in the process of AI use so that appropriate responsibility, and perhaps liability, can be attributed. Is it newsrooms? Members of the public? Technology or third-party companies? Governments? This articulation is often missed.

The part of the AI system being accessed: A few participants talked about the importance of understanding and differentiating among the different levels of AI systems. The first level is, for example, the LLM being built. The second level is the API or corpus (e.g., all data being used as inputs) of what is in the model or further steps such as reinforcement learning through human feedback (RLHF). Third is the application use level, such as ChatGPT, in which humans do not actually interact with the model itself. Each of these, as one participant noted, should have different policy considerations.

Another pitfall in current discussions occurs when we don’t take time to articulate what is neither a part of AI nor tied directly to its use. This is particularly important when it comes to guarding against harms. Participants articulated the need to distinguish between which issues are attributed to the internet and social media age in general and which issues are linked specifically to AI technologies.

Better understanding of these definitions and categorizations can also help build trust, which several participants named as critical for positive outcomes from AI use and policy development. One participant asked, “How do we generate trust around something that is complicated, complex, new and continuously changing?” Another added, “Trust is still really important in how we integrate novel technologies and develop them and think two steps ahead.”

For Further Consideration

How do we create better knowledge and understanding? If we want this articulation to lead to better understanding and knowledge, how do we get there? How can we make that knowledge more accessible to journalists, researchers and policymakers? What can technology companies do? How can CNTI help distribute this knowledge?

2. WHETHER A PARTICULAR AI USE IS A BENEFIT OR HARM TO SOCIETY DEPENDS ON ITS CONTEXT AND DEGREE OF USE, MAKING SPECIFICITY VITAL TO EFFECTIVE POLICY.

Whether a particular use of AI is a benefit or a harm to society can depend on several factors, including the degree of use, the strength of protective actions and the subject matter surrounding the use. Effective policy must include specific and context-sensitive considerations. A prime example of this, discussed extensively by the group, is algorithmic transparency. (For more on this topic, see CNTI’s related Issue Primer.)

While there was general agreement on the importance of some level of transparency about how models are built and how newsrooms, journalists or others use AI – and, in most cases, agreement that more transparency is needed than currently exists – participants also discussed instances when transparency might be counterproductive. Is there a point at which the level of transparency carries more risk than value or causes more confusion than clarity – for instance, where “the level of transparency and detail … can actually undermine the security and safety of the models”? Determining where to draw that line is critical but difficult. 

For example, optimal transparency around how AI is used in hiring processes or in making bail recommendations might differ from optimal transparency around the trillions of tokens that go into a LLM. The latter may not be as useful for unrestricted access to the public because of such varying abilities to evaluate this knowledge, and there could be a point at which the benefit of transparency is outweighed by the risk of malign actors abusing these systems. There seems to be clear value, though, in public transparency about whether content was fully AI-generated, was AI-generated with human involvement or came only from a human. 

There were some different opinions about how to – or who should – determine how transparency extends to the broader public. One individual suggested “more transparency is better,” and then journalists can “make choices about how we describe all of this.” Participants agreed that we “have something to grapple with” and must work toward this together. These conversations call for “precision in the way we are discussing and analyzing different use cases so we ensure that policy that is created is specific to a given situation,” the challenge of doing so amid technology’s rapid evolution notwithstanding.

Another discussion surfaced about the benefits of AI that increased through advancements in LLMs, which AI policy may want to protect: transcription and translation. We “almost gloss over some of those [tools like transcription] because they’re such routinized things,” but “it is really the kind of scale of deployment that’s effective and interesting.” Similarly, translation has been used for a long time, but GAI has scaled it to create new opportunities for public use and benefit.

For Further Consideration

As we think about benefits of AI use, what are other AI practices that can be scaled up in ways that bring value to societies more broadly? What methods can be used to help determine how and when a benefit might become a harm, or vice versa?

3. EVEN WHEN POLICY IS GROUNDBREAKING, IT MUST ALSO TAKE INTO ACCOUNT HOW IT RELATES TO AND BUILDS ON PRIOR POLICY.

While it is important to focus on clear, specific definitions when constructing new regulations, the group recognized that policy development is a layered process, and it is necessary to consider it in the context of previously adopted policy. So while developing clear definitions around AI matters, existing policy will inevitably impact new policy. 

Take, for example, anti-discrimination law. In many countries, discrimination is illegal regardless of how it occurs, including via AI. The specific definition of AI doesn’t really impact whether its use violates such laws. In a similar example, European Union and Canadian policies influence how AI gets trained and used in the context of data protection. Even if many of today’s AI systems did not exist when data protection laws were written, they still have an effect when it comes to AI. As one participant put it:

While there was clear agreement that definitions are important, at least one participant also asked: Have we reached a time where being clear about which principles should be protected is as important as the definitions themselves? Those principles can be articulated in ways that are technology-agnostic. Would this kind of approach help us create policy that can endure future technological developments? Or perhaps the answer is a nexus point of definitions and principles. 

A core principle for CNTI, and for this convening, is a free and independent press, which is under intense pressure in many parts of the world. Could we look at the various ways in which uses of AI in journalism impact that principle and approach policy development with that lens? 

Perhaps. But as other participants pointed out, that approach can be problematic if it is not done well, and definitions remain essential considerations. For example, a 2023 bill in Brazil proposed changing its Penal Code to include an article doubling the penalty for using AI to commit online fraud without actually defining AI – thus validating concerns about the impact of unclear definitions in legislative policy.

For Further Consideration

When it comes to AI use in journalism, what is the right balance between broad principles and specific definitions? Are there examples of successful regulatory structures that already exist, such as anti-discrimination policies, that can help us strike that balance? 

4. ONE POLICY GOAL SHOULD BE TO ADDRESS DISPARITIES IN THE USES AND BENEFITS OF AI – AND PARTICULARLY GAI – AS A PUBLIC GOOD.

The importance of greater parity arose many times throughout the day. There are currently numerous global disparities in access to, use of and value of AI models. There was strong consensus that we need to work together on creating equality around AI as a public good, including the use of AI to help close gaps in news access. Inequalities work against the public good, which public policy is intended to protect. Use of and access to AI tools need to be democratized. How can public policy help that? Is this a case where organizational policy is as beneficial as — or perhaps more beneficial than — legislation?

Some specific areas of disparity discussed at the convening include: 

The content in current AI models misses many people and many parts of the world. Certain parts of the world can’t realize the same potential of AI, especially GAI, because a substantial proportion of the world’s languages are not accounted for in LLMs (either because they do not have enough training data to create them or because tools aren’t well-constructed for them). While the practical effect of this disparity is easy to grasp, some less-considered impacts include reduced public trust in AI tools and their outputs. 

GAI models often replicate existing social biases and inequities. Participants questioned the ability of many AI models to “reflect the full breadth of human expression,” noting examples ranging from the generation of photos of engineers as white men to the inclusion of QAnon conspiracy theories in LLM training data. Research supports this skepticism.

AI models, and the companies providing them, are largely exported from only a few countries. In many parts of the world, technological systems like GAI applications are largely imported from a small number of (often Western) companies. This means data used to build the models, as well as the models and tools, are imported from people and entities who likely do not understand the nuances of the information environment of the end users. This has led to greater public skepticism about — and even distrust toward — the implementation of these tools, both in general and in journalism specifically. One participant noted:

News is not a digital-first product in all parts of the world. Print, television and radio are still popular modes of news consumption in many countries. And there remain areas with much less internet accessthan others. So, as there is a push to digitization, “there’s an extent to which AI is still going to end up on the paper in some way, shape or form and we have to think about what that type of signaling may mean.” This raises the question, not yet given much consideration, of how AI use and policy translate to print. 

Discussions should also consider ways policy can support use of AI as a means to help close gaps in news access. There are parts of global society with less access to independent, fact-based news – whether the result of financial downfalls (e.g., news deserts in the U.S.), government control or high-risk environments such as war zones. Could AI be helpful here? If so, how? “We need to look at ways that we can rebuild newsrooms and do so in a way where it’s not just the large players that survive,” expressed one participant, “but local newsrooms survive, that everyone can actually have access to information.”

Wealthier news publishers have a greater advantage when it comes to AI use and licensing. There are currently clear financial advantages for large, well-resourced — and often Western — national and international news outlets. As pointed out by one participant: “Licensing is one way to obtain information, but you need to have funds, you need to have means … those with the biggest purses can obtain data and can possibly benefit more from AI. … That is bad from a public policy perspective because it means we’re not actually democratizing access to model development.” 

The varying relationships between governments and the press must be considered in policy discussions. In some contexts, policymakers simply may not value the principle of an independent press. In other contexts, such as in Caribbean nations, financial support via government ad spending is critical for the sustainability of media organizations, leading to a hesitance to create friction with their primary sources of funding. If governments do not trust media organizations’ adoption of AI tools, their actions can create economic problems for publishers or journalists.

For Further Consideration

What roles should legislative versus organizational policy play when it comes to addressing disparities in the use of AI as a public good?

5. BOTH INPUTS AND OUTPUTS, AT ALL STAGES OF BUILDING AND USING AI, NEED TO BE CONSIDERED THOROUGHLY IN POLICY DEVELOPMENT.

The last portion of the day was intended to be focused on copyright law, but many of the points raised during the discussion have broader policy implications. This section summarizes these points first, then makes note of considerations specific to copyright and AI in journalism.

A substantial and, as some participants noted, justifiable amount of attention and litigation has focused on the inputs of AI systems, particularly on what data models are being trained on, especially when it comes to news content. Less attention has been paid to the other end of the equation: these systems’ outputs. As one participant noted, there is only one ongoing case related to AI outputs. There was a general acknowledgement that policy and policy discussions must consider the totality of the system. 

Two key issues emerged around AI outputs: ownership and liability. Both issues connect to copyright law and will likely be addressed in courts and public policy. One participant outlined three potential policy approaches:

1. No AI-generated output is copyrighted and, thus, everything is in the public domain. This is the current policy approach in countries like the U.S.
2. Some form of copyright of AI-generated output is recognized, as long as there is some degree of human intervention, and the human would receive the copyrights.
3. AI-generated output receives short protections (e.g., 10 to 15 years) that could be registered to those who want to profit from transformative works.

Each option carries critical implications that are often lost in debates around AI inputs. Where is the cutoff point for AI-generated content? At what point in the editing process do we recognize content as having been transformed by AI and, therefore, no longer protected? Would this include the use of software, such as Photoshop, that have integrated AI tools? What does “human intervention” entail? And, for option three, what would be the duration of short-term protections (an option currently available in countries such as the United Kingdom)? Any policy, the group agreed, needs a clearly defined auditing process that includes evidence of steps taken in the content creation process. 

Participants also discussed the lack of clarity around output protections when it comes to patterns of language that models learn from snippets of words (rather than agreed-upon protections for creative expression like written articles). This ambiguity underlies other critical questions around issues like disinformation, such as decisions about whether to pull content like QAnon conspiracy theories out of model training data.

Clarity around outputs should be added to (but not replace) conversations about inputs: decisions around what data AI models are, or should be, trained on. As one participant noted, “Sticking to an understanding of the inputs here is important when we describe this interplay between the models and journalism, because journalism very much needs to be rooted in fact-based reporting.” Some suggested that the inputs question is potentially the easier issue to solve outside the bounds of policymaking: “You just pay [for data].” Though, it is not clear where current copyright law would fall on this and, as noted earlier, can lead – and has led – to a winner-takes-most approach where technology companies and news publishers with the most money and resources have the most to gain.

This discussion also included a broader conversation about liability, both within the context of copyright policy and beyond it. Participants noted that one critical area in AI policymaking is about the various layers of responsibility for the application of expression via AI tools including the model itself, its API, and its application (e.g., ChatGPT):

One element that came up at several points is the need for transparency from AI model developers in order to have more open discussions around how to apportion responsibility.

Spotlight on Copyright Law

OPPORTUNITIES FOR FURTHER ACTION

In addition to the areas for further consideration noted above, the convening introduced several potential opportunities for future work, including:

• The important remaining question of definitions of journalism in policy. While time limitations did not allow for a more thorough discussion of this question in this convening, participants noted a need to focus on definitions of journalism in policymaking alongside definitions of AI.
• Mapping the various elements of copyright and intellectual property protection and their impacts on journalism. While CNTI’s Issue Primer on copyright lays out the current state of copyright policy related to journalism, participants discussed the value of better understanding how exactly these laws apply to AI use in news. As one participant noted: “We should not only look at what makes sense for us in the here and now” but also “in contexts where different languages are spoken, where access is a huge problem, where skills are a huge problem, where disinformation has very different connotations … what can be the possible consequences for people who are more vulnerable, less empowered, in a different part of the world, and where the consequences can be way worse.”
• A need to take stock of AI practices (separate from guidelines), and establish a living repository of AI use cases related to journalism. CNTI developed a table of potential AI benefits and harms related to journalism (shown below) as a starting point for this discussion, though this was not an exhaustive list, and there is opportunity to develop it further into a digital resource. Participants also noted the need for an open database or repository of actual AI use cases (including their positive and negative impacts) that journalists and others creating and delivering fact-based news can add to. Some foundational work around this exists.

APPENDIX

List of Participants

About This Convening

CNTI’s cross-industry convenings espouse evidence-based, thoughtful and challenging conversations about the issue at hand, with the goal of building trust and ongoing relationships along with some agreed-upon approaches to policy. To that end, this convening adhered to a slightly amended Chatham House Rule:

1. Individuals are invited as leading thinkers from important parts of our digital news environment and as critical voices to finding feasible solutions. For the purposes of transparency, CNTI feels it is important to publicly list all attendees and affiliations present. Any reporting on the event, including CNTI’s reports summarizing key takeaways and next steps, can share information (including unattributed quotes) but cannot explicitly or implicitly identify who said what.
2. CNTI does request the use of photo and video at convenings. Videography is intended to help with the summary report. Any public use of video clips with dialogue by CNTI or its co-hosts requires the explicit, advance consent of the subject.
3. To maintain focus on the discussion at hand, we asked that there be no external posting during the event itself.

Participants were not asked to present prepared remarks; rather, this was a thoughtful guided discussion. To prepare, we asked that participants review CNTI’s Issue Primers on AI in journalism and modernizing copyright law.

Resources Shared by Participants

Participants at our convening event shared a number of helpful resources. Many of these resources are aimed at assisting local newsrooms. We present them in alphabetical order by organization/sponsor below. 

The American Journalism Project (AJP), which has announced a new partnership with OpenAI, serves as a useful resource for local newsrooms, bolstering local news projects to ensure all communities in the U.S. have access to trusted information. 

The Associated Press (AP) has launched, in addition to its Stylebook AI chapter, five new tools to improve workflow efficiencies in newsrooms with a focus on automation and transcription services. The AP also has assigned journalists on its global investigative team and beyond to cover artificial intelligence tools and their impacts on communities.

Another resource that organizations may find useful is CrowdSmart’s research on AI and conducting customer interviews, using AI to measure conversations and engage with human subjects, with the benefit of quantifying conversations in real time. 

Google has developed Pinpoint, a digital tool for analyzing and transcribing documents, that organizes and facilitates document collection. The company has also launched its Data Commons resource which enables users to access publicly available data from around the world.

Microsoft’s Journalism Hub shares tools to promote sustainable approaches to local journalism and includes a partnership with NOTA, an journalist-founded AI startup aiming to streamline newsroom processes without replacing journalists. Meanwhile, Microsoft’s open-data campaign aims to address inequalities in access by developing datasets and technologies that make data sharing easier. 

Finally, Respeecher, a novel startup founded in 2018, uses artificial intelligence to generate synthetic speech. The company has partnered with video game developers and motion picture studios to produce voices for characters – both fictional and real. 

We appreciate all of our participants for sharing these resources with CNTI.

About CNTI

The Center for News, Technology & Innovation (CNTI), an independent global policy research center, seeks to encourage independent, sustainable media, maintain an open internet and foster informed public policy conversations. CNTI’s cross-industry convenings espouse evidence-based, thoughtful but challenging conversations about the issue at hand, with an eye toward feasible steps forward.

The Center for News, Technology & Innovation is a project of the Foundation for Technology, News & Public Affairs.

Acknowledgements

CNTI sincerely thanks the participants of this convening for their time and insights, and we are grateful to the Computer History Museum, the co-sponsor and host of this AI convening. Special thanks to Dan’l Lewin, Marguerite Gong Hancock and David Murphy for their support, and to Marietje Schaake for moderating such a productive discussion.

CNTI is generously supported by Craig Newmark Philanthropies, John D. and Catherine T. MacArthur Foundation, John S. and James L. Knight Foundation, the Lenfest Institute for Journalism and Google.

The Issue

Early forms of artificial intelligence (prior to the development of generative AI) have been used for years to both create and distribute online news and information. Larger newsrooms have leveraged automation for years to streamline production and routine tasks, from generating earnings reports and sports recaps to producing tags and transcriptions. While these practices have been far less common in local and smaller newsrooms, they are being adopted more. Technology companies also increasingly use AI to automate critical tasks related to news and information, such as recommending and moderating content as well as generating search results and summaries.

Until now, public debates around the rise of artificial intelligence have largely focused on its potential to disrupt manual labor and operational work such as food service or manufacturing, with the assumption that creative work would be far less affected. However, a recent wave of accessible – and far more sophisticated – “generative AI” systems such as DALL-E, Lensa AI, Stable Diffusion, ChatGPT, Poe and Bard have raised concerns about their potential for destabilizing white-collar jobs and media work, abusing copyright (both against and by newsrooms), giving the public inaccurate information and eroding trust. At the same time, these technologies also create new pathways for sustainability and innovation in news production, ranging from generating summaries or newsletters and covering local events (with mixed results) to pitching stories and moderating comments sections.

As newsrooms experiment with new uses of generative AI, some of their practices have been criticized for errors and a lack of transparency. News publishers themselves are claiming copyright and terms-of-service violations by those using news content to build and train new AI tools (and, in some cases, striking deals with tech companies or blocking web crawler access to their content), while also grappling with the potential of generative AI tools to further shift search engine traffic away from news content. 

These developments introduce novel legal and ethical challenges for journalists, creators, policymakers and social media platforms. This includes how publishers use AI in news production and distribution, how AI systems draw from news content and how AI policy around the world will shape both. CNTI will address each of these, with a particular focus on areas that require legislation as well as those that should be a part of ethical journalism practice and policy. Copyright challenges emerging from artificial intelligence are addressed here, though CNTI also offers a separate issue primer focusing on issues of copyright more broadly.

What Makes it Complex

In considering legislation, it is unclear how to determine which AI news practices would fall within legal parameters, how to categorize those that do and how news practices differ from other AI uses.

What specific practices and types of content would be subject to legislative policy? How would it apply to other types of AI? What comprises “artificial intelligence” is often contested and difficult to define depending on how broad the scope is (e.g., if it includes or excludes classical algorithms) and whether one uses technical or “human-based” language (e.g., “machine learning” vs. “AI”). New questions have emerged around the umbrella term of “generative AI” systems, most prominently Large Language Models (LLMs), further complicating these distinctions. These questions will need to be addressed in legislation in ways that protect citizens’ and creators’ fundamental rights and safety. At the same time, policymakers must consider the future risks of each system, as these technologies will continue to evolve at a faster pace than policy can reasonably keep up with.

The quantity and type of data collected by generative AI programs introduce new privacy and copyright concerns.

Among these concerns is what data is collected and how it is used by AI tools, in terms of both the input (the online data scraped to train these tools) and the output (the automated content itself). For example, Stable Diffusion was originally trained on 2.3 billion captioned images, including copyrighted works as well as images from artists, Pinterest and stock image sites. Additionally, news publishers have questioned whether their articles are being used to train AI tools without authorization, potentially violating terms-of-service agreements. In response, some technology companies have begun discussing agreements to pay publishers for use of their content to train generative AI models. This issue raises a central question about what in our digital world should qualify as a derivative piece of content, thereby tying it to copyright rules. Further, does new AI-generated content deserve its own copyright? If so, who gets the copyright: the developers who built the algorithm or the entity that published the content?

The copyright challenges of AI (addressed in more detail in our copyright issue primer) also present ethical dilemmas involved in profiting off of the output of AI models trained on copyrighted creative work without attribution or compensation.

Establishing transparency and disclosure standards for AI practices requires a coordinated approach between legal and organizational policies.

While some areas of transparency may make sense to be addressed through legal requirements (like current advertising disclosures), others will be more appropriate for technology companies and publishers to take on themselves. This means establishing their own principles, guidelines and policies for navigating the use of AI within their organizations – ranging from appropriate application to labeling to image manipulation. But these will need to both fit alongside any legal requirements and also be similar enough across organizations for public understanding. Newsroom education will also be critical, as journalists themselves are often unsure of how, or to what extent, their organizations rely on AI. For technology companies specifically, there is ongoing debate over requirements of algorithmic transparency (addressed in more detail in our algorithmic accountability issue primer) and the degree to which legal demand for this transparency could enable bad actors to hack or otherwise take advantage of the system in harmful ways.

The use of generative AI tools to create news stories presents a series of challenges around providing fact-based information to the public; the question of how that factors into legal or organizational policies remains uncertain.

Generative AI tools have initially been shown to produce content riddled with factual errors. Not only do they include false or entirely made-up information, but they are also “confidently wrong,” creating convincingly high-quality content and offering authoritative arguments for inaccuracies. Distinguishing between legitimate and illegitimate content (or even satire) will, therefore, become increasingly difficult – particularly as counter-AI tools have so far been ineffective. Further, it is easy to produce AI-generated images or content and use it to manipulate search engine optimization results. This can be exploited by spammers who churn out AI-generated “news” content or antidemocratic actors who create scalable and potentially persuasive propaganda and misinformation. For instance, while news publishers such as Semafor have generated powerful AI animations of Ukraine war eyewitness accounts in the absence of original footage, the same technology was weaponized by hackers to create convincing “deepfakes” of Ukrainian President Volodymyr Zelenskyy telling citizens to lay down their arms. While it is clear automation offers many opportunities to improve news efficiency and innovation, it also risks further commoditizing and undermining public trust in news.

There are inherent biases in generative AI tools that content generators and policymakers need to be aware of and guard against.

Because these technologies are usually trained on massive swaths of data scraped from the internet, they tend to replicate existing social biases and inequities. For instance, Lensa AI – a photo-editing app that launched a viral AI-powered avatar feature – has been alleged to produce hypersexualized and racialized images. Experts have expressed similar concerns about DALL-E and Stable Diffusion, which employ neural networks to transform text into imagery, which could be used to amplify stereotypes and produce fodder for sexual harassment or misinformation. The highly lauded AI text generator ChatGPT has been shown to generate violent, racist and sexist content (e.g. that only white men would make good scientists). Further, both the application of AI systems to sociocultural contexts they weren’t developed for and the human work in places like Kenya to make generative AI output less toxic present ethical issues. Finally, while natural language processing (NLP) is rapidly improving, AI tools’ training in dominant languages worsens longstanding access barriers for those who speak marginalized languages around the world. Developers have announced efforts to reduce some of these biases, but the longstanding, embedded nature of these biases and global use of the tools will make mitigation challenging.

State of Research

Artificial intelligence is no longer a fringe technology. Research finds a majority of companies, particularly those based in emerging economies, report AI adoption as of 2021. Experts have begun to document the increasingly critical role of AI for news publishers and technology companies, both separately and in relation to each other. And there is mounting evidence that AI technologies are routinely used both in social platforms’ algorithms and in everyday news work, though the latter is often concentrated among larger and upmarket publishers who have the resources to invest in these practices.

There are limitations to what journalists and the public understand when it comes to AI. Research shows there are gaps between the pervasiveness of AI uses in news and journalists’ understandings of and attitudes toward these practices. Further, audience-focused research on AI in journalism has found that news users often cannot discern between AI-generated and human-generated content. They also perceive there to be less media bias and higher credibility for certain types of AI-generated news, despite ample evidence that AI tools can perpetuate social biases and enable the development of disinformation.

Much of the existing research on AI in journalism has been theoretical. Even when the work is evidence-based, it is often more qualitative than quantitative, which allows us to answer some important questions, but makes a representative assessment of the situation difficult. Theoretical work has focused on the changing role of AI in journalism practice, the central role of platform companies in shaping AI and the conditions of news work, and the implications for AI dependence on journalism’s value and its ability to fulfill its democratic aims. Work in the media policy space has largely concentrated around European Union policy debates and the role of transparency around AI news practices in enhancing trust.

Future work should prioritize evidence-based research on how AI reshapes the news people get to see – both directly from publishers and indirectly through platforms. AI research focused outside of the U.S. and outside economically developed countries would offer a fuller understanding of how technological changes affect news practices globally. On the policy side, comparative analyses of use cases would aid in developing transnational best practices in news transparency and disclosure around AI.

State of Legislation

The latest wave of AI innovation has, in most countries, far outpaced governmental oversight or regulation. Regulatory responses to emerging technologies like AI have ranged from direct regulation to soft law (e.g. guidelines) to industry self-regulation, and they vary by country. Some governments, such as Russia and China, directly or indirectly facilitate – and thus often control – the development of AI in their countries. Others attempt to facilitate innovation by involving various stakeholders. Some actively seek to regulate AI technology and protect citizens against its risks. For example, when it comes to privacy the EU’s legislation has placed heavy emphasis on robust protections of citizens’ data from commercial and state entities, while countries like China assume the state’s right to collect and use citizens’ data.

These differences reflect a lack of agreement over what values should underpin AI legislation or ethics frameworks and make global consensus over its regulation challenging. That said, legislation in one country can have important effects elsewhere. It is important that those proposing policy and other solutions recognize global differences and consider the full range of their potential impacts without compromising democratic values of an independent press, an open internet and free expression.

Legislative policies specifically intended to regulate AI can easily be weakened by a lack of clarity around what qualifies as AI, making violations incredibly hard to identify and enforce. Given the complexity of these systems and the speed of innovation in this field, experts have called for individualized and adaptive provisions rather than one-size-fits-all responses. Recommendations for broader stakeholder involvement in building AI legislation also include engaging groups (such as marginalized or vulnerable communities) that are often most impacted by its outcomes.

Finally, as the role of news content in the training of AI systems becomes an increasingly central part of regulatory and policy debates, responses to AI developments will likely need to account for the protection of an independent, competitive news media. Currently, this applies to policy debates about modernizing copyright and fair use provisions for digital content as well as collective bargaining codes and other forms of economic support between publishers and the companies that develop and commodify these technologies.

Notable Legislation

Brazil

In September 2021, Brazil’s Chamber of Deputies approved the Marco Legal da Inteligência Artificial to regulate the development of AI technologies and promote research on AI ethics and accountability. In May 2023, a bill was proposed to regulate AI use based on recommendations made by a working group created in 2022. The stated aim of this legislation is to protect citizens’ fundamental rights and, like the EU’s AI Act, introduces a risk-based regulatory model for AI systems.

Read more here.

Canada

Canada’s proposed Artificial Intelligence & Data Act (AIDA), Bill C-27, would place guardrails on AI uses and enforce penalties for noncompliance. These requirements focus on addressing AI bias, transparency, risk mitigation and record-keeping. A lack of definitional clarity in the legislation makes it unclear exactly which systems it proposes to regulate. Because the AIDA focuses on deliberately harmful AI use, there is also a lack of clarity on the “gray areas” of AI harms. 

Read more here.

China

In 2021, China’s Ministry of Science and Technology published new ethical guidelines for the use of AI in China. These guidelines build off of the 2017 “A Next Generation Artificial Intelligence Development Plan.” The guidelines focus on promoting and actively monitoring AI development by technology companies to achieve national priorities. They also mandate the use of the People’s Republic of China’s national standards for AI, as they apply to big data, cloud computing and industrial software.

European Union

The EU’s AI Act, adopted in June 2023, assigns AI applications to three risk categories, banning uses of systems with “unacceptable” risk levels (violating fundamental rights or safety) and increasing regulation of “high-risk” systems. It also requires that all copyrightable data used in AI model training be published. Concerns include a lack of clarity of what constitutes AI, flexibility in the regulation process and unintended legal implications for marginalized communities. 

Read more here.

Italy

In April 2023, Italy’s data protection authority announced a probe into ChatGPT’s suspected breach of privacy regulations and ordered OpenAI to stop processing Italian user data. The regulator noted concerns about ChatGPT’s lack of age restrictions and the generation of misinformation. OpenAI responded with plans to offer age verification tools, add more visibility to its privacy policy and allow EU users to object to the use of their data to train AI models, and ChatGPT was restored in Italy.

United States

In recent years in the U.S., a range of legislative proposals have been introduced that would, to some extent, regulate AI. The AI Bill of Rights, unveiled in October 2022, may set the stage for future legislation but lacks mechanisms for regulation. In March 2023, the U.S. Copyright Office launched an initiative to examine AI issues. In October 2023, the Biden administration released an executive order to develop safety guidelines around generative AI. Organizations have tracked AI proposals at the federal and state levels.

Read more here.

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