In courts across the country, publishers and creators are suing large AI companies for training generative AI models on copyrighted works. It’s easy to see these cases as a David-and-Goliath showdown between authors and publishers fighting for a cut of Big Tech company profits. But this fight is actually much larger. It affects billions of people who aren’t represented in the case at all, including researchers, artists, activists, journalists, people with disabilities, and anyone who accesses and shares content online.   

That’s why EFF is stepping in to defend the public’s rights in these cases. Today, EFF filed an amicus brief in In re OpenAI, explaining how the court’s ruling on fair use could impact people worldwide. If fair use gets narrowed, it will impede work that benefits the public, like medical research and investigative journalism, and even limit access to privacy protection tools.

Let’s be clear: creators and workers deserve support as AI changes their fields, and Big Tech companies must be held accountable for the harms they cause. But giving copyright holders more control over the web won’t accomplish either of these goals. If the plaintiffs prevail in these cases, courts would have to rewrite copyright law in ways that hurt everyone except powerful media and tech companies.

Rewriting Fair Use Makes Everyone Worse Off

Copyright exists for one reason: to stimulate creativity and innovation in service of the greater public good. Unfortunately, the plaintiffs in this lawsuit are arguing to limit everyone’s fair use rights, in ways that would thwart the basic purpose of copyright. The harms of shrinking fair use will go far beyond generative AI, impacting everyone.

Initial speculation that AI models would be infringement machines, churning out works highly similar to those on which they were trained, has proven unfounded. And courts have already found fair use in cases where copyrighted works were used to train generative AI models, recognizing that training uses those works for a fundamentally different purpose.

Plaintiffs in these cases argue that, even if the use is transformative, using their works to train AI is still unfair, because the AI models could generate new, non-infringing works that might compete with plaintiffs’ own works. They also argue that scraping or downloading the data without permission—or in some cases, from websites containing infringing copies—was independently infringing and/or rendered any subsequent uses of that data unfair.

These arguments would put beneficial research and reporting at risk. First, if copying without permission rules out fair use, the law will never protect transformative uses that benefit us all. Researchers, journalists, and other watchdog groups gather information the same way AI companies do—but they do it to serve the public interest, and often to hold Big Tech itself accountable. For example, journalists and nonprofit researchers have used “crawling” or “scraping” to investigate potentially anticompetitive practices by tech companies, and research misinformation and hateful content posted on X. Their data acquisition techniques are virtually identical to those used by AI giants—but they lack those companies’ deep pockets.

Requiring users to show that copyrighted material was lawfully obtained would also threaten everyday internet use. As EFF’s brief explained, that requirement would “cast legal doubt on the billions of copies we collectively make every day through routine online activities like opening emails, clicking on an embedded link, visiting websites, sharing pictures, streaming music, and watching videos. All of this activity requires intermediate copying.”

Fair use is what lets us all use the internet without being compelled to track the source of every image, video, and article that we click on, and make sure the rights are all cleared.

Additionally, while rightsholders are understandably concerned about competition from AI generated works, copyright law exists to promote new creation, not protect authors who published first. Under the plaintiffs’ theory, no one could stand upon the shoulders of giants. Copyright could prevent people from writing new books inspired by themes in a favorite novel or genre, or prevent academic researchers from publishing literature reviews that distill insights from prior research—simply because readers might prefer those new publications over those that came first.

Finally, general-purpose AI models are neither designed nor usually used to create works targeted at any particular market. These models have been used to generate new insights and tools for medicine, science, journalism, art, and accessibility. For instance, an AI-powered tool called Be My AI uses Open AI’s GPT models to provide visual descriptions for people who are blind or have low vision. Generative tools have also accelerated scientific research, and researchers have used these tools to develop new medical treatments and medications for conditions previously thought to be incurable.

Mandatory Licensing Would Entrench Big Tech

Some creators and publishers argue that AI developers should just get a license. But requiring permission for every work used would give the largest companies massive advantages over small competitors and impede academic research. As Professor Lawrence Lessig recently noted:

If all training requires a license, then none but a handful of tech giants will be able to afford the costs of training. That’s not because authors are greedy but because the copyright system is the least efficient property system known to man.

Moreover, creators are unlikely to benefit from these licensing arrangements in many cases. Media and entertainment giants have exploited the imbalance between themselves and underpaid working artists for decades. Trying to help creators by giving them more rights under copyright is, as Cory Doctorow has written, like trying to help a bullied kid by giving them more lunch money for the bully to take. Artists aren’t getting good deals from record labels or publishers now; there’s no reason to think these companies, which already own the copyrights, will treat artists more fairly once they control the data that powers AI.

The Future of AI Is Too Important to Leave to Big Content or Big Tech

AI technologies will affect all of us, and copyright law is ill-equipped to address many of these consequences. The rules governing these technologies should be rules that serve the public—not just the small number of companies that can afford to litigate copyright battles for decades, or settle for billions.

When the rich and powerful try to use the court to silence negative reporting about themselves, it’s worth calling out that behavior for what it is: an attack on free speech. This is why EFF is happy to stand up for reporters who find themselves in that situation.

The California Court of Appeals upheld a lower court’s decision to strike a former Premise Data CEO’s meritless lawsuit against a journalist who exposed the CEO’s secret arrest for felony domestic violence. Jack Poulson, the writer and publisher of All Source Intelligence, reported details from the San Francisco Police Department’s report of the arrest and posted a copy of the report after receiving the document from a confidential source. Poulson later learned the arrest record had been sealed. The CEO, Maury Blackman, sued Poulson, Substack, AWS, and another organization for damages to try and force the removal of Poulson’s reporting from the internet.

The trial court tossed the entire case under California’s anti-SLAPP statute—SLAPP stands for “strategic lawsuit against public participation” and describes cases where the goal isn’t vindication in court so much as it is costing someone time, money, and peace of mind fighting the lawsuit. To fight SLAPP cases, states like California have passed anti-SLAPP laws, which are invaluable tools for protecting the First Amendment. California’s law provides an avenue for early dismissals of these baseless lawsuits, which curtails their intended effect on the target. Blackman appealed the court’s decision, arguing that a court order sealing the arrest overrides Poulson’s right to report the news.

The Court of Appeals correctly rejected Blackman’s appeal and affirmed the decision to throw out the case. The court held that the First Amendment protects Poulson’s publications. As the court explained in its decision, “the First Amendment protects the lawfully obtained truthful publication of the information at issue absent ‘a need to further a state interest of the highest order,’” a standard that Blackman’s privacy interests do not satisfy. The Court also found that Poulson, as the publisher of the All Source Intelligence newsletter, was protected by California’s Shield Law, relying on precedent established by EFF in 2006. The Court also affirmed that Substack and the other website, which had merely temporarily hosted a copy of the arrest record, were immunized from liability by Section 230.

This decision is a win for free speech, for Jack Poulson, and for everybody.  

California Gov. Gavin Newsom's executive order is an opportunity for a needed, thoughtful conversation about artificial intelligence and its potential harms. Everyday Californians are feeling real anxiety about the risks of artificial intelligence, and as an organization that works to ensure technology empowers people, EFF welcomes this order as a way for the state of California to lead a much-needed dialogue that addresses these concerns. 

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Nonetheless, the most immediate and current concerns with this technology are not about sci-fi scenarios concerning rogue super-intelligence. They are happening right now through biased algorithmic decision-making for employment or government benefits, AI-powered surveillance systems such as Flock cameras, and artificially inflated personalized pricing. People want state and federal leaders to act, and we urge Gov. Newsom to develop thoughtful policies to address those concerns. Today’s EO is a good start. 

To that end, EFF supports the focus on expanding the reporting requirements under SB 53 (2025) for loss-of-control incidents, alongside third-party investigations. We urge the administration to consider how to make these third-party investigations available for smaller developers. As the Government Operations Agency prepares its recommendations for the governor, we urge leaders to also realize that the effectiveness of kill switches in advanced AI systems remains an area of active research. As such, they should ensure that - as we’ve previously mentioned - any technology regulation targeting cybersecurity practices at AI labs must be careful, precise, and practical. Moreover, we also caution that government-controlled kill switches run the risk of being used as a form of retaliation against protected speech, as demonstrated by the Trump Administration’s retaliatory actions against Anthropic earlier this year.

Ultimately, true safety requires California to focus on concrete, immediate, and urgent harms of AI technologies by ensuring that algorithmic decision-making in both the government and private sectors respects people’s rights and well-being. We urge Gov. Newsom and the state of California to develop thoughtful policy in collaboration with those most at risk of harm to address these and other concerns.

With doomsday AI scenarios dominating the news, lawmakers are rightly concerned about reports concerning security breaches at major US AI labs, such as the OpenAI–Hugging Face incident and the many others reported in its aftermath. As they consider potentially regulating frontier AI, they should focus any new legislation on the immediate, demonstrated risks from those incidents. 

Post-incident reports show that the Hugging Face incident could have been mitigated or prevented by following longstanding cybersecurity best practices, like stronger sandboxing and monitoring. Any new legislation should focus on closing gaps in existing law to prevent AI companies from taking unreasonable risks with the public's security.

When an AI developer or deployer runs a test or a task that has a high likelihood of causing harm to third parties—for instance, by breaking into someone else's computers—there should be clear minimum safety requirements. Such tests should run in a properly sandboxed test environment, disconnected from other systems, and be monitored and logged. Following these fundamental best practices would have prevented or substantially mitigated all of the incidents at AI labs that we currently know about.

That said, any proposal must be flexible enough to evolve with changing technology. Minimum safety requirements specific only to current AI technologies are likely to become obsolete; legal standards tied to well-established cybersecurity best practices are far more likely to stand the test of time. Tying any new mandates to evidence-backed security protocols also protects the public without impeding future AI development.

Strong legislation should also mandate and fund independent third-party investigations into any serious security incidents that may occur during AI labs’ tests of new tools, and make reports of these investigations available to the public. This important transparency measure would go a long way toward providing public oversight of the industry.

As with any technology regulation, those targeting cybersecurity practices at AI labs must be careful, precise, and practical.

The history of technology is rife with copyright panics.  In the 1980s, major rightsholders ran to Congress and the courts, claiming that videotape recorders (VTR) were “to the American film producer and the American public as the Boston strangler is to the woman home alone.” Then, the Supreme Court declined to embrace the hype, noting that the VTR was capable of all kinds of non-infringing uses, like time-shifting and cautioning courts to avoid rewriting copyright law in response to new technologies. We believe that courts now should be similarly wary about the hype surrounding AI.

Hollywood’s hyperbole has echoed that of composer John Phillip Sousa, who claimed in 1906 that the player piano and the gramophone would destroy music composition; portrait artists who feared the camera would replace the paintbrush. None of these things happened. Cameras, for example, sparked a resurgence of portraiture and, by making it possible for more people to create images, led to unexpected developments—like the rise of photojournalism.

New markets, new ideas, and new creators are actually what copyright is supposed to promote, not restrict. Using copyright to lock in existing gatekeepers and massive rightsholders’ profits helps neither the public nor individual artists.

Generative AI has sparked the latest wave of anxiety and with it a massive wave of litigation. In multiple cases around the U.S. and the world, rightsholders are asking courts to do precisely what the Supreme Court warned against: dramatically expand copyright protections based in substantial part on hyperbole and speculation. They should decline to do so.

Copyright owners claim that unless courts abandon 300-year-old copyright principles—and give rightsholders the power to control non-infringing works created by others—an imagined flood of AI-generated works will devastate creative markets. Under this “market dilution” theory, building generative AI tools cannot be fair use because those tools might be encourage the proliferation of competing works.

As EFF has explained to the courts in multiple amicus briefs in Concord Music Group, Inc. v. Anthropic PBC and In re Mosaic LLM Litigation, that’s not how copyright works. In fact, accepting this theory would undermine copyright’s constitutional purpose: promoting the creation of expressive works for the public’s benefit. Because copyright law is designed to encourage others to build freely on existing works, it punishes infringement, not competition. The “market dilution” theory would eviscerate not only the fair use doctrine, but also other limits on copyright that work specifically to prevent rightsholders from unfairly suppressing competition by claiming broad ownership over tropes, genres, styles, and so on. In other words, publishers would wield unchecked veto power over any expression that might conceivably compete with a work they own.

The result? Art doesn’t get created, ideas are never expressed, and we’re all worse off. Copyright shouldn’t be a tool to silence future creative competitors—whether or not they use AI in their work.

And the plaintiffs in these cases get at least two other things wrong. First, research shows that large generative AI models are unlikely to produce infringing works because the more data on which a model is trained, the less any individual training example matters to any particular output.

Second, AI tools aren’t necessarily displacing human creativity. To take a just a few examples:

• Boston-based artist Nettrice Gaskins uses AI to create Afro-futurist art, including a portrait of Octavia Butler displayed at the San Francisco Airport

• Indian artists Prateek Arora and Varun Gupta use generative AI to reimagine Western science fiction.

• Philadelphia-based artist Alex Smith uses generative AI to reimagine Afrofuturism with queer, plus-sized Black superheroes.

• Ana Miljački, a professor of architecture at MIT, used generative AI to create a “non-liner documentary” film on Yugoslav World War II memorials and the values they embodied.

• A research-creation project used AI generated visual art to both amplify the voices of activists in the Iran Woman Life Freedom Movement and evaluate AI’s role in sociopolitical advocacy through art.

• AI company Bronze works with musicians like Disclosure and Jai Paul to create songs that never sound the same when played back twice, challenging audience conceptions of what music could be.

It is not the place of courts to say these people are not artists or that AI cannot augment human creativity in a positive way.

Given this range of experimentation, courts should be reluctant to decide in advance what tools do and do not foster “human creativity.” Like the VTR, large language models are general purpose tools, used by humans to do a broad variety of things far beyond generating lyrics. The effects of this particular technological innovation will doubtless be far-reaching, disruptive, and potentially harmful for some—but distorting copyright law is not the way to address those harms.

EFF joined a group of 18 civil society organizations to send a letter encouraging New York Governor Kathy Hochul to veto Senate Bill 9934A, the New York Stealth Crawler Prohibition Act. The letter states:

While framed as a measure to protect local journalism, this legislation harms free expression and establishes a dangerous precedent by effectively deanonymizing and criminalizing automated access to the open web. By requiring all web crawlers to disclose their identity and explicit purpose, and by granting media outlets unchecked authority to obtain judicial subpoenas to unmask unidentified automated web traffic without any showing of misconduct or actual injury, this bill threatens digital privacy, compromises the foundational architecture of the internet, and will ultimately stifle the very independent journalism it seeks to protect.

As we’ve previously explained, so-called “stealth crawlers” are simply automated tools to access and collect public web data—without disclosing the user’s identity. Private crawlers like these facilitate all kinds of important work that benefits the public, including investigative reporting, academic research, cybersecurity protection, and EFF’s own Privacy Badger. As we illustrate in the letter: 

Anonymous crawling fuels important investigative journalism. For example, The Markup, a non-profit news site, used anonymous crawlers to investigate potentially anti-competitive practices by tech companies, such as Amazon’s tendency to prioritize Amazon brands and Amazon-exclusive products over competitors with higher ratings. The crawlers identified themselves as ordinary Firefox browsers to web servers, which allowed The Markup to understand how Amazon search results pages would appear to ordinary users. Similarly, ProPublica used an automated tool designed to simulate an ordinary Amazon customer to reveal that the site steered shoppers to more expensive products over cheaper alternatives. 

Anonymous web scraping is also crucial for cybersecurity professionals, who use automated tools to monitor the web for information that helps them protect against malicious attackers. Privacy tools, including EFF’s Privacy Badger, also crawl sites anonymously to identify trackers without compromising user privacy.

Laws like S9934A sweep far beyond AI, targeting anonymity rather than the real technical issue: overaggressive crawling that can overtax technological infrastructure. Unmasking crawlers won't fix these server strains, but it will chill vital public-interest research and compromise digital privacy. Addressing the harms of web scraping requires narrow technical solutions—not policies that give publishers veto power over the open web. This is why we are calling on Governor Hochul to veto S9934A.

You can read the full letter here. For a deeper dive into why crawlers and scrapers are vital for the open web, check out this blog post.

The Federal Trade Commission (FTC) in July issued a proposed policy statement “concerning the suppression of accuracy in artificial intelligence systems.” We urge the FTC to withdraw this misguided proposal and instead focus on its core strengths and mission to protect consumers. 

The new proposed policy builds on, and directly references, the Trump administration’s  “Preventing Woke AI in the Federal Government” executive order—a nightmare for civil liberties that seeks to strong-arm AI companies into modifying their models to conform with the its ideological agenda. In recently filed comments, EFF,  Public Knowledge, and Fight for the Future call for the FTC to stop its unconstitutional efforts to regulate lawful speech, override state laws, and intimidate AI developers into ideological alignment with the Trump administration.

The government may not install itself as the arbiter of truth.

In the joint comments, we outline three critical flaws within the latest proposed policy. First, it violates the First Amendment. The policy calls for the Commission to become the judge of which AI outputs meet an undefined standard of accuracy. Installing the FTC as the authority of this sort of viewpoint-based judgment is a prior restraint on speech. Additionally, the policy’s proposed solution to address speech concerns compounds, rather than properly limits, the likely harms to speech. As we say in our comments: the government may not install itself as the arbiter of truth. 

Second, it exceeds the FTC’s legal authority by claiming that its federal regulatory rules can override, or “preempt,” laws in states that have passed to regulate artificial intelligence use. This is clearly an attempt to target state laws the administration disagrees with. For example, the policy specifically criticizes Colorado's automated decisionmaking law, which applies when automated technology is used to consider consequential decisions such as those around employment, access to housing, health care, and insurance. We noted to the FTC that characterizing this law as one that requires AI companies to “suppress accuracy,” or encourages deception, is itself inaccurate. In any case, the FTC lacks the authority to put its rules in place over state law, unless Congress directly delegates it that power. It has been given no such power here.

Third, the policy is vague and sets the stage for improper jawboning of AI developers and companies that use AI tools (deployers). Jawboning is a term for situations in which the government urges private companies or people to censor another's speech. The proposal, as written, creates an enforcement regime that would put a thumb on the scale in favor of certain partisan speech and ideals. This will lead companies to censor only what the administration interprets as biased or untruthful. Yet, in our filing, we note that the FTC itself can't define an objective standard for what “bias” means, conceding the “exact line of what constitutes bias may be difficult to draw.”

There is work the FTC should be doing to protect consumers in the age of AI. In our comments, we conclude by saying:

[We] implore the Commission to focus on its core strengths and the mission for which it is so urgently needed—promoting structural market competition and protecting consumers from real unfair and deceptive acts and practices—in both the burgeoning and critically important AI industry and across the broader technology marketplace.

EFF and our partners have always urged the FTC to police genuine deception in technology markets. We have also consistently opposed government efforts to dictate what private speakers may say. That’s why we urge the FTC to withdraw this proposal. 

You can read our full comments here. 

There’s a new boogeyman in the battles over AI: so-called “stealth crawlers.” We’ll admit it—the term “stealth crawlers” sounds quite nefarious. In reality, they’re anything but.

“Stealth crawlers” are simply automated tools to access and collect public web data—without disclosing the user’s identity. Private crawlers like these facilitate all kinds of important work that benefits the public, including investigative reporting, academic research, cybersecurity protection, and more.

Anonymous crawling enables some of the most publicly beneficial uses of the open web.

Many publishers want to unmask crawlers anyways—and are pushing for new legislation that would give them new powers to do so. These legislative proposals threaten the open web, user privacy, and valuable research without directly addressing the problems they’re supposedly intending to solve.

Alarmingly, these harmful proposals are gaining traction. The New York state legislature has already passed such a bill, the NY Stealth Crawler Protection Act, which is now on Governor Hochul’s desk. We expect to see similar bills introduced in other states, and potentially in Congress. That’s a big problem for the open web—and the many benefits it provides.

Anonymous crawling is worth protecting

Anonymous crawling enables some of the most publicly beneficial uses of the open web. Researchers, journalists, and other watchdog groups use unidentified automated tools to gather the information necessary to hold powerful institutions accountable and protect the public.

Anonymous crawling fuels important investigative journalism. For example, The Markup, a non-profit news site, used anonymous crawlers to investigate potentially anti-competitive practices by tech companies, such as Amazon’s tendency to prioritize Amazon brands and Amazon-exclusive products over competitors with higher ratings. The crawlers identified themselves as ordinary Firefox browsers to web servers, which allowed The Markup to understand how Amazon search results pages would appear to ordinary users. Similarly, ProPublica used an automated tool designed to simulate an ordinary Amazon customer to reveal that the site steered shoppers to more expensive products over cheaper alternatives.

Anonymous web scraping is also crucial for cybersecurity professionals, who use automated tools to monitor the web for information that helps them protect against malicious attackers. Privacy tools, including EFF’s own Privacy Badger, also crawl sites anonymously to identify trackers without compromising user privacy.

However, without the ability to scrape anonymously, these tools would likely be blocked. Sites can—and do—block crawlers operated by researchers, journalists, and activists who criticize them. For example, Facebook shut down accounts belonging to researchers who used automated tools to study misinformation on the platform and demanded that they take down published research. Many sites block automated access by anyone who hasn’t paid to crawl public webpages.    

Unmasking crawlers threatens the open web

News publishers—and their allies in government—say that unmasking crawlers is necessary to protect news organizations from technological strain caused by AI-related crawling, and fears that AI could reduce news sites’ traffic and ad revenue. These are legitimate concerns.

But enacting broad, reactionary restrictions on automated access is not the answer. Legislation targeting anonymous crawling threatens the open web, user privacy, and valuable research without actually addressing these technological and potential economic harms of scraping.

The New York state legislature recently passed the NY Stealth Crawler Protection Act, a law that would make it illegal to crawl news websites without revealing who is operating the crawler and all possible future uses of the data collected by the crawler. The law would give websites the power to obtain court orders that unmask anyone using an unidentified crawler—without any evidence that they broke the law.

Laws like the New York bill sweep far beyond AI, and do not meaningfully address the technological or potential harms of AI-related web scraping. These policies would chill beneficial crawling by allowing publishers to veto lawful public access, giving them the power to block not just bad actors, but also security professionals, researchers, dissidents, or anyone who has not paid for a license to view public text. This needlessly undermines the free and open internet.

Digital news publishers—like most websites—face real technological challenges in the AI era. While web crawling has been around for decades, with the proliferation of AI, crawlers now collect far more public web data than they used to. This pushes servers closer to their maximum capacity, and if some bots collect information too aggressively, they may strain web servers to the point that it degrades site performance. The problem is not anonymity—so unmasking crawlers won’t solve it. The real problem is overaggressive crawling, which can be effectively addressed with technical measures that target harmful conduct without impeding anonymous access to information.

A better path forward

There are other, far less harmful ways to protect publishers from the harms these “stealth crawler” laws claim to target. Addressing the harms of AI-related crawling requires policies that narrowly target the causes of these issues–without undermining free expression and the open web. Policies that target crawlers and scrapers are anything but.

The ability to access publicly available information using automated tools is a central value and benefit of a free and open internet. Automated access—often called crawling or scraping—powers important, useful tools for locating, preserving, and analyzing online information. For example, crawling and scraping helps journalists, researchers, and watchdog organizations report the news, find security flaws, and investigate discrimination. Crawling the web allows non-profits like the Internet Archive to preserve historical copies of websites. Tools for automated comparison shopping allow consumers to find the best deals on items they want to buy. And so on.

Yet the open internet access is increasingly under threat from publishers and Big Tech companies alike. Fearing lost advertising and licensing revenues, website operators increasingly claim that they need to lock down their sites from bots that crawl public web content to train or operate AI models. Some companies are even trying to embed their business models into internet standards by changing Internet Engineering Task Force (IETF) technical standards that shape much of the internet.

Many of their economic anxieties are understandable. AI bots can strain websites’ infrastructure, in some cases, degrading site performance or taking them offline altogether. Upgrading systems costs money that some sites may not have. And AI is likely to disrupt the business models many publishers adopted in response to the rise of the internet, if users rely on AI overviews instead of visiting source websites.

However reasonable these fears may be, the answer is not to change the IETF standards from neutral protocols that encourage openness to restrictive requirements designed to monetize internet access.

The worst of these proposed standards would give websites far greater ability to automatically block legitimate, lawful scraping and crawling. For example, the AI Preferences working group is working on proposals to give publishers a way to express “preference signals” against crawling web data for AI-related purposes, including to train models, generate outputs, and help users search the web. These preference signals would be expressed through robots.txt and could potentially become legally binding in some jurisdictions.

Another working group, called Web Bot Auth, is pursuing efforts to protect sites from overly-aggressive bots that strain website resources—a positive goal that could meaningfully improve the internet in the AI era. But Web Bot Auth is simultaneously pursuing a much more dangerous path as well: standards changes that would enable sites to cryptographically identify bots so that they can more easily block anyone they wish—not just “bad” actors, but competitors, dissidents, or anyone who hasn’t paid for the right to access sites using automated tools. If sites restrict crawling to a preapproved list of cryptographically authenticated bots, they could require licensing payments from those wishing to crawl their sites. This would close off the open web to researchers, archivists, and startups without the ability to pay for automated access.  

Websites may have legitimate reasons to worry about AI’s impacts on their traffic and advertising revenue, but those reasons must be weighed against the benefits of the open web. These proposals would effectively give website operators veto power over a wide range of important uses—from the investigations and archival works described above to accessibility tools for people with disabilities, to research efforts aimed at holding governments accountable.

That is why we are fighting back against these threats to open access. EFF and our allies in the open internet community have successfully resisted some of the most dangerous IETF proposals thus far—and won’t stop working to protect the open web from efforts to manipulate internet standards to undermine the right to freely access the internet in any legal way, including with automated tools.

As long as people have had more than one purchasing option, they’ve been comparing those options and looking for bargains. Online shoppers are no exception; in fact, one of the potential benefits of the internet is that it expands our options for everything from car rentals to airline tickets to dish soap. New AI tools can make the process even easier. These tools could provide some welcome relief for consumers facing sky-high prices that many cannot afford.

Unfortunately, Amazon is trying to block these helpful new tools, which can steer shoppers towards competitors. Taking a page from Facebook and RyanAir, they are trying to use computer crime laws to do it. 

Amazon’s target is Perplexity, which makes an AI-enabled web browser, called Comet, that allows users to browse the web as they normally would, but can also perform certain actions on the user’s behalf. For example, a user could ask Comet to find the best price on a 24-pack of toilet paper, and if satisfied with the results, have the browser order it. Amazon claims that Perplexity violated the Computer Fraud and Abuse Act (CFAA) by building a tool that helps users access information on Amazon and engage with the site.

Unfortunately, a federal district court agreed. The court’s fundamental mistake: relying on the Ninth Circuit’s misguided decision in Facebook v Power Ventures, rather than the court’s much better and more applicable reasoning in hiQ Labs.

Perplexity has appealed to the Ninth Circuit. As we explain in an amicus brief filed in support, the district court’s mistake, if affirmed, could lead to myriad unintended consequences. Overbroad readings of the CFAA have undermined research, security, competition, and innovation. For years, we’ve worked to limit its scope to Congress’s original intention: actual hacking that bypasses computer security. It should have nothing to do with Amazon’s claims here, not least because most of Amazon’s website is publicly available.

The court’s approach would be especially dangerous for journalists and academic researchers. Researchers often create a variety of testing accounts. For example, if they’re researching how a service displays housing offers, they may create separate accounts associated with different race, gender, or language settings. These sorts of techniques may be adversarial to the company, but they shouldn’t be illegal. But according to the court’s opinion, if a company disagrees with this sort of research, it can’t just ban the researchers from using the site; it can render that research criminal by just sending a letter notifying the researcher that they’re not authorized to use the service in this way.

A broad reading of CFAA in this case would also undermine competition by enabling companies to limit data scraping, effectively cutting off one of the ways websites offer tools to compare prices and features.

The Ninth Circuit should follow Van Buren’s lead and interpret the CFAA narrowly, as Congress intended. Website owners do not need new shields against independent accountability.

The rapidly escalating conflict between Anthropic and the Pentagon, which started when the company refused to let the government use its technology to spy on Americans, has now gone to court. The Department of Defense retaliated by designating the company a “supply chain risk” (SCR). Now, Anthropic is asking courts to block the designation, arguing that the First Amendment does not permit the government to coerce a private actor to rewrite its code to serve government ends.

We agree.

As EFF, the Foundation for Individual Rights and Expression, and multiple other public interest organizations explained in a brief filed in support of Anthropic’s motion, the development and operation of large language models involve multiple expressive choices protected by the First Amendment. Requiring a company to rewrite its code to remove guardrails means compelling different expression, a clear constitutional violation. Further, the public record shows that the SCR designation is intended to punish the company both for pushing back and for its CEO’s public statements explaining that AI may supercharge surveillance practices that current law has proven ill-equipped to address.

As we also explain, the company’s concerns about how the government will use its technology are well-founded. The U.S. government has a long history of illegally surveilling its citizens without adequate judicial oversight based on questionable interpretations of its Constitutional and statutory obligations. The Department of Defense acquires vast troves of personal information from commercial entities, including individuals’ physical location, social media, and web browsing data. Other government agencies continue to collect and query vast quantities of Americans’ information, including by acquiring information from third party data brokers.

A growing body of social science research illustrates the chilling effects of these pervasive activities. Fearing retribution for unpopular views, dissenters stay silent. And AI only exacerbates the problem. AI can quickly analyze the government’s massive datasets or combine that information with data scraped off the internet, purchased through the commercial data broker market, or from local police surveillance devices and use all of that data to construct a comprehensive picture of a person’s life and infer sensitive details like their religious beliefs, medical conditions, political opinions, or even sex partners. For example, an agency could use AI to infer an individual’s association with a particular mosque based on data showing that they visited its website, followed its social media accounts, and were located near the mosque during religious services. AI can also deanonymize online speech by using public information to unmask anonymous users.

It is easy to conceive how an agency, a government employee with improper intent, or a malicious hacker could exploit these capabilities to monitor public discourse, preemptively squelch dissent, or persecute people from marginalized communities. Against this background and absent meaningful changes to the governing national security laws and judicial oversight structure, it is entirely reasonable for Anthropic—or any other company—to insist on its own guardrails.

Without action from Congress, the task of protecting your privacy has fallen in large part to Big Tech—something no one wants, including Big Tech. But if Congress won’t do it, companies like Anthropic must be allowed to step in, without facing retribution.

The phrase "artificial intelligence" has been around for a long time, covering everything from computers with "brains"—think Data from Star Trek or Hal 9000 from 2001: A Space Odyssey—to the autocomplete function that too often has you sending emails to the wrong person. It's a term that sweeps a wide array of uses into it—some well-established, others still being developed.

Recent news shows us a rapidly expanding catalog of potential harms that may result from companies pushing AI into every new feature and aspect of public life—like the automation of bias that follows from relying on a backward-looking technology to make consequential decisions about people's housing, employment, education, and so on. Complicating matters, the computation needed for some AI services requires vast amounts of water and electricity, leading to sometimes difficult questions about whether the increased fossil fuel use or consumption of water is justified.

We are also inundated with advertisements and exhortations to use the latest AI-powered apps, and with hype insisting AI can solve any problem.

Obscured by this hype, there are some real examples of AI proving to be a helpful tool. For example, machine learning is especially useful for scientists looking at everything from the inner workings of our biology to cosmic bodies in outer space. AI tools can also improve accessibility for people with disabilities, facilitate police accountability initiatives, and more. There are reasons why these problems are amenable to machine learning and why excitement over these uses shouldn’t translate into a perception that just any language model or AI technology possesses expert knowledge or can solve whatever problem it’s marketed as solving.

EFF has long fought for sensible, balanced tech policies because we’ve seen how regulators can focus entirely on use cases they don’t like (such as the use of encryption to hide criminal behavior) and cause enormous collateral harm to other uses (such as using encryption to hide dissident resistance). Similarly, calls to completely preempt state regulation of AI would thwart important efforts to protect people from the real harms of AI technologies. Context matters. Large language models (LLMs) and the tools that rely on them are not magic wands—they are general-purpose technologies. And if we want to regulate those technologies in a way that doesn’t shut down beneficial innovations, we have to focus on the impact(s) of a given use or tool, by a given entity, in a specific context. Then, and only then, can we even hope to figure out what to do about it.

So let’s look at the real-world landscape.

AI’s Real and Potential Harms

Thinking ahead about potential negative uses of AI helps us spot risks. Too often, the corporations developing AI tools—as well as governments that use them—lose sight of the real risks, or don’t care. For example, companies and governments use AI to do all sorts of things that hurt people, from price collusion to mass surveillance. AI should never be part of a decision about whether a person will be arrested, deported, placed into foster care, or denied access to important government benefits like disability payments or medical care.

There is too much at stake, and governments have a duty to make responsible, fair, and explainable decisions, which AI can’t reliably do yet. Why? Because AI tools are designed to identify and reproduce patterns in data that they are “trained” on.  If you train AI on records of biased government decisions, such as records of past arrests, it will “learn” to replicate those discriminatory decisions.

And simply having a human in the decision chain will not fix this foundational problem. Studies have shown that having a human “in the loop” doesn’t adequately correct for AI bias, both because the human tends to defer to the AI and because the AI can provide cover for a biased human to ratify decisions that agree with their biases and override the AI at other times.

These biases don’t just arise in obvious contexts, like when a government agency is making decisions about people. It can also arise in equally life-affecting contexts like medical care. Whenever AI is used for analysis in a context with systemic disparities and whenever the costs of an incorrect decision fall on someone other than those deciding whether to use the tool.  For example, dermatology has historically underserved people of color because of a focus on white skin, with the resulting bias affecting AI tools trained on the existing and biased image data.

These kinds of errors are difficult to detect and correct because it’s hard or even impossible to understand how an AI tool arrives at individual decisions. These tools can sometimes find and apply patterns that a human being wouldn't even consider, such as basing diagnostic decisions on which hospital a scan was done at. Or determining that malignant tumors are the ones where there is a ruler next to them—something that a human would automatically exclude from their evaluation of an image. Unlike a human, AI does not know that the ruler is not part of the cancer.

Auditing and correcting for these kinds of mistakes is vital, but in some cases, might negate any sort of speed or efficiency arguments made in favor of the tool. We all understand that the more important a decision is, the more guardrails against disaster need to be in place. For many AI tools, those don't exist yet. Sometimes, the stakes will be too high to justify the use of AI. In general, the higher the stakes, the less this technology should be used.

We also need to acknowledge the risk of over-reliance on AI, at least as it is currently being released. We've seen shades of a similar problem before online (see: "Dr. Google"), but the speed and scale of AI use—and the increasing market incentive to shoe-horn “AI” into every business model—have compounded the issue.

Moreover, AI may reinforce a user’s pre-existing beliefs—even if they’re wrong or unhealthy. Many users may not understand how AI works, what it is programmed to do, and how to fact check it. Companies have chosen to release these tools widely without adequate information about how to use them properly and what their limitations are. Instead they market them as easy and reliable. Worse, some companies also resist transparency in the name of trade secrets and reducing liability, making it harder for anyone to evaluate AI-generated answers. 

Other considerations may weigh against AI uses are its environmental impact and potential labor market effects. Delving into these is beyond the scope of this post, but it is an important factor in determining if AI is doing good somewhere and whether any benefits from AI are equitably distributed.

Research into the extent of AI harms and means of avoiding them is ongoing, but it should be part of the analysis.

AI’s Real and Potential Benefits

However harmful AI technologies can sometimes be, in the right hands and circumstances, they can do things that humans simply can’t. Machine learning technology has powered search tools for over a decade. It’s undoubtedly useful for machines to help human experts pore through vast bodies of literature and data to find starting points for research—things that no number of research assistants could do in a single year. If an actual expert is involved and has a strong incentive to reach valid conclusions, the weaknesses of AI are less significant at the early stage of generating research leads. Many of the following examples fall into this category.

Machine learning differs from traditional statistics in that the analysis doesn’t make assumptions about what factors are significant to the outcome. Rather, the machine learning process computes which patterns in the data have the most predictive power and then relies upon them, often using complex formulae that are unintelligible to humans. These aren’t discoveries of laws of nature—AI is bad at generalizing that way and coming up with explanations. Rather, they’re descriptions of what the AI has already seen in its data set.

To be clear, we don't endorse any products and recognize initial results are not proof of ultimate success. But these cases show us the difference between something AI can actually do versus what hype claims it can do.

Researchers are using AI to discover better alternatives to today’s lithium-ion batteries, which require large amounts of toxic, expensive, and highly combustible materials. Now, AI is rapidly advancing battery development: by allowing researchers to analyze millions of candidate materials and generate new ones. New battery technologies discovered with the help of AI have a long way to go before they can power our cars and computers, but this field has come further in the past few years than it had in a long time.

AI Advancements in Scientific and Medical Research

AI tools can also help facilitate weather prediction. AI forecasting models are less computationally intensive and often more reliable than traditional tools based on simulating the physical thermodynamics of the atmosphere. Questions remain, though about how they will handle especially extreme events or systemic climate changes over time.

For example:

• The National Oceanic and Atmospheric Administration has developed new machine learning models to improve weather prediction, including a first-of-its-kind hybrid system that  uses an AI model in concert with a traditional physics-based model to deliver more accurate forecasts than either model does on its own. to augment its traditional forecasts, with improvements in accuracy when the AI model is used in concert with the physics-based model.
• Several models were used to forecast a recent hurricane. Google DeepMind’s AI system performed the best, even beating official forecasts from the U.S. National Hurricane Center (which now uses DeepMind’s AI model).

 Researchers are using AI to help develop new medical treatments:

• Deep learning tools, like the Nobel Prize-winning model AlphaFold, are helping researchers understand protein folding. Over 3 million researchers have used AlphaFold to analyze biological processes and design drugs that target disease-causing malfunctions in those processes.
• Researchers used machine learning simulate and computationally test a large range of new antibiotic candidates hoping they will help treat drug-resistant bacteria, a growing threat that kills millions of people each year.
• Researchers used AI to identify a new treatment for idiopathic pulmonary fibrosis, a progressive lung disease with few treatment options. The new treatment has successfully completed a Phase IIa clinical trial. Such drugs still need to be proven safe and effective in larger clinical trials and gain FDA approval before they can help patients, but this new treatment for pulmonary fibrosis could be the first to reach that milestone.
• Machine learning has been used for years to aid in vaccine development—including the development of the first COVID-19 vaccines––accelerating the process by rapidly identifying potential vaccine targets for researchers to focus on.

AI Uses for Accessibility and Accountability 

AI technologies can improve accessibility for people with disabilities. But, as with many uses of this technology, safeguards are essential. Many tools lack adequate privacy protections, aren’t designed for disabled users, and can even harbor bias against people with disabilities. Inclusive design, privacy, and anti-bias safeguards are crucial. But here are two very interesting examples:

• AI voice generators are giving people their voices back, after losing their ability to speak. For example, while serving in Congress, Rep. Jennifer Wexton developed a debilitating neurological condition that left her unable to speak. She used her cloned voice to deliver a speech from the floor of the House of Representatives advocating for disability rights.
• Those who are blind or low-vision, as well as those who are deaf or hard-of-hearing, have benefited from accessibility tools while also discussing their limitations and drawbacks. At present, AI tools often provide information in a more easily accessible format than traditional web search tools and many websites that are difficult to navigate for users that rely on a screen reader. Other tools can help blind and low vision users navigate and understand the world around them by providing descriptions of their surroundings. While these visual descriptions may not always be as good as the ones a human may provide, they can still be useful in situations when users can’t or don’t want to ask another human to describe something. For more on this, check out our recent podcast episode on “Building the Tactile Internet.”

When there is a lot of data to comb through, as with police accountability, AI is very useful for researchers and policymakers:

•  The Human Rights Data Analysis Group used LLMs to analyze millions of pages of records regarding police misconduct. This is essentially the reverse of harmful use cases relating to surveillance; when the power to rapidly analyze large amounts of data is used by the public to scrutinize the state there is a potential to reveal abuses of power and, given the power imbalance, very little risk that undeserved consequences will befall those being studied.
• An EFF client, Project Recon, used an AI system to review massive volumes of transcripts of prison parole hearings to identify biased parole decisions. This innovative use of technology to identify systemic biases, including racial disparities, is the type of AI use we should support and encourage.

It is not a coincidence that the best examples of positive uses of AI come in places where experts, with access to infrastructure to help them use the technology and the requisite experience to evaluate the results, are involved. Moreover, academic researchers are already accustomed to explaining what they have done and being transparent about it—and it has been hard won knowledge that ethics are a vital step in work like this.

Nor is it a coincidence that other beneficial uses involve specific, discrete solutions to problems faced by those whose needs are often unmet by traditional channels or vendors. The ultimate outcome is beneficial, but it is moderated by human expertise and/or tailored to specific needs.

Context Matters

It can be very tempting—and easy—to make a blanket determination about something, especially when the stakes seem so high. But we urge everyone—users, policymakers, the companies themselves—to cut through the hype. In the meantime, EFF will continue to work against the harms caused by AI while also making sure that beneficial uses can advance.

We're taking part in Copyright Week, a series of actions and discussions supporting key principles that should guide copyright policy. Every day this week, various groups are taking on different elements of copyright law and policy, and addressing what's at stake, and what we need to do to make sure that copyright promotes creativity and innovation.

Copyright owners increasingly claim more draconian copyright law and policy will fight back against big tech companies. In reality, copyright gives the most powerful companies even more control over creators and competitors. Today’s copyright policy concentrates power among a handful of corporate gatekeepers—at everyone else’s expense. We need a system that supports grassroots innovation and emerging creators by lowering barriers to entry—ultimately offering all of us a wider variety of choices.

Pro-monopoly regulation through copyright won’t provide any meaningful economic support for vulnerable artists and creators. Because of the imbalance in bargaining power between creators and publishing gatekeepers, trying to help creators by giving them new rights under copyright law is like trying to help a bullied kid by giving them more lunch money for the bully to take.

Entertainment companies’ historical practices bear out this concern. For example, in the late-2000’s to mid-2010’s, music publishers and recording companies struck multimillion-dollar direct licensing deals with music streaming companies and video sharing platforms. Google reportedly paid more than $400 million to a single music label, and Spotify gave the major record labels a combined 18 percent ownership interest in its now- $100 billion company. Yet music labels and publishers frequently fail to share these payments with artists, and artists rarely benefit from these equity arrangements. There’s no reason to think that these same companies would treat their artists more fairly now.

AI Training

In the AI era, copyright may seem like a good way to prevent big tech from profiting from AI at individual creators’ expense—it’s not. In fact, the opposite is true. Developing a large language model requires developers to train the model on millions of works. Requiring developers to license enough AI training data to build a large language model would  limit competition to all but the largest corporations—those that either have their own trove of training data or can afford to strike a deal with one that does. This would result in all the usual harms of limited competition, like higher costs, worse service, and heightened security risks. New, beneficial AI tools that allow people to express themselves or access information.

For giant tech companies that can afford to pay, pricey licensing deals offer a way to lock in their dominant positions in the generative AI market by creating prohibitive barriers to entry.

Legacy gatekeepers have already used copyright to stifle access to information and the creation of new tools for understanding it. Consider, for example, Thomson Reuters v. Ross Intelligence, the first of many copyright lawsuits over the use of works train AI. ROSS Intelligence was a legal research startup that built an AI-based tool to compete with ubiquitous legal research platforms like Lexis and Thomson Reuters’ Westlaw. ROSS trained its tool using “West headnotes” that Thomson Reuters adds to the legal decisions it publishes, paraphrasing the individual legal conclusions (what lawyers call “holdings”) that the headnotes identified. The tool didn’t output any of the headnotes, but Thomson Reuters sued ROSS anyways. A federal appeals court is still considering the key copyright issues in the case—which EFF weighed in on last year. EFF hopes that the appeals court will reject this overbroad interpretation of copyright law. But in the meantime, the case has already forced the startup out of business, eliminating a would-be competitor that might have helped increase access to the law.

Requiring developers to license AI training materials benefits tech monopolists as well. For giant tech companies that can afford to pay, pricey licensing deals offer a way to lock in their dominant positions in the generative AI market by creating prohibitive barriers to entry. The cost of licensing enough works to train an LLM would be prohibitively expensive for most would-be competitors.

The DMCA’s “Anti-Circumvention” Provision

The Digital Millennium Copyright Act’s “anti-circumvention” provision is another case in point. Congress ostensibly passed the DMCA to discourage would-be infringers from defeating Digital Rights Management (DRM) and other access controls and copy restrictions on creative works.

Section 1201 has been used to block competition and innovation in everything from printer cartridges to garage door openers

In practice, it’s done little to deter infringement—after all, large-scale infringement already invites massive legal penalties. Instead, Section 1201 has been used to block competition and innovation in everything from printer cartridges to garage door openers, videogame console accessories, and computer maintenance services. It’s been used to threaten hobbyists who wanted to make their devices and games work better. And the problem only gets worse as software shows up in more and more places, from phones to cars to refrigerators to farm equipment. If that software is locked up behind DRM, interoperating with it so you can offer add-on services may require circumvention. As a result, manufacturers get complete control over their products, long after they are purchased, and can even shut down secondary markets (as Lexmark did for printer ink, and Microsoft tried to do for Xbox memory cards.)

Giving rights holders a veto on new competition and innovation hurts consumers. Instead, we need balanced copyright policy that rewards consumers without impeding competition.

EFF asked a California appeals court to uphold a lower court’s decision to strike a tech CEO’s lawsuit against a journalist that sought to silence reporting the CEO, Maury Blackman, didn’t like.

The journalist, Jack Poulson, reported on Maury Blackman’s arrest for felony domestic violence after receiving a copy of the arrest report from a confidential source. Blackman didn’t like that. So, he sued Poulson—along with Substack, Amazon Web Services, and Poulson’s non-profit, Tech Inquiry—to try and force Poulson to take his articles down from the internet.

Fortunately, the trial court saw this case for what it was: a classic SLAPP, or a strategic lawsuit against public participation. The court dismissed the entire complaint under California’s anti-SLAPP statute, which provides a way for defendants to swiftly defeat baseless claims designed to chill their free speech.

The appeals court should affirm the trial court’s correct decision.  

Poulson’s reporting is just the kind of activity that the state’s anti-SLAPP law was designed to protect: truthful speech about a matter of public interest. The felony domestic violence arrest of the CEO of a controversial surveillance company with U.S. military contracts is undoubtedly a matter of public interest. As we explained to the court, “the public has a clear interest in knowing about the people their government is doing business with.”

Blackman’s claims are totally meritless, because they are barred by the First Amendment. The First Amendment protects Poulson’s right to publish and report on the incident report. Blackman argues that a court order sealing the arrest overrides Poulson’s right to report the news—despite decades of Supreme Court and California Court of Appeals precedent to the contrary. The trial correctly rejected this argument and found that the First Amendment defeats all of Blackman’s claims. As the trial court explained, “the First Amendment’s protections for the publication of truthful speech concerning matters of public interest vitiate Blackman’s merits showing.”

The court of appeals should reach the same conclusion.

A tidal wave of copyright lawsuits against AI developers threatens beneficial uses of AI, like creative expression, legal research, and scientific advancement. How courts decide these cases will profoundly shape the future of this technology, including its capabilities, its costs, and whether its evolution will be shaped by the democratizing forces of the open market or the whims of an oligopoly. As these cases finished their trials and moved to appeals courts in 2025, EFF intervened to defend fair use, promote competition, and protect everyone’s rights to build and benefit from this technology.

At the same time, rightsholders stepped up their efforts to control fair uses through everything from state AI laws to technical standards that influence how the web functions. In 2025, EFF fought policies that threaten the open web in the California State Legislature, the Internet Engineering Task Force, and beyond.

Fair Use Still Protects Learning—Even by Machines

Copyright lawsuits against AI developers often follow a similar pattern: plaintiffs argue that use of their works to train the models was infringement and then developers counter that their training is fair use. While legal theories vary, the core issue in many of these cases is whether using copyrighted works to train AI is a fair use.

We think that it is. Courts have long recognized that copying works for analysis, indexing, or search is a classic fair use. That principle doesn’t change because a statistical model is doing the reading. AI training is a legitimate, transformative fair use, not a substitute for the original works.

More importantly, expanding copyright would do more harm than good: while creators have legitimate concerns about AI, expanding copyright won’t protect jobs from automation. But overbroad licensing requirements risk entrenching Big Tech’s dominance, shutting out small developers, and undermining fair use protections for researchers and artists. Copyright is a tool that gives the most powerful companies even more control—not a check on Big Tech. And attacking the models and their outputs by attacking training—i.e. “learning” from existing works—is a dangerous move. It risks a core principle of freedom of expression: that training and learning—by anyone—should not be endangered by restrictive rightsholders.

In most of the AI cases, courts have yet to consider—let alone decide—whether fair use applies, but in 2025, things began to speed up.

But some cases have already reached courts of appeal. We advocated for fair use rights and sensible limits on copyright in amicus briefs filed in Doe v. GitHub, Thomson Reuters v. Ross Intelligence, and Bartz v. Anthropic, three early AI copyright appeals that could shape copyright law and influence dozens of other cases. We also filed an amicus brief in Kadrey v. Meta, one of the first decisions on the merits of the fair use defense in an AI copyright case.

How the courts decide the fair use questions in these cases could profoundly shape the future of AI—and whether legacy gatekeepers will have the power to control it. As these cases move forward, EFF will continue to defend your fair use rights.

Protecting the Open Web in the IETF

Rightsholders also tried to make an end-run around fair use by changing the technical standards that shape much of the internet. The IETF, an Internet standards body, has been developing technical standards that pose a major threat to the open web. These proposals would give websites to express “preference signals” against certain uses of scraped data—effectively giving them veto power over fair uses like AI training and web search.

Overly restrictive preference signaling threatens a wide range of important uses—from accessibility tools for people with disabilities to research efforts aimed at holding governments accountable. Worse, the IETF is dominated by publishers and tech companies seeking to embed their business models into the infrastructure of the internet. These companies aren’t looking out for the billions of internet users who rely on the open web.

That’s where EFF comes in. We advocated for users’ interests in the IETF, and helped defeat the most dangerous aspects of these proposals—at least for now.

Looking Ahead

The AI copyright battles of 2025 were never just about compensation—they were about control. EFF will continue working in courts, legislatures, and standards bodies to protect creativity and innovation from copyright maximalists.