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.

What do EFF staffers Sarah Chen, Javier Morales, Caitlin Chin, Emma Rodriguez, and Mikko Kopponen have in common? 

For one thing, they don’t exist. 

For another, all have been quoted as EFF experts in articles published in the past two months on a site called News-USA Today, which describes itself as “an independent news publisher focused on clear, accurate, and useful journalism.” 

Uh… 

(Please don’t confuse this site with USA Today, in which real EFF experts are accurately quoted on a regular basis.) 

News-USA Today is hardly the only slagheap that’s hallucinating or fabricating EFF personnel and quotes; as we wrote last September, media companies large and small are using AI to generate news content because it’s cheaper than paying for journalists’ salaries, but that savings can come at the cost of the outlets’ reputations— assuming they care about reputation at all. 

But this many fake EFF sources in two months? That’s making a play for the championship title of bogus news content. 

News-USA Today’s site proclaims, “Our goal is simple: give readers the facts and the context they need to make informed decisions.” It then defines its mission:

• “Deliver timely, factual reporting grounded in verifiable sources and public documents.”
• “Make complex topics understandable without losing nuance or accuracy.”
• “Serve the public interest by surfacing stories that affect lives, institutions, and communities.”
• “Maintain a clear separation between news, analysis, opinion, and sponsored content.” 

Attempts to reach contacts listed on the site went unanswered. In fact, after we reached out to them, they published a story on June 9 with quotes from Electronic Frontier Foundation Executive Director Jared Cohen — who also doesn’t exist. 

As we noted last year, EFF is all about having our words spread far and wide. Per our copyright policy, any and all original material on the EFF website may be freely distributed at will under the Creative Commons Attribution 4.0 International License (CC-BY), unless otherwise noted.  

However, we don't want disreputable sites making up words (or false identities!) for us, whether or not they’re using AI. False quotations that misstate our positions damage the trust that the public and reputable media outlets have in us.  

The best thing a news consumer can do is invest a little time and energy to learn how to discern the real from the fake. It’s unfortunate that it's the public’s burden to put in this much effort, but while we're adjusting to new tools and a new normal, a little effort now can go a long way.   

As we’ve noted before in the context of election misinformation, the nonprofit journalism organization ProPublica has published a handy guide about how to tell if what you’re reading is accurate or “fake news,” as has FactCheck.org. 

Governments must not adopt emerging and powerful AI technologies without also adopting strong and clear safeguards to protect Constitutional rights, EFF Senior Policy Analyst Dr. Matthew Guariglia testified today to the House Homeland Security Subcommittee on Cybersecurity and Infrastructure Protection. 

During the hearing on “The AI Security Landscape: How Frontier Models, Agentic AI, and AI Coding Tools Are Reshaping Cybersecurity and Critical Infrastructure Resilience,” he explained that the use of generative AI for the purposes of mass government surveillance would supercharge unconstitutional violations of civil liberties. He also highlighted how government secrecy, in addition to the black box of for-profit proprietary technology, prevents the public and lawmakers from knowing when AI models make mistakes, including errors that seriously impact the cybersecurity of critical infrastructure and the lives of individuals.  

“AI also has a track record of getting things wrong—from false citations on legal briefs to a major AI mistake that sent DHS recruits to the field without proper training. There are likely more consequential examples that we do not even know about because of classification that would prevent a more thorough accounting," he said in his opening remarks.

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“At this level the question is not how do we rein in AI, it’s how do we rein in the agencies that would unleash AI on the American public,” Matthew said in response to a question by Subcommittee Ranking Member Delia Ramirez, D-Ill.  

You can read his full testimony as prepared here. 

While the very public fight continues between the Department of Defense and Anthropic over whether the government can punish a company for refusing to allow its technology to be used for mass surveillance, another agency of the U.S. government is quietly working to ensure that this dispute will never happen again. How? By rewriting government procurement rules.

Using procurement — meaning, the processes by which governments acquire goods and services — to accomplish policy goals is a time-honored and often appropriate strategy. The government literally expresses its politics and priorities by deciding where and how it spends its money. To that end, governments can and should give our tax dollars to companies and projects that serve the public interest, such as open-source software development, interoperability, or right to repair. And they should withhold those dollars from those that don’t, like shady contractors with inadequate security systems.

New proposed rules for the principal agency in charge of acquiring goods, property, and services for the federal government, the General Services Administration (GSA), are supposed to be primarily an effort to implement one policy priority: promoting “ideologically neutral” American AI innovation. But the new guidelines do far more than that.

As explained in comments filed today with our partners at the Center for Democracy and Technology, the Protect Democracy Project, and the Electronic Privacy Information Center, the GSA’s guidelines include broad provisions that would make AI tools less safe and less useful. If finally adopted, these provisions would become standard components of every federal contract. You can read the full comments here.

The most egregious example is a requirement that contractors and government service providers must license their AI systems to the government for “all lawful purposes.” Given the government’s loose interpretations of the law, ability to find loopholes to surveil you, and willingness to do illegal spying, we need serious and proactive legal restrictions to prevent it from gobbling up all the personal data it can acquire and using even routine bureaucratic data for punitive ends.

Relatedly, the draft rules require that “AI System(s) must not refuse to produce data outputs or conduct analyses based on the Contractor’s or Service Provider’s discretionary policies.” In other words, if a company’s safety guardrails might prevent responding to a government request, the company must disable those guardrails. Given widespread public concerns about AI safety, it seems misguided, at best, to limit the safeguards a company deems necessary.

There are myriad other problems with the draft rules, such as technologically incoherent “anti-Woke” requirements. But, the overarching problem is clear: much of this proposal would not serve the overall public interest in using American tax dollars to promote privacy, safety, and responsible technological innovation. The GSA should start over.

Imagine a newspaper publisher announcing it will no longer allow libraries to keep copies of its paper. 

That’s effectively what’s begun happening online in the last few months. The Internet Archive—the world’s largest digital library—has preserved newspapers since it went online in the mid-1990s. The Archive’s mission is to preserve the web and make it accessible to the public. To that end, the organization operates the Wayback Machine, which now contains more than one trillion archived web pages and is used daily by journalists, researchers, and courts.

But in recent months The New York Times began blocking the Archive from crawling its website, using technical measures that go beyond the web’s traditional robots.txt rules. That risks cutting off a record that historians and journalists have relied on for decades. Other newspapers, including The Guardian, seem to be following suit. 

For nearly three decades, historians, journalists, and the public have relied on the Internet Archive to preserve news sites as they appeared online. Those archived pages are often the only reliable record of how stories were originally published. In many cases, articles get edited, changed, or removed—sometimes openly, sometimes not. The Internet Archive often becomes the only source for seeing those changes. When major publishers block the Archive’s crawlers, that historical record starts to disappear.

The Times says the move is driven by concerns about AI companies scraping news content. Publishers seek control over how their work is used, and several—including the Times—are now suing AI companies over whether training models on copyrighted material violates the law. There’s a strong case that such training is fair use. 

Whatever the outcome of those lawsuits, blocking nonprofit archivists is the wrong response. Organizations like the Internet Archive are not building commercial AI systems. They are preserving a record of our history. Turning off that preservation in an effort to control AI access could essentially torch decades of historical documentation over a fight that libraries like the Archive didn’t start, and didn’t ask for. 

If publishers shut the Archive out, they aren’t just limiting bots. They’re erasing the historical record. 

Archiving and Search Are Legal 

Making material searchable is a well-established fair use. Courts have long recognized it’s often impossible to build a searchable index without making copies of the underlying material. That’s why when Google copied entire books in order to make a searchable database, courts rightly recognized it as a clear fair use. The copying served a transformative purpose: enabling discovery, research, and new insights about creative works. 

The Internet Archive operates on the same principle. Just as physical libraries preserve newspapers for future readers, the Archive preserves the web’s historical record. Researchers and journalists rely on it every day. According to Archive staff, Wikipedia alone links to more than 2.6 million news articles preserved at the Archive, spanning 249 languages. And that’s only one example. Countless bloggers, researchers, and reporters depend on the Archive as a stable, authoritative record of what was published online.

The same legal principles that protect search engines must also protect archives and libraries. Even if courts place limits on AI training, the law protecting search and web archiving is already well established.

The Internet Archive has preserved the web’s historical record for nearly thirty years. If major publishers begin blocking that mission, future researchers may find that huge portions of that historical record have simply vanished. There are real disputes over AI training that must be resolved in courts. But sacrificing the public record to fight those battles would be a profound, and possibly irreversible, mistake. 

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.

OpenAI, the maker of ChaptGPT, is rightfully facing widespread criticism for its decisions to fill the gap the U.S. Department of Defense (DoD) created when rival Anthropic refused to drop its restrictions against using its AI for surveillance and autonomous weapons systems. After protests from both users and employees who did not sign up to support government mass surveillance—early reports show that ChaptGPT uninstalls rose nearly 300% after the company announced the deal—Sam Altman, CEO of OpenAI, conceded that the initial agreement was “opportunistic and sloppy.” He then re-published an internal memo on social media stating that additions to the agreement made clear that “Consistent with applicable laws, including the Fourth Amendment to the United States Constitution, National Security Act of 1947, [and] FISA Act of 1978, the AI system shall not be intentionally used for domestic surveillance of U.S. persons and nationals.”

Trouble is, the U.S. government doesn’t believe “consistent with applicable laws” means “no domestic surveillance.” Instead, for the most part, the government has embraced a lax interpretation of “applicable law” that has blessed mass surveillance and large-scale violations of our civil liberties, and then fought tooth and nail to prevent courts from weighing in. 

"After all, many of the world’s most notorious human rights atrocities have historically been “legal” under existing laws at the time."

“Intentionally” is also doing an awful lot of work in that sentence. For years the government has insisted that the mass surveillance of U.S. persons only happens incidentally (read: not intentionally) because their communications with people both inside the United States and overseas are swept up in surveillance programs supposedly designed to only collect communications outside the United States. 

The company’s amendment to the contract continues in a similar vein, “For the avoidance of doubt, the Department understands this limitation to prohibit deliberate tracking, surveillance, or monitoring of U.S. persons or nationals, including through the procurement or use of commercially acquired personal or identifiable information.” Here, “deliberate” is the red flag given how often intelligence and law enforcement agencies rely on incidental or commercially purchased data to sidestep stronger privacy protections.

Here’s another one: “The AI System shall not be used for unconstrained monitoring of U.S. persons’ private information as consistent with these authorities. The system shall also not be used for domestic law-enforcement activities except as permitted by the Posse Comitatus Act and other applicable law.” What, one wonders, does “unconstrained” mean, precisely—and according to whom? 

Lawyers sometimes call these “weasel words” because they create ambiguity that protects one side or another from real accountability for contract violations. As with the Anthropic negotiations, where the Pentagon reportedly agreed to adhere to Anthropic’s red lines only “as appropriate,” the government is likely attempting to publicly commit to limits in principle, but retain broad flexibility in practice.

OpenAI also notes that the Pentagon promised the NSA would not be allowed to use OpenAI’s tools absent a new agreement, and that its deployment architecture will help it verify that no red lines are crossed. But secret agreements and technical assurances have never been enough to rein in surveillance agencies, and they are no substitute for strong, enforceable legal limits and transparency.

OpenAI executives may indeed be trying, as claimed, to use the company’s contractual relationship with the Pentagon to help ensure that the government should use AI tools only in a way consistent with democratic processes. But based on what we know so far, that hope seems very naïve.

Moreover, that naïvete is dangerous. In a time when governments are willing to embrace extreme and unfounded interpretations of “applicable laws,” companies need to put some actual muscle behind standing by their commitments. After all, many of the world’s most notorious human rights atrocities have historically been “legal” under existing laws at the time. OpenAI promises the public that it will  “avoid enabling uses of AI or AGI that harm humanity or unduly concentrate power,” but we know that enabling mass surveillance does both.     

OpenAI isn’t the only consumer-facing company that is, on the one hand, seeking to reassure the public that they aren’t participating in actions that violate human rights while, on the other, seeking to cash in on government mass surveillance efforts.  Despite this marketing double-speak, it is very clear that companies just cannot do both. It’s also clear that companies shouldn’t be given that much power over the limits of our privacy to begin with. The public should not have to rely on a small group of people—whether CEOs or Pentagon officials—to protect our civil liberties.

The U.S. military has officially ended its $200 million contract with AI company Anthropic and has ordered all other military contractors to cease use of their products. Why? Because of a dispute over what the government could and could not use Anthropic’s technology to do. Anthropic had made it clear since it first signed the contract with the Pentagon in 2025 that it did not want its technology to be used for mass surveillance of people in the United States or for fully autonomous weapons systems. Starting in January, that became a problem for the Department of Defense, which ordered Anthropic to give them unrestricted use of the technology. Anthropic refused, and the DoD retaliated.

There is a lot we could learn from this conflict, but the biggest take away is this: the state of your privacy is being decided by contract negotiations between giant tech companies and the U.S. government—two entities with spotty track records for caring about your civil liberties. It’s good when CEOs step up and do the right thing—but it's not a sustainable or reliable solution to build our rights on. Given the government’s loose interpretations of the law, ability to find loopholes to surveil you, and willingness to do illegal spying, we needs serious and proactive legal restrictions to prevent it from gobbling up all the personally data it can acquire and using even routine bureaucratic data for punitive ends.

Imposing and enforcing such those restrictions is properly a role for Congress and the courts, not the private sector. 

The companies know this. When speaking about the specific risk that AI poses to privacy, the CEO of Anthropic Dario Amodei said in an interview, “I actually do believe it is Congress’s job. If, for example, there are possibilities with domestic mass surveillance—the government buying of bulk data has been produced on Americans, locations, personal information, political affiliations, to build profiles, and it’s not possible to analyze all of that with AI—the fact that that is legal—that seems like the judicial interpretation of the Fourth Amendment has not caught up or the laws passed by Congress have not caught up.” 

The example he cites here is a scarily realistic one—because it’s already happening. Customs and Border Protection has tapped into the online advertising world to buy data on Americans for surveillance purposes. Immigration and Customs Enforcement has been using a tool that maps millions of peoples’ devices based on purchased cell phone data. The Office of the Director of National Intelligence has proposed a centralized data broker marketplace to make it easier for intelligence agencies to buy commercially available data. Considering the government’s massive contracts with a bunch of companies that could do analysis, including Palantir, a company which does AI-enabled analysis of huge amounts of data, then the concerns are incredibly well founded. 

But Congress is sadly neglecting its duties. For example, a bill that would close the loophole of the government buying personal information passed the House of Representatives in 2024, but the Senate stopped it.  And because Congress did not act, Americans must rely on a tech company CEO has to try to protect our privacy—or at least refuse to help the government violate it.

Privacy in the digital age should be an easy bipartisan issue. Given that it’s wildly popular (71% of American adults are concerned about the government's use of their data and among adults that have heard of AI 70% have little to no trust in how companies use those products) you would think politicians would be leaping over each other to create the best legislation and companies would be promising us the most high-end privacy protecting features. Instead, for the time being, we are largely left adrift in a sea of constant surveillance, having to paddle our own life rafts.

EFF has, and always will, fight for real and sustainable protections for our civil liberties including  a world where our privacy does not rest upon the whims of CEOs and back room deals with the surveillance state. 

The Secretary of Defense has given an ultimatum to the artificial intelligence company Anthropic in an attempt to bully them into making their technology available to the U.S. military without any restrictions for their use. Anthropic should stick by their principles and refuse to allow their technology to be used in the two ways they have publicly stated they would not support: autonomous weapons systems and surveillance. The Department of Defense has reportedly threatened to label Anthropic a “supply chain risk,” in retribution for not lifting restrictions on how their technology is used. According to WIRED, that label would be, “a scarlet letter usually reserved for companies that do business with countries scrutinized by federal agencies, like China, which means the Pentagon would not do business with firms using Anthropic’s AI in their defense work.”

Anthropic should stick by their principles and refuse to allow their technology to be used in the two ways they have publicly stated they would not support: autonomous weapons systems and surveillance.

In 2025, reportedly Anthropic became the first AI company cleared for use in relation to classified operations and to handle classified information. This current controversy, however, began in January 2026 when, through a partnership with defense contractor Palantir, Anthropic came to suspect their AI had been used during the January 3 attack on Venezuela. In January 2026, Anthropic CEO Dario Amodei wrote to reiterate that surveillance against US persons and autonomous weapons systems were two “bright red lines” not to be crossed, or at least topics that needed to be handled with “extreme care and scrutiny combined with guardrails to prevent abuses.” You can also read Anthropic’s self-proclaimed core views on AI safety here, as well as their LLM, Claude’s, constitution here. 

Now, the U.S. government is threatening to terminate the government’s contract with the company if it doesn’t switch gears and voluntarily jump right across those lines.  

Companies, especially technology companies, often fail to live up to their public statements and internal policies related to human rights and civil liberties for all sorts of reasons, including profit. Government pressure shouldn’t be one of those reasons. 

Whatever the U.S. government does to threaten Anthropic, the AI company should know that their corporate customers, the public, and the engineers who make their products are expecting them not to cave. They, and all other technology companies, would do best to refuse to become yet another tool of surveillance.

We recently introduced a policy governing large language model (LLM) assisted contributions to EFF's open-source projects. At EFF, we strive to produce high quality software tools, rather than simply generating more lines of code in less time. We now explicitly require that contributors understand the code they submit to us and that comments and documentation be authored by a human.

LLMs excel at producing code that looks mostly human generated, but can often have underlying bugs that can be replicated at scale. This makes LLM-generated code exhausting to review, especially with smaller, less resourced teams. LLMs make it easy for well-intentioned people to submit code that may suffer from hallucination, omission, exaggeration, or misrepresentation.

It is with this in mind that we introduce a new policy on submitting LLM-assisted contributions to our open-source projects. We want to ensure that our maintainers spend their time reviewing well thought out submissions. We do not completely outright ban LLMs, as their use has become so pervasive a blanket ban is impractical to enforce.

Banning a tool is against our general ethos, but this class of tools comes with an ecosystem of problems. This includes issues with code reviews turning into code refactors for our maintainers if the contributor doesn’t understand the code they submitted. Or the sheer scale of contributions that could come in as AI generated code but is only marginally useful or potentially unreviewable. By disclosing when you use LLM tools, you help us spend our time wisely.

EFF has described how extending copyright is an impractical solution to the problem of AI generated content, but it is worth mentioning that these tools raise privacy, censorship, ethical, and climatic concerns for many. These issues are largely a continuation of tech companies’ harmful practices that led us to this point. LLM generated code isn’t written on a clean slate, but born out of a climate of companies speedrunning their profits over people. We are once again in “just trust us” territory of Big Tech being obtuse about the power it wields. We are strong  advocates of using tools to innovate and come up with new ideas. However, we ask you to come to our projects knowing how to use them safely.

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 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 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.

Long before generative AI, copyright holders warned that new technologies for reading and analyzing information would destroy creativity. Internet search engines, they argued, were infringement machines—tools that copied copyrighted works at scale without permission. As they had with earlier information technologies like the photocopier and the VCR, copyright owners sued.

Courts disagreed. They recognized that copying works in order to understand, index, and locate information is a classic fair use—and a necessary condition for a free and open internet.

Today, the same argument is being recycled against AI. It’s whether copyright owners should be allowed to control how others analyze, reuse, and build on existing works.

Fair Use Protects Analysis—Even When It’s Automated

U.S. courts have long recognized that copying for purposes of analysis, indexing, and learning is a classic fair use. That principle didn’t originate with artificial intelligence. It doesn’t disappear just because the processes are performed by a machine.

Copying works in order to understand them, extract information from them, or make them searchable is transformative and lawful. That’s why search engines can index the web, libraries can make digital indexes, and researchers can analyze large collections of text and data without negotiating licenses from millions of rightsholders. These uses don’t substitute for the original works; they enable new forms of knowledge and expression.

Training AI models fits squarely within that tradition. An AI system learns by analyzing patterns across many works. The purpose of that copying is not to reproduce or replace the original texts, but to extract statistical relationships that allow the AI system to generate new outputs. That is the hallmark of a transformative use. 

Attacking AI training on copyright grounds misunderstands what’s at stake. If copyright law is expanded to require permission for analyzing or learning from existing works, the damage won’t be limited to generative AI tools. It could threaten long-standing practices in machine learning and text-and-data mining that underpin research in science, medicine, and technology. 

Researchers already rely on fair use to analyze massive datasets such as scientific literature. Requiring licenses for these uses would often be impractical or impossible, and it would advantage only the largest companies with the money to negotiate blanket deals. Fair use exists to prevent copyright from becoming a barrier to understanding the world. The law has protected learning before. It should continue to do so now, even when that learning is automated. 

A Road Forward For AI Training And Fair Use 

One court has already shown how these cases should be analyzed. In Bartz v. Anthropic, the court found that using copyrighted works to train an AI model is a highly transformative use. Training is a kind of studying how language works—not about reproducing or supplanting the original books. Any harm to the market for the original works was speculative. 

The court in Bartz rejected the idea that an AI model might infringe because, in some abstract sense, its output competes with existing works. While EFF disagrees with other parts of the decision, the court’s ruling on AI training and fair use offers a good approach. Courts should focus on whether training is transformative and non-substitutive, not on fear-based speculation about how a new tool could affect someone’s market share. 

AI Can Create Problems, But Expanding Copyright Is the Wrong Fix 

Workers’ concerns about automation and displacement are real and should not be ignored. But copyright is the wrong tool to address them. Managing economic transitions and protecting workers during turbulent times are core functions of government. Copyright law doesn’t help with those tasks in the slightest. Expanding copyright control over learning and analysis won’t stop new forms of worker automation—it never has. But it will distort copyright law and undermine free expression. 

Broad licensing mandates may also do harm by entrenching the current biggest incumbent companies. Only the largest tech firms can afford to negotiate massive licensing deals covering millions of works. Smaller developers, research teams, nonprofits, and open-source projects will all get locked out. Copyright expansion won’t restrain Big Tech—it will give it a new advantage.  

Fair Use Still Matters

Learning from prior work is foundational to free expression. Rightsholders cannot be allowed to control it. Courts have rejected that move before, and they should do so again.

Search, indexing, and analysis didn’t destroy creativity. Nor did the photocopier, nor the VCR. They expanded speech, access to knowledge, and participation in culture. Artificial intelligence raises hard new questions, but fair use remains the right starting point for thinking about training.

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.

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.