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.

There’s a crisis of creativity in mainstream American culture. We have fewer and fewer studios and record labels and fewer and fewer platforms online that serve independent artists and creators.  

At its core, copyright is a monopoly right on creative output and expression. It’s intended to allow people who make things to make a living through those things, to incentivize creativity. To square the circle that is “exclusive control over expression” and “free speech,” we have fair use.

However, we aren’t just seeing artists having a time-limited ability to make money off of their creations. We are also seeing large corporations turn into megacorporations and consolidating huge stores of copyrights under one umbrella. When the monopoly right granted by copyright is compounded by the speed and scale of media company mergers, we end up with a crisis in creativity. 

People have been complaining about the lack of originality in Hollywood for a long time. What is interesting is that the response from the major studios has rarely, especially recently, to invest in original programming. Instead, they have increased their copyright holdings through mergers and acquisitions. In today’s consolidated media world, copyright is doing the opposite of its intended purpose: instead of encouraging creativity, it’s discouraging it. The drive to snap up media franchises (or “intellectual properties”) that can generate sequels, reboots, spinoffs, and series for years to come has crowded out truly original and fresh creativity in many sectors. And since copyright terms last so long, there isn’t even a ticking clock to force these corporations to seek out new original creations. 

In theory, the internet should provide a counterweight to this problem by lowering barriers to entry for independent creators. But as online platforms for creativity likewise shrink in number and grow in scale, they have closed ranks with the major studios.  

It’s a betrayal of the promise of the internet: that it should be a level playing field where you get to decide what you want to do, watch, listen to, read. And our government should be ashamed for letting it happen.  

This year, we fought back against the return of a terrible idea that hasn’t improved with age: site blocking laws. 

More than a decade ago, Congress tried to pass SOPA and PIPA—two sweeping bills that would have allowed the government and copyright holders to quickly shut down entire websites based on allegations of piracy. The backlash was massive. Internet users, free speech advocates, and tech companies flooded lawmakers with protests, culminating in an “Internet Blackout” on January 18, 2012. Turns out, Americans don’t like government-run internet blacklists. The bills were ultimately shelved.  

But we’ve never believed they were gone for good. The major media and entertainment companies that backed site blocking in the US in 2012 turned to pushing for site-blocking laws in other countries. Rightsholders continued to ask US courts for site-blocking orders, often winning them without a new law. And sure enough, the Motion Picture Association (MPA) and its allies have asked Congress to try again. 

There were no less than three Congressional drafts of site-blocking legislation. Representative Zoe Lofgren kicked off the year with the Foreign Anti-Digital Piracy Act (FADPA). Fellow House of Representatives member Darrell Issa also claimed to be working on a bill that would make it offensively easy for a studio to block your access to a website based solely on the belief that there is infringement happening. Not to be left out, the Senate Judiciary Committee produced the terribly named Block BEARD Act.  

None of these three attempts to fundamentally alter the way you experience the internet moved too far after their press releases. But the number tells us that there is, once again, an appetite among major media conglomerates and politicians to resurrect SOPA/PIPA from the dead.  

None of these proposals fixes the flaws of SOPA/PIPA, and none ever could. Site blocking is a flawed idea and a disaster for free expression that no amount of rewriting will fix. There is no way to create a fast lane for removing your access to a website that is not a major threat to the open web. Just as we opposed SOPA/PIPA over ten years ago, we oppose these efforts.  

Site blocking bills seek to build a new infrastructure of censorship into the heart of the internet. They would enable court orders directed to the organizations that make the internet work, like internet service providers, domain name resolvers, and reverse proxy services, compelling them to help block US internet users from visiting websites accused of copyright infringement. The technical means haven’t changed much since 2012. - tThey involve blocking Internet Protocol addresses or domain names of websites. These methods are blunt—sledgehammers rather than scalpels. Today, many websites are hosted on cloud infrastructure or use shared IP addresses. Blocking one target can mean blocking thousands of unrelated sites. That kind of digital collateral damage has already happened in Austria, Italy, South Korea, France, and in the US, to name just a few.  

Given this downside, one would think the benefits of copyright enforcement from these bills ought to be significant. But site blocking is trivially easy to evade. Determined site owners can create the same content on a new domain within hours. Users who want to see blocked content can fire up a VPN or change a single DNS setting to get back online.  

The limits that lawmakers have proposed to put on these laws are an illusion. While ostensibly aimed at “foreign” websites, they sweep in any website that doesn’t conspicuously display a US origin, putting anonymity at risk. And despite the rhetoric of MPA and others that new laws would be used only by responsible companies against the largest criminal syndicates, laws don’t work that way. Massive new censorship powers invite abuse by opportunists large and small, and the costs to the economy, security, and free expression are widely borne. 

It’s time for Big Media and its friends in Congress to drop this flawed idea. But as long as they keep bringing it up, we’ll keep on rallying internet users of all stripes to fight it. 

This article is part of our Year in Review series. Read other articles about the fight for digital rights in 2025.

The state of streaming is... bad. It’s very bad. The first step in wanting to watch anything is a web search: “Where can I stream X?” Then you have to scroll past an AI summary with no answers, and then scroll past the sponsored links. After that, you find out that the thing you want to watch was made by a studio that doesn’t exist anymore or doesn’t have a streaming service. So, even though you subscribe to more streaming services than you could actually name, you will have to buy a digital copy to watch. A copy that, despite paying for it specifically, you do not actually own and might vanish in a few years. 

Then, after you paid to see something multiple times in multiple ways (theater ticket, VHS tape, DVD, etc.), the mega-corporations behind this nightmare will try to get Congress to pass laws to ensure you keep paying them. In the end, this is easier than making a product that works. Or, as someone put it on social media, these companies have forgotten “that their entire existence relies on being slightly more convenient than piracy.” 

It’s important to recognize this as we see more and more media mergers. These mergers are not about quality, they’re about control. 

In the old days, studios made a TV show. If the show was a hit, they increased how much they charged companies to place ads during the show. And if the show was a hit for long enough, they sold syndication rights to another channel. Then people could discover the show again, and maybe come back to watch it air live. In that model, the goal was to spread access to a program as much as possible to increase viewership and the number of revenue streams.  

Now, in the digital age, studios have picked up a Silicon Valley trait: putting all their eggs into the basket of “increasing the number of users.” To do that, they have to create scarcity. There has to be only one destination for the thing you’re looking for, and it has to be their own. And you shouldn’t be able to control the experience at all. They should.  

They’ve also moved away from creating buzzy new exclusives to get you to pay them. That requires risk and also, you know, paying creative people to make them. Instead, they’re consolidating.  

Media companies keep announcing mergers and acquisitions. They’ve been doing it for a long time, but it’s really ramped up in the last few years. And these mergers are bad for all the obvious reasons. There are the speech and censorship reasons that came to a head in, of all places, late night television. There are the labor issues. There are the concentration of power issues. There are the obvious problems that the fewer studios that exist the fewer chances good art gets to escape Hollywood and make it to our eyes and ears. But when it comes specifically to digital life there are these: consumer experience and ownership.  

First, the more content that comes under a single corporation’s control, the more they expect you to come to them for it. And the more they want to charge. And because there is less competition, the less they need to work to make their streaming app usable. They then enforce their hegemony by using the draconian copyright restrictions they’ve lobbied for to cripple smaller competitors, critics, and fair use.  

When everything is either Disney or NBCUniversal or Warner Brothers-Discovery-Paramount-CBS and everything is totally siloed, what need will they have to spend money improving any part of their product? Making things is hard, stopping others from proving how bad you are is easy, thanks to how broken copyright law is.  

Furthermore, because every company is chasing increasing subscriber numbers instead of multiple revenue streams, they have an interest in preventing you from ever again “owning” a copy of a work. This was always sort of part of the business plan, but it was on a scale of a) once every couple of years,  b) at least it came, in theory, with some new features or enhanced quality and c) you actually owned the copy you paid for. Now they want you to pay them every month for access to same copy. And, hey, the price is going to keep going up the fewer options you have. Or you will see more ads. Or start seeing ads where there weren’t any before.  

On the one hand, the increasing dependence on direct subscriber numbers does give users back some power. Jimmy Kimmel’s reinstatement by ABC was partly due to the fact that the company was about to announce a price hike for Disney+ and it couldn’t handle losing users due to the new price and due to popular outrage over Kimmel’s treatment.  

On the other hand, well, there's everything else. 

The latest kerfuffle is over the sale of Warner Brothers-Discovery, a company that was already the subject of a sale and merger resulting in the hyphen. Netflix was competiing against another recently merged media megazord of Paramount Skydance.  

Warner Brothers-Discovery accepted a bid from Netflix, enraging Paramount Skydance, which has now launched a hostile takeover.  

Now the optimum outcome is for neither of these takeovers to happen. There are already too few players in Hollywood. It does nothing for the health of the industry to allow either merger. A functioning antitrust regime would stop both the sale and the hostile takeover attempt, full stop. But Hollywood and the federal government are frequent collaborators, and the feds have little incentive to stop Hollywood’s behemoths from growing even further, as long as they continue to play their role pushing a specific view of American culture.    

The promise of the digital era was in part convenience. You never again had to look at TV listings to find out when something would be airing. Virtually unlimited digital storage meant everything would be at your fingertips. But then the corporations went to work to make sure it never happened. And with each and every merger, that promise gets further and further away.  

Note 12/10/2025: One line in this blog has been modified a few hours post-publication. The substance remains the same. 

As all things old are new again, a bill that would make obtaining bad patents easier and harder to challenge is being considered in the Senate Judiciary Committee. The Patent Eligibility Restoration Act (PERA) would reverse over a decade of progress in fighting patent trolls and making the patent system more balanced.

PERA would overturn long-standing court decisions that have helped keep some of the most problematic patents in check. This includes the Supreme Court’s Alice v. CLS Bank decision, which bars patents on abstract ideas. While Alice has not completely solved the problems of the patent system or patent trolling, it has led to the rejection of hundreds of low-quality software patents and, as a result, has allowed innovation and small businesses to grow.

Thanks to the Alice decision, courts have invalidated a rogue’s gallery of terrible software patents—such as patents on online photo contests, online bingo, upselling, matchmaking, and scavenger hunts. These patents didn’t describe real inventions—they merely applied old ideas to general-purpose computers. But PERA would wipe out the Alice framework and replace it with vague, hollow exceptions, taking us back to an era where patent trolls and large corporate patent-holders aggressively harassed software developers and small companies.

This bill, combined with recent changes that have restricted access to the Patent Trial and Appeal Board (PTAB), would create a perfect storm—giving patent trolls and major corporations with large patent portfolios free rein to squeeze out independent inventors and small businesses.

EFF is proud to join a letter, along with Engine, the Public Interest Patent Law Institute, Public Knowledge, and R Street, to the Senate Judiciary Committee opposing this poorly-timed and concerning bill. We urge the committee to instead focus on restoring the PTAB as the accessible, efficient check on patent quality that Congress intended.

There is a lot of bad on the internet and it seems to only be getting worse. But one of the things the internet did well, and is worth preserving, is nontraditional paths for creativity, journalism, and criticism. As governments and major corporations throw up more barriers to expression—and more and more gatekeepers try to control the internet—it’s important to learn how to crash through those gates. 

In EFF's interview series, Gate Crashing, we talk to people who have used the internet to take nontraditional paths to the very traditional worlds of journalism, creativity, and criticism. We hope it's both inspiring to see these people and enlightening for anyone trying to find voices they like online.  

Our mini-series will be dropping an episode each month closing out 2025 in style.

• Episode 1: Fanfiction Becomes Mainstream – Launching October 1*
• Episode 2: From DIY to Publishing – Launching November 1
• Episode 3: A New Path for Journalism – Launching December 1

Be sure to mark your calendar or check our socials on drop dates. If you have a friend or colleague that might be interested in watching our series, please forward this link: eff.org/gatecrashing

Check Out Episode 1

For over 35 years, EFF members have empowered attorneys, activists, and technologists to defend civil liberties and human rights online for everyone.

Tech should be a tool for the people, and we need you in this fight.

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Jimmy Kimmel has been in the news a lot recently, which means the ongoing lawsuit against him by perennial late-night punching bag/convicted fraudster/former congressman George Santos flew under the radar. But what happened in that case is an essential illustration of the limits of both copyright law and the “fine print” terms of service on websites and apps. 

What happened was this: Kimmel and his staff saw that Santos was on Cameo, which allows people to purchase short videos from various public figures with requested language. Usually it’s something like “happy birthday” or “happy retirement.” In the case of Kimmel and his writers, they set out to see if there was anything they couldn’t get Santos to say on Cameo. For this to work, they obviously didn’t disclose that it was Jimmy Kimmel Live! asking for the videos.  

Santos did not like the segment, which aired clips of these videos, called “Will Santos Say It?”.  He sued Kimmel, ABC, and ABC’s parent company, Disney. He alleged both copyright infringement and breach of contract—the contract in this case being Cameo’s terms of service. He lost on all counts, twice: his case was dismissed at the district court level, and then that dismissal was upheld by an appeals court. 

On the copyright claim, Kimmel and Disney argued and won on the grounds of fair use. The court cited precedent that fair use excuses what might be strictly seen as infringement if such a finding would “stifle the very creativity” that copyright is meant to promote. In this case, the use of the videos was part of the ongoing commentary by Jimmy Kimmel Live! around whether there was anything Santos wouldn’t say for money. Santos tried to argue that since this was their purpose from the outset, the use wasn’t transformative. Which... isn’t how it works. Santos’ purpose was, presumably, to fulfill a request sent through the app. The show’s purpose was to collect enough examples of a behavior to show a pattern and comment on it.  

Santos tried to say that their not disclosing what the reason was invalidated the fair use argument because it was “deceptive.” But the court found that the record didn’t show that the deception was designed to replace the market for Santos’s Cameos. It bears repeating: commenting on the quality of a product or the person making it is not legally actionable interference with a business. If someone tells you that a movie, book, or, yes, Cameo isn’t worth anything because of its ubiquity or quality and shows you examples, that’s not a deceptive business practice. In fact, undercover quality checks and reviews are fairly standard practices! Is this a funnier and more entertaining example than a restaurant review? Yes. That doesn’t make it unprotected by fair use.  

It’s nice to have this case as a reminder that, despite everything, the major studios often argue, fair use protects everyone, including them. Don’t hold your breath on them remembering this the next time someone tries to make a YouTube review of a Hollywood movie using clips.  

Another claim from this case that is less obvious but just as important involves the Cameo terms of service. We often see contracts being used to restrict people’s fair use rights. Cameo offers different kinds of videos for purchase. The most well-known comes with a personal use license, the “happy birthdays,” and so on. They also offer a “commercial” use license, presumably if you want to use the videos to generate revenue, like you do with an ad or paid endorsement. However, in this case, the court found that the terms of service are a contract between a customer and Cameo, not between the customer and the video maker. Cameo’s terms of service explicitly lay out when their terms apply to the person selling a video, and they don’t create a situation where Santos can use those terms to sue Jimmy Kimmel Live! According to the court, the terms don’t even imply a shared understanding and contract between the two parties.  

It's so rare to find a situation where the wall of text that most terms of service consist of actually helps protect free expression; it’s a pleasant surprise to see it here.  

In general, we at EFF hate it when these kinds of contracts—you know the ones, where you hit accept after scrolling for ages just so you can use the app—are used to constrain users’ rights. Fair use is supposed to protect us all from overly strict interpretations of copyright law, but abusive terms of service can erode those rights. We’ll keep fighting for those rights and the people who use them, even if the one exercising fair use is Disney.