The Litigation Era of AI

The pace of AI innovation has been staggering, but an equally rapid rise in litigation is now challenging how AI companies operate. These lawsuits go far beyond the familiar copyright disputes over training data. From privacy and biometric surveillance to algorithmic bias, product safety, and even defamation, recent cases reveal legal risks at the core of how AI systems collect data, make decisions, and impact people’s lives. This unfolding legal battle carries enormous implications for AI developers, users, and investors alike. In a journalistic deep dive, we explore the headline cases and what they mean for the industry – with a human touch, because this story is ultimately about people, trust, and the high stakes of getting AI wrong.

The Data Privacy Showdown: Biometric Scans and Secret Scraping

In March 2025, controversial facial-recognition startup Clearview AI finally buckled under legal pressure – but with a twist. After years of outrage over its practices, Clearview agreed to a $50 million settlement in a class-action lawsuit accusing it of scraping billions of people’s photos from sites like Facebook and YouTube without consent. The case was filed under Illinois’ Biometric Information Privacy Act (BIPA), a strict state law that lets individuals sue companies for mishandling biometric data. What made headlines was not just the payout, but how it was structured: instead of cash up front, Clearview’s victims get a 23% stake in the company’s future value. In other words, the people whose faces were taken without permission now literally own a piece of Clearview. Observers called it a “novel” deal – and a sign that courts see biometric data itself as an asset with long-term value. If your AI business treats personal data as loot, expect that data to become leverage against you in court.

Meta (formerly Facebook) learned a similarly costly lesson about biometric data. In July 2024, Meta struck a record $1.4 billion settlement with the state of Texas over its old “Tag Suggestions” feature, which had scanned users’ photos for faces without explicit consent. Notably, this was not a class action – it was Texas’s Attorney General suing on behalf of the state, and it became the largest privacy payout ever obtained by a single U.S. state. Texas argued that Meta’s automatic face-tagging violated a 2009 state law against capturing biometric identifiers without consent. Meta, which had already shut down its face-recognition system amid backlash, paid up and promised to “hold itself accountable” – all while denying wrongdoing, of course. The sheer size of the fine sent a loud message to the tech industry: if you use people’s biometric data without asking, a single state regulator can hit you with a billion-dollar bill. And Texas wasn’t done yet.

In May 2025, Texas hit Google with an almost identical price tag – a $1.375 billion settlement over two lawsuits claiming the company secretly tracked users and captured their biometric data. One lawsuit accused Google of storing Texans’ voiceprints and face geometry without consent, and another said it misled users about location tracking. Texas’ firebrand AG Ken Paxton crowed that “in Texas, Big Tech is not above the law,” touting the deal as a “historic win” for privacy. Google, for its part, noted it had “long since changed” the practices in question – effectively admitting the claims had merit while avoiding any official admission of guilt. The Google and Meta cases together signal a trend: in the U.S., where federal privacy rules are patchy, individual states are stepping in to police AI and data practices. For AI companies, that means compliance isn’t just a Washington, D.C. game; it’s a state-by-state minefield with potentially billion-dollar consequences.

It’s not only government watchdogs. Private class actions are homing in on companies’ AI-fueled data grabs. In Illinois (home of BIPA), Amazon faces a lawsuit claiming its photo-storage service harvested users’ face data to train its Rekognition facial recognition software – again, without proper consent. According to the complaint, Amazon Photos would automatically scan faces in images uploaded by users, then use those scans to improve Rekognition, which Amazon licenses out to clients, including law enforcement. Essentially, the lawsuit says Amazon treated its customers’ personal photos as free AI training fodder. Amazon denies wrongdoing, but if Illinois courts side with the plaintiffs, the case could set a precedent that any user-generated content used to train AI needs explicit permission. Given BIPA’s stiff penalties (up to $5,000 per violation), an unfavorable ruling could cost Amazon heavily – and send shivers through any AI outfit relying on user data streams.

Even voice data – an often overlooked biometric – is under scrutiny. In Delgado v. Meta, a pending case, an Illinois woman alleges Facebook and Messenger illegally collected “voiceprints” from audio messages without consent. She says Meta’s apps analyzed users’ voices to create unique voice identifiers (a digital fingerprint of your voice) in violation of BIPA. Meta tried to get the case tossed or moved under California law (since its user terms choose California), but a judge ruled that Illinois’ strong biometric law still applies. That victory for the plaintiff kept the case alive and signaled that big platforms can’t escape Illinois’ privacy protections just by citing their Terms of Service. Recently, a federal judge even gave Meta permission to seek an early summary judgment – essentially asking to throw the case out for lack of evidence – but regardless of the outcome, the case is a wake-up call for anyone working with voice AI. The legal system is saying: voice data is biometric data too, and it had better be handled with care.

One particularly eye-opening case didn’t involve faces or voices at all, but private messages. In late 2024, Microsoft-owned LinkedIn quietly added a new setting that automatically opted users into sharing their communications for “AI research”. InMail messages – those private notes sent between LinkedIn users – were allegedly scooped up and shared with third parties (including, unsurprisingly, Microsoft itself) to help train generative AI models. The change went almost unnoticed until a class-action lawsuit early in 2025 accused LinkedIn of effectively harvesting confidential chats without consent. The complaint painted a vivid picture: job seekers’ messages about offers or negotiations could end up regurgitated by AI systems; sensitive business data from private chats could surface in Microsoft’s AI products. LinkedIn was even accused of trying to cover its tracks by updating its privacy fine print only after media reports called out the practice. Interestingly – and somewhat mysteriously – the plaintiff dropped the lawsuit just days after filing. We don’t know why (settlement? second thoughts?), but the short-lived case still sent ripples through the industry. It expanded the conversation beyond biometrics: any personal data, even messages or documents used in model training, can trigger legal exposure if collected improperly. For AI companies hungry for real-world data, it’s a cautionary tale that boils down to a simple principle: ask permission or expect a fight.

Bias and Discrimination: When Algorithms Break Civil Rights Laws

AI’s power to make decisions at scale – who gets hired, who gets a loan, how much you pay for insurance – has raised a thorny question: what happens when the algorithms get it wrong in biased ways? We’re now seeing the answer in court. Even if AI bias isn’t intentional, companies can be held to account under longstanding anti-discrimination laws. In effect, the centuries-old civil rights framework is being applied to 21st-century tech, and it’s catching some firms off guard.

One landmark case comes from Colorado, where in March 2025 civil rights groups (including the ACLU) filed a complaint against software giant Intuit and its AI hiring vendor, HireVue. The story is painfully illustrative: A Native American woman, referred to as D.K., who is Deaf, applied for an internal promotion at Intuit and was subjected to an automated video interview run by HireVue’s AI. The AI was supposed to assess her speech and demeanor, but because D.K. signs and speaks with a Deaf accent, the system apparently penalized her for not sounding like a typical hearing person. She didn’t get the promotion. According to the complaint, the AI wasn’t trained to accommodate Deaf or Indigenous speech patterns, so it simply gave her a low communication score – in essence, downgrading her because of her disability and background. The legal filing argues this violated the Americans with Disabilities Act, Title VII of the Civil Rights Act (which prohibits race/national origin bias), and Colorado’s anti-discrimination laws. In D.K.’s words, “companies like Intuit and HireVue must be held accountable for deploying flawed and exclusionary technology” that creates “artificial barriers” for qualified people. If the regulators (the EEOC and Colorado civil rights division) or a future lawsuit agree, it could force a sea change in AI hiring tools. The case puts AI vendors on notice that selling “black box” hiring algorithms that haven’t been vetted for bias is not just a bad PR move – it might be straight-up illegal.

Meanwhile, in Illinois, another discrimination battlefront is insurance. In a still-unfolding case filed in 2022, two Black homeowners accused State Farm of systematically using AI to discriminate against Black policyholders. The plaintiffs, Jacqueline Huskey and Riian Wynn, say that after a hailstorm, their home insurance claims were dragged out for months and subjected to extra scrutiny and paperwork, while white neighbors with similar damage had claims paid faster. Why the difference? The lawsuit points to State Farm’s algorithmic claims-processing system. The company uses AI models to predict fraud and decide which claims to pay immediately and which to vet more closely. The plaintiffs allege that whatever data or criteria the AI was using, it disproportionately flagged claims in Black neighborhoods for suspicion, leading to delayed repairs and further property damage when fixes were postponed. In essence, they argue the AI learned to redline – reviving the ugly old practice of treating customers differently by race, even if no human deliberately programmed it to do so. State Farm strenuously denies any bias, but notably, a federal judge refused to dismiss the case in 2023, allowing it to proceed into discovery. The suit could even balloon into a class action covering thousands of policyholders across multiple Midwestern states. This is breaking new ground: it’s possibly the first time a machine-learning algorithm’s unintended discrimination is being tested under laws like the Fair Housing Act (cited here because it covers insurance discrimination in housing). For the insurance and finance sectors, a verdict against State Farm would be a legal earthquake, proving that “the algorithm did it” is no defense if the outcome is a civil rights violation.

These cases underscore a pivotal point for the industry: You are responsible for what your AI does, even if its biases were inherited from historical data or are unintentional. Anti-discrimination laws from the 1960s and 70s are proving quite adaptable to the AI age. Companies can’t hide behind complex models – if an algorithm denies opportunities or benefits in a way that systematically disadvantages protected groups, expect lawsuits and expect to lose. In some places, regulators aren’t waiting. For example, New York City has already enacted a law requiring bias audits for AI hiring tools, and Colorado passed a law to curb algorithmic bias in insurance decisions. While Europe’s forthcoming AI Act explicitly demands that “high-risk” AI systems be free from discrimination, the U.S. lacks a comparable federal law. But as we see, creative lawyers and aggressive state enforcers are using existing statutes to fill the gap. The takeaway for AI companies? What you don’t know can hurt you. Rigorous testing for bias and fairness isn’t just an ethical nice-to-have – it’s quickly becoming a legal must-have if you want to avoid multimillion-dollar litigation.

When AI Goes Rogue: Denied Care and Deadly Mishaps

Some of the most heart-wrenching AI lawsuits involve scenarios where automated systems make life-and-death decisions – and get them horribly wrong. It’s one thing for AI to recommend the wrong song; it’s another when AI denies a cancer treatment or crashes a car. These high-stakes failures are now translating into high-stakes lawsuits, raising profound questions about accountability and safety.

Take healthcare. By early 2025, three of America’s biggest health insurance companies – Cigna, Humana, and UnitedHealth Group – had all been slapped with class-action lawsuits accusing them of using AI algorithms to wrongfully deny medical claims. To their patients, these denials weren’t an abstract data issue; they meant real people losing coverage for surgeries, hospital stays, and vital medications. One lawsuit against Cigna (exposed by reporting in 2023) revealed an almost dystopian process: Cigna doctors were allegedly rubber-stamping hundreds of thousands of denials triggered by an AI system, spending an average of just 1.2 seconds reviewing each case. In a mere two months, over 300,000 claims were rejected by this method – often without any human truly looking at the patient’s file. The AI, a system called “PXDX,” would scan if a claim’s diagnosis code matched its criteria and automatically spit out denials, which physicians then perfunctorily approved. “It was essentially a scheme to avoid paying for needed care,” the lawsuit alleges. Cigna insists it was just automating routine paperwork for minor claims, but to one patient who got a surprise bill for a necessary test, that distinction is cold comfort. The case is now working through federal court, and a judge has allowed it to proceed despite Cigna’s attempts to dismiss it . If a jury finds that Cigna’s algorithmic denials violated its legal duty to conduct “thorough, fair, and objective” claim reviews, the insurer could face massive damages – and the entire industry will have to rethink these tools.

Perhaps even more striking is a lawsuit targeting UnitedHealthcare’s use of an AI known as “nH Predict.” UnitedHealth (the largest U.S. insurer) and a subsidiary called NaviHealth deployed nH Predict to decide how long patients should stay in rehab or nursing facilities after hospital discharge. According to a class-action on behalf of patients, this AI often cut off coverage for care too soon, on the premise that the patient was “predicted” to recover quickly. In one example, an 80-year-old patient recovering from surgery was denied a short stay in a skilled nursing facility because nH Predict algorithmically determined she didn’t need it – a decision later overturned by human appeal, but only after painful delays. The kicker: the lawsuit cites evidence that nH Predict’s decisions were wrong a staggering 90% of the time – nine out of ten denials were reversed when patients appealed. Yet because less than 1% of patients actually go through the arduous appeal process, most just accepted the denials. In essence, the suit claims UnitedHealth knowingly let a flawed AI deny needed therapy for thousands of people, betting (correctly) that almost nobody would challenge it. UnitedHealth denies that it uses the tool to make final coverage calls, but in February 2025, a judge ruled that the core claims – including breach of fiduciary duty under insurance law – can move forward. Lawmakers are taking notice, too. After these allegations emerged, members of Congress called for a federal investigation into AI-driven health denials, and some states are moving to tighten rules around prior authorization algorithms. The broader implication is chilling: if insurers lean too heavily on AI to cut costs at patients’ expense, they may not only harm patients but also violate contracts, insurance regulations, and medical ethics – a trifecta of legal troubles.

From hospital beds to highways: AI has also been blamed for deadly accidents. Tesla, a company as much in the AI business as in the car business, has been fighting a wave of lawsuits over crashes involving its Autopilot driver-assistance system. In one ongoing case in California, the family of 31-year-old Genesis Mendoza-Martinez sued Tesla after he died in a horrific February 2023 wreck. Genesis was driving his Model S on I-680 near San Francisco with Autopilot engaged when the car failed to recognize a fire truck parked across the highway and plowed straight into it at 70 mph. He was killed instantly. His family’s lawsuit accuses Tesla of fraudulent misrepresentation, arguing that Elon Musk and the company had wildly exaggerated Autopilot’s capabilities in marketing and public statements. They point to the very name “Autopilot” – and Musk’s boasts over the years that Teslas would soon be self-driving – as misleading assurances that lulled Genesis (and many others) into a false sense of security. Tesla’s defense is that drivers are warned to stay alert and keep hands on the wheel, and that Genesis himself was tragically negligent to trust the system completely. In May 2025, a judge in the case delivered a split decision: he dismissed one claim that Tesla had an outright duty to disclose Autopilot’s limits, but he allowed the core misrepresentation claims to proceed, remarking that calling a semi-assisted driving feature “Autopilot” is “plausibly misleading”. Internal Tesla emails, revealed in court, showed even some Tesla employees worried the Autopilot name would make drivers overestimate the tech’s abilities. Those concerns were echoed by German regulators years ago, who formally asked Tesla to rename Autopilot to something less misleading. Tesla refused – and now that stubborn branding might come back to bite it. The Mendoza-Martinez case isn’t the only one; in Florida, another Autopilot crash led to a jury verdict of $243 million against Tesla in late 2023, although Tesla was found only partly liable and is fighting the punitive damages as excessive. All these lawsuits collectively raise the pressure on Tesla (and the industry) to be far more transparent about what “self-driving” tech can and cannot do. And they raise a broader legal question: when advanced AI-driven products fail, how do we assign blame between human users and the companies that marketed them? Courts are now grappling with that, and the outcomes will likely shape product liability law for the AI era.

Defamation and Data Heists: AI’s Uncharted Legal Territory

Even as courts apply established laws to AI in areas like privacy and product safety, entirely new legal frontiers are emerging. Two of the most intriguing: AI-generated defamation and “prompt injection” hacks. These sound like sci-fi hypotheticals, but they’re real – and the decisions could redefine legal responsibilities for AI companies and their adversaries.

In 2023, a radio host in Georgia named Mark Walters found himself at the center of what’s believed to be the first libel suit involving generative AI. Walters, a gun-rights advocate, wasn’t accused of defamation – he was the victim of it, courtesy of OpenAI’s ChatGPT. A journalist had asked ChatGPT to summarize a (completely unrelated) court case, and the AI hallucinated wildly, outputting a fake summary that claimed Walters was a defendant in a fraud lawsuit and had been accused of embezzling funds from a Second Amendment group. None of it was true; ChatGPT seemingly stitched together bits of context and invented a case out of thin air. Walters was never involved in any such litigation. Understandably outraged, he sued OpenAI in June 2023 for defamation, arguing that the company should be held liable for publishing false and damaging statements about him. This raised a mind-bending question: Who is the “speaker” when an AI makes something up? OpenAI’s defense was that ChatGPT is a tool that users operate, more like a search engine or library, not a publisher with editorial control. And indeed, internet platforms usually have broad immunity (under Section 230 of the Communications Decency Act) for content posted by users – but in this case, the “user” (the journalist) didn’t input the false info, the AI did. Legal scholars began debating whether Section 230 would protect an AI company for its algorithm’s speech. Walters’ case never got to settle that question, because in May 2025, a Georgia judge dismissed it on other grounds. The court found Walters hadn’t proven the essential element of “actual malice” – basically, that OpenAI knowingly or recklessly let the falsehood out. The judge even cited OpenAI’s extensive warnings about ChatGPT’s tendency to err as evidence that the company was trying to prevent harm, not acting negligently. OpenAI understandably hailed the ruling. But the broader issue is far from settled. Had Walters been an average private individual (not a semi-public figure) or had OpenAI been less proactive in warning about errors, the outcome might have differed. The case underscored a critical risk of generative AI: so-called “hallucinations” can ruin reputations and spread lies on a mass scale. If those lies injure someone, AI firms may find themselves hauled into court just like any newspaper that printed a defamatory story. More such suits are likely – we’ve already heard of an Australian mayor considering suing OpenAI for a similar false accusation. So far, no firm precedents have been set, but executives should heed the writing on the wall: truthfulness is not just an AI ethics issue; it’s a legal liability issue. Rigorous model tuning and user warnings are not just good practice – they might save you from paying damages or getting regulated as a publisher.

Another novel battleground is emerging around the concept of prompt injection – essentially, hacking an AI through cleverly crafted inputs. In early 2025, a startup called OpenEvidence (which offers a medical AI chatbot) sued a competitor, Pathway Medical, in federal court, accusing it of a corporate espionage caper fit for a thriller. According to the complaint, Pathway’s co-founder posed as a doctor to gain access to OpenEvidence’s system (which is normally limited to licensed professionals), then bombarded the chatbot with special prompts designed to trick it into revealing its hidden instructions and data. In other words, a prompt injection attack. OpenEvidence claims that the confidential “system prompts” and other under-the-hood details that Pathway extracted are its trade secrets, and Pathway stole them in violation of the Defend Trade Secrets Act and other laws. If this sounds esoteric, consider the implications: AI models often operate with non-public parameters, fine-tuned datasets, or secret sauce instructions that give them an edge. If a rival can reverse-engineer those via clever querying, is it akin to stealing source code or proprietary formulas? OpenEvidence is essentially saying yes – that their “crown jewel” system prompt (the hidden directive guiding the AI’s answers) has economic value and was protected, and Pathway’s actions were no different than hacking into a database. Pathway will likely argue that sending prompts to an open web service isn’t “improper means” – maybe even framing it as legitimate competitive intelligence. How the court lands could set a major precedent for AI security. If OpenEvidence wins, AI companies will have stronger footing to go after those who manipulate their models or scrape their outputs to clone functionality. It would be a signal that the law is willing to protect not just the code and data input to AI, but even the behavior of the model as a secret asset. On the flip side, a Pathway victory might suggest that once an AI is publicly accessible, others are free to test and probe it – even if that reveals secrets – much as they are allowed to reverse-engineer a competitor’s physical product. The stakes are high: trade secret cases can lead to injunctions and hefty damages. In the meantime, the smart play for AI firms is obvious – tighten your system security and maybe don’t rely on obscurity alone to protect your IP. Because if someone can jailbreak your AI, someone will.

The Road Ahead: Regulation or Litigation – or Both?

These diverse lawsuits – spanning privacy, bias, health, safety, and beyond – collectively point to an industry at a crossroads. AI companies have enjoyed a period of relative Wild West growth, largely unburdened by specific regulations. That era is closing. Courts and regulators are now drawing lines, often after harms have occurred. For executives and investors, the question is how to navigate this new landscape of risk.

One theme is clear: existing laws are being applied aggressively to AI. Companies might have assumed that in the absence of AI-specific regulations, they had carte blanche. But as we’ve seen, data protection laws (like Illinois’ BIPA or Texas’s biometric and privacy statutes) are readily wielded against AI misuse. Long-standing civil rights laws can anchor lawsuits over algorithmic discrimination. Product liability and consumer protection laws provide a basis to sue over dangerous or deceptive AI products. And where laws haven’t kept up – say, dealing with AI libel or data-poaching hacks – plaintiffs are testing novel legal theories in court. This means AI businesses must proactively assess how the law of the land (in every jurisdiction they operate in) might apply to their tech. “We didn’t realize our model could do that” won’t cut it as an excuse. The companies best positioned to thrive are those baking compliance and risk mitigation into their AI development process now – not after the subpoena arrives.

That said, there’s an increasing drumbeat for new, AI-specific legislation, especially in the U.S., where a comprehensive framework is conspicuously absent. The European Union is in the final stages of adopting its AI Act, which will impose sweeping requirements on AI systems, from transparency to bias testing, with hefty fines for violations. By contrast, the United States has so far taken a piecemeal approach. There’s no federal AI law ensuring, for instance, that AI decisions in hiring or lending are audited for fairness – leaving that to scattered state initiatives (like the ones in New York and Colorado). Privacy at the federal level is still the Wild West: no nationwide law like Europe’s GDPR exists, meaning issues like consent for data usage fall to a patchwork of state laws. Recognizing the gap, the Biden administration has tried to guide policy through non-binding means – releasing an “AI Bill of Rights” blueprint and, in late 2023, issuing an Executive Order on AI safety. That executive order (the first of its kind in the US) pushed for the development of AI safety standards and addressed issues like watermarking AI-generated content and evaluating AI systems used by the government for biases. It even referenced the need to protect against algorithmic discrimination in areas like housing and credit. But an executive order is limited; it’s not the same as Congress passing enforceable rules. So far, proposed AI bills in Congress have ranged from creating licensing for advanced AI models to carving out liability exemptions, but none have crossed the finish line. The upshot is that, at least for the next couple of years, the “regulation” of AI in the U.S. will likely happen through litigation and state action, not comprehensive federal statutes.

For industry leaders, this uncertainty is vexing – but it also means you have an opportunity to help shape best practices before harsher mandates inevitably arrive. The spate of lawsuits we’ve discussed carry lessons that forward-thinking companies are already heeding. For example: Minimize the collection and use of personal data, especially biometrics, or at least get clear consent – not just to avoid fines, but to earn user trust. Vet your AI models for bias and disparate impact, because if you don’t, someone else will do it for you in a courtroom. If you’re deploying AI in sensitive areas (healthcare, driving, legal advice, etc.), invest in rigorous safety testing and human oversight – lives are literally at risk, along with your brand and balance sheet. Be transparent about your AI’s capabilities and limits; overhyping can backfire legally (just ask Tesla) . And as the OpenAI case showed, being candid about AI’s imperfections and building in user warnings can actually be a shield against liability.

The mood among AI-focused investors and executives is shifting from the old move-fast-and-break-things mantra to something more akin to move-fast-but-don’t-break-the-law. It’s a recognition that long-term viability requires navigating not just technological challenges but legal and ethical ones. Each lawsuit settled or verdict rendered is gradually drawing the borders of what’s acceptable. In the absence of clear laws, these cases are effectively creating a common-law of AI one judgment at a time. It’s a messy, reactive way to govern a technology – but it’s where we are.

In time, we can expect lawmakers to catch up. There’s bipartisan talk in Washington about requiring licensing for the most powerful AI systems, or mandating disclosures when AI is used in consumer interactions, and enhancing Section 230 to clarify AI-generated content. But until any of that becomes real, the smart money in the AI industry will do what the best companies in other industries do: plan for the worst-case scenarios. That might mean carrying new types of insurance, budgeting for legal compliance teams, or even collaborating on industry standards that demonstrate a commitment to “AI safety” broadly defined.

The past year’s legal salvos – billion-dollar fines, history-making complaints, and verdicts that read like sci-fi plots – should dispel any notion that AI exists in a lawless frontier. The law is very much entering the chat. AI companies are being held to account for the impact of their systems on people, including invading their privacy, denying them equal treatment, putting them in danger, or dragging their name through the mud. For executives and investors, this reckoning poses challenges, sure, but it’s also an opportunity to build the next generation of AI with liability and ethics in mind from day one. Those who succeed in doing so won’t just avoid court – they’ll earn the trust that’s necessary for AI to truly integrate into society. And ultimately, that trust is the bedrock of any lasting innovation.