TLDR: A new wave of class action lawsuits is emerging, targeting consumer-facing companies that utilize generative AI tools. These lawsuits are driven by privacy concerns, the potential for AI to make biased or fallible decisions in customer interactions, and follow previous litigation trends seen with other consumer-tracking technologies.
The landscape of consumer privacy litigation is poised for a significant shift, with a new wave of class action lawsuits beginning to target companies that deploy generative artificial intelligence (AI) tools in consumer-facing applications. This trend mirrors past litigation surges seen with technologies like “session replay,” website chatbots, and website pixels, which previously led to hundreds of class actions across the United States, particularly in states like Florida and California.
Legal experts from firms such as Holland & Knight and Crowell & Moring indicate that while initial AI-related lawsuits primarily focused on copyright infringement claims brought by content creators against generative AI developers, the focus is now broadening to encompass consumer privacy and protection.
Warrington Parker, managing partner of Crowell & Moring’s San Francisco office, highlights that companies’ increasing reliance on AI for direct customer interactions could lead to a surge in consumer class actions. These lawsuits are anticipated to fall into two main categories: “bad decisions” and “selection cases.”
“Bad decisions” refer to instances where AI tools, used in areas like customer service, product returns, refunds, or dynamic pricing, yield illogical or unfavorable outcomes for consumers. Parker notes, “There is an assumption today that AI is rational, reasonable and makes great decisions, but that’s a falsity.” The “black box” nature of many AI systems, where the decision-making process is opaque, could further complicate a company’s ability to explain AI-driven outcomes, exposing them to liability.
“Selection cases” involve AI making discriminatory choices, such as in hiring, credit extension, or loan approvals, where AI might inadvertently reflect biases present in its training data, leading to claims based on age, race, or gender. Companies are advised to assess their AI tools and training content for bias, with some jurisdictions, like New York, already requiring certification that AI systems are not biased.
The legal precedent for such actions is already forming. In 2024, privacy-related litigation against generative AI continued, with lawsuits filed under various state and federal theories of liability, including state wiretapping laws and unfair or deceptive acts or practices laws. For example, California’s Invasion of Privacy Act (CIPA) has been invoked in “AI eavesdropping” lawsuits, alleging that AI-powered chatbots intercept and record customer communications without consent, with the AI provider then using these communications for training. While many of these cases have been dismissed or settled, some, like *Ambriz v. Google, LLC*, have survived motions to dismiss, indicating a growing legal pathway for plaintiffs.
Regulators, including the Federal Trade Commission (FTC) and state attorneys general, are also actively pursuing enforcement actions against AI companies under consumer protection laws, further signaling the heightened scrutiny on AI’s impact on consumers.
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Given the potential for millions of customers across numerous jurisdictions to be affected, Parker warns that the legal landscape for companies using AI to serve consumers could become a “wild, wild world.” This emerging litigation wave underscores the critical need for companies to meticulously evaluate the privacy implications, fairness, and transparency of their AI deployments.
