LexNews | #043 — AI Products and the Consumer Fraud Class Action Problem

📰 LexNews — SavvyLex's take on what's moving in legal AI this week.

Source: AI Products and the Challenges of Consumer Fraud Class Actions — David E. Kouba, Arnold & Porter (Law.com, April 15, 2026)

The Story

AI litigation has been building in waves. First came product liability (Raine v. OpenAI). Then copyright (Andersen v. Stability AI). Then biometric privacy (McPherson v. Clearview). The next wave being watched by defense counsel: consumer fraud class actions.

Writing in Law.com, Arnold & Porter counsel David E. Kouba analyzes why AI products — despite their widespread use and natural appeal to the plaintiffs' bar — present uniquely difficult structural barriers to class certification.

The argument is technical, but the implications are significant for any organization building, deploying, or advising on AI products.

Why Class Certification Is So Hard for AI Products

The consumer fraud class action model was built for uniform products: a car that fails the same way for everyone, a drug with a misrepresented side effect affecting every patient. The class cohesion argument works when everyone bought the same thing and experienced the same harm.

AI products break that model in three distinct ways.

1. Variable Outputs, Variable Experiences

AI systems are probabilistic. The same product, the same query — different outputs. One user gets an accurate legal summary. Another gets a hallucinated citation. A third gets a response that's technically correct but contextually wrong for their jurisdiction.

Courts have repeatedly held that class certification is inappropriate where some class members received exactly what was allegedly represented and suffered no harm. (Rivera v. Wyeth Ayerst Laboratories, 5th Cir.; Briehl v. General Motors, 8th Cir.) AI's inherent variability creates exactly this problem — at scale.

2. Economic Harm Can't Be Reduced to a Formula

Consumer fraud class actions typically allege a single economic injury: you paid X for a product worth less than X. AI products don't conform to this structure:

• Many AI tools are free — no purchase price, no measurable economic injury

• Paid products offer multiple tiers at different price points

• Even identical pricing doesn't create identical harm — one user's incorrect output is trivial, another's causes significant financial damage

Aggregate damages calculations fail when individualized proof of harm is required. That's the class action's structural weakness when applied to AI.

3. User Knowledge Varies Dramatically

AI commentary is everywhere — and it's contradictory. Some users came to an AI product fully aware of hallucination risks. Others had no idea. That variability is legally significant.

A user who understood that an AI product could produce incorrect outputs may struggle to show that alleged misrepresentation caused their harm. Individual knowledge becomes relevant to assumption of risk, comparative fault, statute of limitations, and failure to mitigate — all of which fracture class cohesion.

Additional Structural Defenses

Kouba notes two further structural barriers that AI vendors have available:

• Mandatory arbitration clauses and class-action waivers — standard in most AI product terms of service, which would block class relief entirely for a significant portion of any potential class

• Product complexity and rapid evolution — the technology, training data, algorithms, and marketing of an AI product change continuously, creating certification challenges based on the absence of a stable, definable product at any single point in time

The SavvyLex Take

Kouba's analysis is defense-oriented — and correctly identifies why consumer fraud class actions against AI products face steep structural hurdles. But the article also contains an important warning that legal AI vendors should not miss:

"Companies that manufacture, market and sell AI products should be thoughtful when evaluating things like product design, marketing and advertising, contractual terms, and how a product performs."

This is exactly the governance discipline that separates defensible AI from exposed AI.

What Kouba is describing — from the plaintiffs' perspective — is the absence of a governed implementation layer. The reason AI harm is individualized, variable, and difficult to certify as a class is the same reason it is difficult to audit, monitor, and defend: AI outputs depend on context, prompts, user behavior, and system configuration.

For organizations deploying AI in legal environments, the class action risk is asymmetric. Certified or not, the litigation is coming. And when it arrives, your defense will depend entirely on:

• Whether you documented what your AI system was designed to do

• Whether you implemented attorney oversight at appropriate workflow junctures

• Whether your citation verification layer caught hallucinations before they reached a client or a court

• Whether your audit trail can reconstruct what happened, when, and why

The variability that defeats class certification does not defeat individual claims. A single attorney submitting a hallucinated citation to a federal judge — documented, sanctionable, and attributable to an ungoverned AI deployment — does not need a class to create liability.

Governance is not a hedge against class actions. It is the baseline defense against every form of AI liability.

What to Watch

• Raine v. OpenAI (Cal. Super. Ct. 2025) — product liability framework for AI-enabled products; how courts define "defect" for probabilistic systems

• Arbitration enforcement — whether courts uphold AI product class-action waivers as AI-specific litigation matures

• State consumer protection statutes — some state AG enforcement actions don't require class certification; watch for state-level AI consumer protection enforcement as an alternative litigation vehicle

• EU AI Act (August 2026) — high-risk AI system requirements will create new documented obligations that could be used as the standard of care in U.S. litigation

Bottom Line

The plaintiffs' bar will keep trying. The structural barriers Kouba identifies are real — but they are also temporary. As AI products mature, as outputs become more consistent, and as courts develop AI-specific class action doctrine, the certification hurdles will evolve.

The organizations that will weather this wave are the ones that governed their AI deployments from the beginning — with documented design choices, oversight architecture, audit trails, and accountability chains that can withstand scrutiny in any forum.

→ Assess your AI governance posture: savvylex-consulting.com/BookACall