The Raine v. OpenAI wrongful death case has thrust a previously little-known governance tool into the spotlight of AI-related legal battles. System cards, which detail AI systems’ performance as disclosed by developers, are now key in lawsuits, helping both sides argue what a company knew, when, and if its safety measures were sufficient, as illustrated by this high-profile litigation.
Understanding how these documents create legal exposure is key in a thoughtful risk mitigation strategy. The risks are similar, but not exactly the same, across AI developers, companies that build tools on AI platforms, and companies that use AI tools.
System Cards and Legal Exposure
The system card for OpenAI’s GPT-5.5 model, released in April 2026, reports lower scores than the immediately preceding model for not producing disallowed outputs when the input includes an image across all four categories evaluated-hate, extremism, self-harm, and erotic harms-though OpenAI describes the differences as minor and not statistically significant.
This data suggests GPT-5.5 may have struggled more with flagging inappropriate content. If a deployer of AI that developed a companion app used this new ChatGPT model, there might be a valid argument that the deployer should have known the selected model was not appropriate for this use case because of the information in the system card.
System Cards Create Liability
System cards function primarily as evidence rather than as an independent cause of action. They can nonetheless give rise to liability where the disclosure itself is false or misleading because AI capability representations are subject to Section 5 of the Federal Trade Commission Act and, in California, to civil penalties under the Transparency in Frontier Artificial Intelligence Act enacted in 2025.
Importance of System Cards to Companies
AI providers publish system cards to outline how their technologies function in practical settings, covering intended applications, acknowledged weaknesses, testing processes, and safety precautions. Firms that embed third-party AI into their offerings may face allegations that they ignored risks highlighted in these documents.
If those materials identify known limitations, foreseeable misuse, or safety concerns, a downstream company may be expected to account for those risks in its own product design, user disclosures, contractual controls, and usage restrictions. Regulatory guidance confirms that AI performance claims must meet the same honesty standards as any other product description.
The Securities and Exchange Commission has taken the same posture for public companies. Its Division of Examinations, in priorities released in November 2025, said it will assess whether registered companies’ AI-related disclosures, supervisory frameworks, and controls align with actual practices. Those priorities reach investment advisers, broker-dealers, and investment companies, among others, rather than issuers generally.
California codified the same principle with penalties. The Transparency in Frontier Artificial Intelligence Act, effective January 1, 2026, requires frontier AI developers to publish safety frameworks, disclose catastrophic risk assessments, and report critical safety incidents within fifteen days to state regulators.
California Law Imposes Penalties
Civil penalties of up to $1 million per violation, enforced by the California attorney general, attach to enumerated violations. Raine v. OpenAI, No. CGC-25-628528 (Cal. Super. filed Aug. 26, 2025) arose from the suicide of a sixteen-year-old who had used ChatGPT extensively in the months before his death.
The plaintiffs claim OpenAI’s platform was flawed, that the dangers were predictable, and that the company neglected to implement protective measures to prevent harm. Later court filings accused OpenAI of intentionally removing a ‘suicide guardrail’ from the system.
As of now, the case remains in pretrial stages. Both sides cite OpenAI’s own documentation to argue whether the company was aware of risks and whether its safety measures were adequate, with neither party disputing the relevance of the system card’s content.
Both sides treat system cards as probative of the company’s knowledge, the foreseeability of harms, and the adequacy of the company’s response. However, the issue is whether the disclosures aligned with what the system actually did. System card statements can function as admissions.
Disclosures Become Evidence
Descriptions of known limitations or failure modes are often drafted to demonstrate responsible governance, but in litigation, those same statements become evidence that the company had actual knowledge of a specific risk before harm occurred. Every disclosure of a weakness should be accompanied by internal documentation showing how that weakness was evaluated and what was done about it.
A documented risk with no documented response is a failure-to-warn claim waiting to be filed. If a system card identifies a limitation but the company does not translate that acknowledgment into user-facing warnings, usage restrictions, or design changes, plaintiffs will argue the company knew of the hazard and chose not to address it.
The Raine amended complaint makes exactly this argument regarding the removed guardrail. Outdated documentation creates independent exposure.
AI systems change faster than the documents that describe them. A system card that no longer reflects current system behavior, particularly one that omits the removal of a previously disclosed safety feature, suggests the company stopped paying attention to its own disclosures.
Companies should treat system card updates as a compliance obligation. Inconsistency between internal testing and external statements is discoverable. System cards will be reviewed alongside internal evaluations, safety logs, and communications.
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