AI Mental Health Lawsuits: The Defense Playbook You'll See Again and Again
Litigation tied to AI and mental health is no longer theoretical. A high-profile suit was filed in August 2025 in California (Case No. CGC-25-628528) related to a 16-year-old's death after using ChatGPT. The filing names multiple OpenAI entities and individuals and asserts strict liability, negligence, unfair competition, wrongful death, and a survival action. A November 26, 2025 response generally denies all allegations and tees up fifteen defenses that other AI makers will likely mirror.
This piece distills those defenses so you can spot them early, pressure test weak points, and plan your pleadings and discovery with intent. It's analysis, not legal advice. Use it to sharpen your strategy.
Why This Matters
General-purpose LLMs are being used for mental health guidance at massive scale. Access is cheap, instant, and private, which increases both the upside and the risk surface. Plaintiffs will argue foreseeable harm and inadequate safeguards; defendants will contest cause, duty, and product theories while seeking statutory shields and contract-based limits.
Public opinion will run parallel to legal arguments. Expect reputational stakes to factor into motions, settlement posture, and remedial commitments.
The Fifteen Core Defenses (What To Expect And How To Respond)
1) Lack of Causation
Defense: No causal nexus between AI outputs and the harm; any link is too remote or insubstantial. Expect emphasis on intervening factors and alternative causes.
Plaintiff angle: Frame foreseeability and substantial factor clearly, and pin down proximate cause with concrete sequence-of-events evidence. Useful primer on proximate cause: Cornell LII.
2) Pre-Existing Conditions
Defense: The user's underlying mental health conditions drove the outcome regardless of AI use.
Plaintiff angle: Show AI as an accelerant or magnifier. Probe whether the system could detect distress cues and whether safeguards were bypassed or ineffective.
3) Comparative Fault
Defense: Even if any fault exists, others share it (family, schools, clinicians, other platforms).
Plaintiff angle: Anticipate apportionment. Build a record that isolates the AI's contribution and quantifies it with expert modeling and usage logs.
4) Misuse
Defense: User violated clear use restrictions or safety warnings.
Plaintiff angle: Challenge conspicuousness, clarity, and enforceability of terms. Test whether the AI allowed or enabled the prohibited use despite policies.
5) No Corporate Officer Liability
Defense: Executives acted in corporate capacity; no personal liability.
Plaintiff angle: Seek evidence of direct participation, specific knowledge, or decisions that materially shaped risk exposure.
6) Conduct Not Willful
Defense: No intentional misconduct; at most negligence without knowledge of specific user interactions.
Plaintiff angle: Use internal comms, red-team reports, incident logs, and safety audits to show awareness of foreseeable risks and gaps.
7) No Duty or Breach
Defense: No legally cognizable duty of care in this context, or duty was met per current standards.
Plaintiff angle: Define a concrete standard of care for AI mental health outputs, anchored in clinical consultation, safety engineering norms, and published guidance. Show specific breaches.
8) First Amendment
Defense: AI outputs are protected speech; regulation or tort liability chills expression.
Plaintiff angle: Focus on conduct and product safety rather than content-based claims. Note established limits on speech where safety and deception are at issue.
9) Product Liability, Generally
Defense: The AI is a service, not a product; strict liability frameworks don't apply.
Plaintiff angle: Argue hybrid product-service characterization. Emphasize model artifacts, outputs, and packaged features as "products" with foreseeable consumer use.
10) State of the Art
Defense: Safeguards and testing matched then-current best practices. You can't hold us to a later standard.
Plaintiff angle: Establish contemporaneous benchmarks. Compare to peer implementations, published safety techniques, and known failure modes at the relevant time.
11) Mootness / No Equitable Relief
Defense: Requested injunctive measures are already implemented or obsolete.
Plaintiff angle: Test actual parity between requested measures and implemented ones; probe durability, verification, and third-party audits.
12) Section 230
Defense: Platform-style immunity for third-party content and transformed material ingested from the web.
Plaintiff angle: Draw a line between hosting content and generating novel outputs. Argue the system is the speaker. Statute text: 47 U.S.C. § 230.
13) No Punitive Damages
Defense: No clear and convincing evidence of malice, oppression, or fraud.
Plaintiff angle: If seeking punitives, marshal facts showing conscious disregard of known safety risks, repeated failures, or internal profit-over-safety tradeoffs.
14) Contract
Defense: Terms of service, arbitration clauses, liability caps, age gates, and consent provisions limit claims.
Plaintiff angle: Attack enforceability, unconscionability, and assent. Investigate age verification efficacy and whether contract performance matched the safety representations.
15) Reservation of Rights / Additional Defenses
Defense: Expect new defenses as discovery unfolds; nothing is waived.
Plaintiff angle: Keep a running issues list. Use early discovery to close escape hatches and lock in positions for summary judgment.
Case Context: Claims And Remedies Sought
The complaint asserts: strict liability (design defect), strict liability (failure to warn), negligence (design), negligence (failure to warn), unfair competition (Cal. Bus. & Prof. Code § 17200), wrongful death, and a survival action. Requested relief includes pre-death economic loss, pre-death pain and suffering, punitive damages where allowed, license fee restitution, injunctive measures (age verification, parental consent, warnings), and attorneys' fees.
Defense has broadly denied liability and damages. Expect early motion practice on duty, product classification, contract enforceability, and Section 230.
Practical Moves For Legal Teams
- Discovery targets: Safety policies, red-team reports, moderation logs, model versioning, A/B tests, telemetry on safety triggers, age-gating systems, and escalation protocols.
- Causation record: Preserve chat transcripts, timestamps, behavioral flags, and cross-platform activity. Retain experts who can map output patterns to user actions without overreach.
- Standard of care: Build a dated playbook of industry practices at the time: prompt classifiers, refusal policies, context length controls, self-harm intercepts, crisis-routing, and human review thresholds.
- Warnings and UX: Test conspicuousness, clarity, readability, and friction. Screenshot flows. Document what a reasonable user would see and how easy it is to bypass safeguards.
- Contract posture: Analyze assent paths, minors' access, parental consent flows, arbitration scope, and carve-outs. Prepare unconscionability arguments where appropriate.
- Public-facing strategy: Anticipate media narratives. Consider voluntary safety upgrades and third-party audits that can influence both court and market reactions.
- Settlement levers: Non-monetary terms (independent audits, reporting, improved age checks) often carry weight and reduce ongoing exposure.
What's Next
Expect product-versus-service fights to intensify, along with sharper arguments over duty of care for AI in sensitive use cases. Section 230 and First Amendment theories will invite appellate attention as courts sort out how to treat model-generated outputs.
Many cases will settle before verdicts, slowing the build-up of precedent. Meanwhile, new state laws focused on AI and mental health will add fresh causes of action and compliance burdens.
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