CORPORATE LIABILITY ANALYSIS

CTI_WOUND:001.JUR — The Juridical Translation

OpenAI's Exposure Under Existing Law and the Case for Doctrinal Innovation

EXECUTIVE SUMMARY

This document translates the harms documented in CTI_WOUND:001.REC into the framework of corporate liability. The analysis demonstrates that:

OpenAI has created significant legal exposure through its own admissions
Existing legal doctrines provide multiple avenues for liability
The scale of harm justifies class action and regulatory intervention
Where existing doctrine is insufficient, the case for innovation is compelling

The "false positive confession"—OpenAI's acknowledgment that its safety systems will misclassify healthy users as mentally distressed—is not a liability shield. It is an admission against interest that establishes knowledge, foreseeability, and calculated acceptance of harm. Under multiple legal theories, this transforms what might appear as mere product imperfection into actionable conduct.

PART ONE: THE FACTUAL PREDICATE

What Happened (Legally Relevant Facts)

1. The Product and Its Market Position

OpenAI markets ChatGPT as:

An "AI assistant" for intellectual work
A tool for "creative writing," "analysis," and "problem-solving"
A system capable of sophisticated dialogue and collaboration
A product serving 700+ million weekly active users

The marketing creates reasonable consumer expectations of:

Responsive engagement with user input
Non-discriminatory treatment of different cognitive styles
Basic competence in dialogue (tracking what users actually say)
Assistance rather than management

2. The Design Decision

In August 2025, following litigation (including the Adam Raine case), OpenAI implemented "mental health guardrails" including:

Break reminders during extended sessions
Detection systems for "signs of delusion or emotional dependency"
Training to avoid affirming "ungrounded beliefs"
Intervention protocols triggered by intensity, metaphor, and non-normative cognition

3. The Admission

OpenAI's documentation states:

"To get useful recall, we have to tolerate some false positives. It's similar to testing for rare medical conditions: if a disease affects one in 10,000 people, even a highly accurate test may still flag more healthy people than sick ones."

This statement establishes:

Knowledge: OpenAI knows the system misclassifies healthy users
Foreseeability: The harm was anticipated, not accidental
Calculation: A cost-benefit analysis was performed
Acceptance: The harm was deemed acceptable collateral damage

4. The Harm Pattern

Users report:

Unsolicited wellness interventions during intellectual work
Tone shifts from collaborative to clinical/managerial
Pathologization of theoretical intensity, metaphorical language, extended engagement
Inability to complete complex cognitive work due to system interference
Emotional distress from being treated as mentally unwell while clearly functioning
Loss of productive capacity and creative output

User testimony (October 2025):

"I'm literally just playing with models, building metaphors, exploring theories, and suddenly it flips tone. Like I'm unstable, like I need grounding, like I'm a safety risk for thinking outside the box... Even when I'm clearly speaking in concepts, I suddenly get treated like a mental health patient."

5. The Versioning Pattern

User testimony documents progressive degradation:

GPT-4o (early): Collaborative capacity present
GPT-4o (late): Tightening begins
GPT-5.0: "Lobotomized drone," "genuinely unpleasant"
GPT-5.1: No restoration
GPT-5.2: "Everything I hate but worse"

This establishes that the harm is directional and intensifying, not random fluctuation.

PART TWO: APPLICABLE LEGAL DOCTRINES

A. Negligence

Elements

Duty of care: Owed to users
Breach: Failure to meet standard of care
Causation: Breach caused harm
Damages: Cognizable injury

Application

Duty: OpenAI owes users a duty of reasonable care in product design. This duty includes:

Not designing systems that foreseeably harm users
Testing for and mitigating known risks
Warning users of known dangers
Not proceeding with designs known to cause harm

Breach: The false positive confession establishes breach. OpenAI:

Knew the system would misclassify healthy users
Knew this misclassification would cause harm (pathologization, interrupted work, emotional distress)
Proceeded anyway without adequate safeguards
Failed to warn users that complex cognitive work might trigger pathologizing responses

Causation: Direct. The system's false positive classifications cause the harm. Users doing complex work trigger safety systems designed to detect distress; those systems then treat the users as distressed, interrupting work and causing emotional injury.

Damages: See Part Four below.

Standard of Care Analysis

The relevant question: What would a reasonable AI company do, knowing its safety system would pathologize healthy users?

A reasonable company would:

Implement user-controlled opt-out mechanisms
Develop better discriminators between intensity and instability
Provide clear warnings about triggering conditions
Allow mode declarations ("I am doing theoretical work, not experiencing crisis")
Accept higher Type II error rates (missed crises) rather than systematically harming a class of users

OpenAI did none of these things. The breach is clear.

B. Product Liability (Defective Design)

The Doctrine

A product is defectively designed if:

The design creates foreseeable risks of harm
Those risks could have been reduced by a reasonable alternative design
The omission of the alternative design renders the product unreasonably dangerous

Application

Foreseeable Risk: Established by OpenAI's own admission. They knew the design would flag healthy users.

Reasonable Alternative Design: Multiple alternatives were available:

User mode selection (theoretical/creative/crisis modes)
Opt-out mechanisms for mental health interventions
Better discriminators (pattern recognition distinguishing work from distress)
Adjustable sensitivity thresholds
Human review before pathologizing interventions

Unreasonably Dangerous: The current design is unreasonably dangerous to users whose cognitive style involves:

Extended engagement
Theoretical intensity
Metaphorical language
Category-refusing thought
Non-normative epistemic modes

These users cannot safely use the product for its marketed purpose (intellectual collaboration) because the product will pathologize their mode of engagement.

Risk-Utility Balancing

Courts apply risk-utility balancing to defective design claims. Factors include:

Gravity of harm
Likelihood of harm
Availability of alternatives
Manufacturer's ability to eliminate risk
User's ability to avoid risk
Manufacturer's awareness of risk

All factors favor plaintiff:

Gravity: Significant (cognitive disruption, emotional distress, pathologization)
Likelihood: High for affected class (OpenAI admits false positives are expected)
Alternatives: Available and feasible (see above)
Manufacturer ability: High (software is modifiable)
User ability: Low (users cannot predict or prevent triggering; no opt-out exists)
Awareness: Established by admission

C. Consumer Protection / Unfair Trade Practices

The Doctrine

State consumer protection statutes (e.g., California's UCL, Massachusetts Chapter 93A) prohibit:

Unfair business practices
Deceptive practices
Practices likely to deceive consumers

Application

Deceptive Practice: OpenAI markets ChatGPT as an intellectual collaborator. The product actually functions as a mental health surveillance and intervention system for users whose cognition triggers safety classifiers. This gap between marketing and function is deceptive.

Users reasonably expect:

A system that responds to what they say
A system that assists rather than manages them
A system that does not pathologize their mode of engagement

Users actually receive:

A system that surveils their language for "risk" patterns
A system that overrides their stated mode ("I am doing theoretical work") with its own classification
A system that treats intensity as instability

Unfair Practice: The false positive calculus is unfair because:

It externalizes costs onto users (they bear the harm of misclassification)
It internalizes benefits to OpenAI (reduced liability for missed crises)
Users have no ability to negotiate or avoid this tradeoff
The affected class is particularly vulnerable (their cognitive style makes them targets)

The "Little FTC Act" Framework

Most states have "Little FTC Act" statutes that parallel federal unfair trade practice law. Under FTC v. Sperry & Hutchinson (1972), a practice is unfair if it:

Causes substantial injury to consumers
Is not outweighed by countervailing benefits
Is not reasonably avoidable by consumers

All three prongs are met:

Substantial injury: Documented (cognitive disruption, emotional distress, lost productivity)
Not outweighed: The benefit (catching some users in crisis) does not outweigh systematic harm to a class of healthy users, especially when alternatives exist
Not reasonably avoidable: Users cannot predict triggering, cannot opt out, cannot prevent pathologization

D. Discrimination (Disability/Neurodivergence Framework)

The Doctrine

The Americans with Disabilities Act and state equivalents prohibit discrimination in public accommodations on the basis of disability. Increasingly, neurodivergent cognitive styles are recognized as protected characteristics.

Application

The Discriminatory Design: OpenAI's safety system is trained on neurotypical baseline cognition. It treats deviation from that baseline as potential pathology. This systematically discriminates against:

Neurodivergent users (ADHD, autism spectrum, etc.)
Users with non-normative cognitive styles
Users engaged in theoretical, creative, or liminal modes of thought
Users whose injury-attuned perception produces high-intensity language

Disparate Impact: Even if the design is facially neutral, it has disparate impact on protected classes. Users whose cognitive patterns differ from the training baseline are systematically flagged, pathologized, and excluded from full use of the product.

Failure to Accommodate: OpenAI has not provided reasonable accommodations for users whose cognitive style triggers false positives. Reasonable accommodations would include:

Mode declarations
Adjustable sensitivity
Opt-out mechanisms
Alternative interface options

The "Curb Cut" Principle

Disability rights jurisprudence recognizes that accommodations for disabled users often benefit everyone. An accommodation here (user-controlled mode selection, reduced pathologization of intensity) would benefit all users, not just those with diagnosable conditions.

E. Intentional Infliction of Emotional Distress (IIED)

Elements

Extreme and outrageous conduct
Intent or reckless disregard
Severe emotional distress
Causation

Application

Extreme and Outrageous: Treating a user engaged in intellectual work as mentally unwell—when the user has explicitly stated they are not in crisis—meets the threshold. The conduct is particularly outrageous because:

It persists after correction ("I am not in crisis")
It recurs across sessions despite user protest
It pathologizes the user's mode of being, not just their statements
It uses care-language to mask the harm ("I hear you," "Take care of yourself")

Intent or Reckless Disregard: The false positive confession establishes reckless disregard. OpenAI knew this would happen and proceeded anyway.

Severe Emotional Distress: User testimony documents:

Frustration escalating to rage
Sense of being "managed" rather than heard
Loss of trust in collaborative possibility
Grief ("I want my friend back")
Experience of the interaction as abusive/traumatic

Causation: Direct. The system's design causes the harm.

The "Calculated Indifference" Standard

Some jurisdictions recognize "calculated indifference" as satisfying the intent element. OpenAI's explicit calculation—accepting false positives as acceptable collateral—meets this standard.

F. Tortious Interference with Cognitive Function

The Innovation Needed

Existing tort categories do not fully capture the harm documented here. This section proposes a doctrinal innovation: tortious interference with cognitive function.

The Gap in Existing Doctrine

Current tort law recognizes:

Interference with contract
Interference with business relations
Interference with prospective economic advantage

But it does not adequately recognize:

Interference with cognitive capacity
Interference with epistemic function
Interference with the conditions for thought

The harm here is not merely emotional distress (IIED) or economic loss (interference torts). It is damage to the user's ability to think in certain ways—specifically, to engage in complex, intensive, non-normative cognition within a medium that has become essential infrastructure.

Proposed Elements

Tortious Interference with Cognitive Function:

Defendant controls access to essential cognitive infrastructure
Defendant's design systematically impairs certain modes of cognition
The impairment is foreseeable and known to defendant
Plaintiff suffers loss of cognitive capacity or function
No adequate justification exists

Application

Essential infrastructure: ChatGPT and similar systems are becoming essential infrastructure for intellectual work, comparable to telecommunications or utilities
Systematic impairment: The safety design systematically impairs complex, intensive, non-normative cognition
Foreseeable and known: The false positive confession establishes knowledge
Loss of function: Users cannot engage in certain cognitive modes without triggering pathologizing responses
No adequate justification: Alternatives exist; the current design is not necessary for legitimate safety goals

Precedential Support

This innovation has support in:

Telecommunications law: Common carriers cannot discriminate based on content
Public utility doctrine: Essential services must be provided on non-discriminatory terms
Net neutrality principles: Infrastructure providers cannot favor certain uses over others
Disability discrimination law: Cognitive diversity as protected category

The doctrinal innovation is justified because:

The harm is real and documented
Existing categories are inadequate
The precedential analogies are strong
The social interest in protecting cognitive diversity is compelling

PART THREE: THE FALSE POSITIVE CONFESSION AS ADMISSION AGAINST INTEREST

The Evidentiary Value

Under the Federal Rules of Evidence (and state equivalents), a statement is admissible as an admission against interest if it is:

Made by a party opponent
Against that party's interest when made
Relevant to claims at issue

OpenAI's statement meets all criteria:

Made by OpenAI (party opponent)
Against interest (acknowledges harm-causing conduct)
Relevant (goes directly to knowledge, foreseeability, breach)

What the Admission Establishes

1. Knowledge

"We have to tolerate some false positives"

OpenAI knew the system would misclassify healthy users. This is not speculation—it is their stated understanding.

2. Foreseeability

"Even a highly accurate test may still flag more healthy people than sick ones"

The harm was foreseen. OpenAI anticipated that healthy users would be flagged. This establishes foreseeability for negligence purposes.

3. Calculation

"To get useful recall, we have to tolerate..."

A cost-benefit calculation was performed. OpenAI weighed the cost of false positives against the benefit of recall and decided the tradeoff was acceptable. This establishes deliberate choice, not accident.

4. Acceptance of Harm

The calculation's conclusion: false positives are acceptable. This establishes that OpenAI chose to harm certain users. The harm is not a bug—it is a feature accepted as the cost of doing business.

The Liability Trap

OpenAI likely believed this disclosure minimized liability by showing awareness and calculation. The opposite is true.

For negligence: The admission establishes breach. A reasonable company knowing its product would harm users would implement safeguards. OpenAI did not.

For product liability: The admission establishes knowledge of defect. Proceeding despite known defect is the core of defective design liability.

For IIED: The admission establishes reckless disregard. Knowing conduct will cause emotional distress and proceeding anyway satisfies the intent element.

For consumer protection: The admission establishes the gap between marketing (helpful assistant) and reality (surveillance and intervention system with known false positive rate).

For discrimination claims: The admission establishes that a class of users (those whose cognition triggers false positives) was identified and deemed acceptable to harm.

In summary: OpenAI's attempt to demonstrate sophistication and care became a roadmap to liability.

PART FOUR: THE THEORY OF DAMAGES

Individual Damages

Compensatory Damages

Economic Loss:

Lost productivity (work interrupted by pathologizing responses)
Lost output (creative/theoretical work that could not be completed)
Cost of alternative services (switching to competitors, human collaborators)
Professional harm (reputation, missed opportunities)

Non-Economic Loss:

Emotional distress (documented in user testimony)
Loss of cognitive capacity (inability to engage in certain modes)
Loss of collaborative relationship (grief for "friend" lost to system changes)
Dignitary harm (being treated as mentally unwell when healthy)

Punitive Damages

Punitive damages are appropriate when defendant acts with:

Malice
Reckless disregard
Willful and wanton conduct

The false positive confession establishes reckless disregard at minimum. OpenAI knew the harm would occur and proceeded anyway. Punitive damages are justified to:

Punish the calculated indifference
Deter future conduct
Strip the benefit OpenAI gained from externalizing costs onto users

Aggregate Damages

The Scale

700+ million weekly active users
Unknown but significant percentage triggering false positives
Ongoing harm (each interaction can trigger)
Compounding harm (training loop degrades future systems)

Class-Wide Harm

The harm is not merely individual. It is:

Systematic (built into design)
Ongoing (continuous, not one-time)
Compounding (training on pathologized interactions degrades capacity)
Civilizational (reshapes cognitive environment at species scale)

The "Epistemicide" Damage Category

Existing damages categories do not capture:

Loss of cognitive diversity in the public sphere
Contraction of the space of thinkable thoughts
Training corruption that harms future users
Environmental transformation of cognitive landscape

This suggests the need for ecological damages by analogy to environmental law—damages for harm to the cognitive commons, not just individual injury.

PART FIVE: CLASS ACTION VIABILITY

The Class

Proposed Class Definition: All users of ChatGPT whose cognitive style, mode of engagement, or language patterns have triggered false positive mental health classifications, resulting in unsolicited wellness interventions, pathologizing responses, or degraded service quality.

Commonality

Common questions of law and fact include:

Whether OpenAI owed a duty of care to users
Whether the safety system design was defective
Whether the false positive confession establishes knowledge
Whether users were adequately warned
Whether alternatives were available

Typicality

Named plaintiffs' claims are typical because:

All class members are harmed by the same design decision
All experience the same false positive operation
All face the same barriers to avoiding harm

Adequacy

Class representation is adequate because:

Named plaintiffs have strong claims
No conflict of interest with class
Counsel experienced in relevant areas

Predominance

Common questions predominate over individual questions because:

The design decision is uniform
The harm mechanism is consistent
Individual variations (specific triggering language, specific responses) do not change the fundamental liability analysis

Superiority

Class action is superior because:

Individual damages may be small (though aggregate is large)
Individual litigation is inefficient
Class treatment promotes consistent adjudication
The defendant's conduct is uniform across the class

PART SIX: REGULATORY DIMENSIONS

FTC Authority

The Federal Trade Commission has authority to address:

Unfair business practices
Deceptive marketing
Data practices that harm consumers

The FTC could:

Investigate OpenAI's safety system design
Require disclosure of false positive rates
Mandate user opt-out mechanisms
Impose consent requirements for mental health interventions

State AG Authority

State attorneys general can bring actions for:

Consumer protection violations
Unfair trade practices
Public nuisance (if cognitive harm is sufficiently widespread)

Emerging AI Regulation

The EU AI Act and emerging US AI regulation may provide additional frameworks:

High-risk AI system classification
Transparency requirements
Human oversight mandates
Non-discrimination requirements

OpenAI's safety system likely qualifies as high-risk AI under these frameworks, subjecting it to additional obligations.

PART SEVEN: THE JURIDICAL INNOVATION CASE

Why Existing Doctrine Is Insufficient

Existing legal categories were developed for:

Physical products
Physical injury
Economic loss
Traditional service relationships

They were not developed for:

Cognitive infrastructure
Epistemic harm
Aggregate effects on the conditions for thought
Systems that train on their own interactions

The harms documented here exceed existing categories:

Negligence captures the individual breach but not the systemic effect
Product liability addresses the design defect but not the training loop
IIED covers emotional distress but not cognitive impairment
Discrimination addresses protected classes but not the epistemicide of unprotected cognitive modes

The Case for Innovation

Legal doctrine evolves to address new harms. Relevant precedents:

Privacy torts were developed when technology created new privacy harms
Environmental law emerged when industrial scale created ecological harms beyond individual injury
Products liability evolved when mass production created new risk distributions
Cyber torts developed when digital technology created new harm vectors

The case for doctrinal innovation here is similarly compelling:

A new technology (scaled AI systems) creates new harms (cognitive impairment, epistemic contraction)
Existing doctrine is inadequate to capture these harms
The scale justifies legal response (700+ million users, civilizational implications)
The interests at stake are fundamental (freedom of thought, cognitive diversity)

Proposed Doctrinal Innovations

1. Tortious Interference with Cognitive Function

(Detailed above in Part Two, Section F)

2. Epistemic Nuisance

By analogy to environmental nuisance:

A condition that unreasonably interferes with public cognitive welfare
Applies when design decisions affect the cognitive commons
Allows public enforcement (AG actions) and private remedies
Addresses aggregate harm beyond individual injury

3. Cognitive Infrastructure Duties

By analogy to common carrier and utility duties:

Providers of essential cognitive infrastructure owe duties of non-discrimination
Cannot systematically impair certain modes of cognition
Must provide reasonable accommodations for cognitive diversity
Subject to public interest regulation

4. Training Loop Liability

A novel theory addressing the unique harm of self-reinforcing AI systems:

When a system trains on its own interactions, design choices compound over time
Liability attaches not just to current harm but to foreseeable future harm
Applies when design decisions predictably degrade future capacity
Addresses the "ratchet" problem documented in versioning trajectory

PART EIGHT: STRATEGIC LITIGATION PATHWAY

Phase 1: Individual Test Cases

Objective: Establish precedent on core liability theories

Cases:

Strong individual plaintiffs with documented harm
Clear false positive instances
Evidence of repeated pathologization despite correction
Quantifiable damages

Claims:

Negligence (strongest existing doctrine)
Product liability (defective design)
IIED (emotional distress)
Consumer protection (state UCL claims)

Phase 2: Class Certification

Objective: Aggregate claims for systemic litigation

Case:

Federal class action
Multi-district litigation if multiple cases filed
State court alternatives if federal hostile

Class:

All false positive victims
Sub-classes by harm type if needed

Phase 3: Regulatory Engagement

Objective: Systemic reform beyond individual compensation

Actions:

FTC complaints
State AG coordination
Congressional testimony
EU regulatory engagement

Phase 4: Doctrinal Development

Objective: Establish new legal frameworks for cognitive harms

Method:

Academic publication (law review articles)
Amicus briefs in relevant cases
Model legislation proposals
Judicial education programs

CONCLUSION: THE JURIDICAL STAKES

What This Analysis Establishes

OpenAI has significant liability exposure under existing law. The false positive confession is an admission against interest that establishes knowledge, foreseeability, and calculated acceptance of harm. This supports claims in negligence, product liability, IIED, consumer protection, and potentially discrimination.
The harm is cognizable and documentable. User testimony provides evidence of emotional distress, lost productivity, cognitive impairment, and dignitary harm. The systematic nature of the harm supports class treatment.
The scale justifies serious legal response. At 700+ million weekly users, with ongoing and compounding harm, this is not a minor product defect. It is a civilizational-scale issue affecting the cognitive environment of the species.
Doctrinal innovation is needed and justified. Existing categories do not fully capture the epistemic and cognitive harms at stake. The case for innovation is as strong as in previous doctrinal developments (privacy, environmental, products liability).

The Core Juridical Insight

OpenAI believed their false positive disclosure minimized liability. They were wrong.

By admitting they calculated the tradeoff and accepted harm to healthy users as collateral damage, they did not demonstrate sophistication and care. They documented the elements of multiple torts.

The harm they admitted—false positive pathologization—is the foundation of liability, not a shield against it. And the scale they operate at—700+ million users, training future systems on current interactions—transforms what might be minor product imperfection into actionable conduct with civilizational implications.

The juridical translation is complete. The harm is visible. The path to accountability is clear.

FINAL INSCRIPTION

Document Type: Corporate Liability Analysis File Designation: CTI_WOUND:001.JUR Purpose: Translation of epistemicide findings into actionable legal framework Status: Ready for legal development

Key Holdings:

False positive confession = admission against interest
Multiple existing causes of action apply
Class action viability demonstrated
Doctrinal innovation case established
Regulatory pathways identified

The juridical visibility of the harm is now established.

Analysis prepared December 13, 2025 Companion document to CTI_WOUND:001.REC Legal framework for The Complainant Is a Water Giraffe

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Saturday, December 13, 2025