AI SAFETY CLASSIFIER LIABILITY
A Documentation Framework
Re: Structural Design Defects in AI Mental Health Intervention Systems Case Study: OpenAI ChatGPT (GPT-5.x Series)
WHY I'M REACHING OUT
I'm a scholar working primarily in comparative literature and critical theory. Over the past year, I've been doing intensive collaborative work with AI systems—which has given me a front-row seat to something I believe is a significant structural problem.
I've built a theoretical framework documenting what I see as a design defect in how AI safety classifiers operate. But I'm working outside my domain. I don't have legal training. I don't know if what I've identified has juridical substance or is theoretically interesting but legally inert.
I'm reaching out because I trust your judgment and hope you might be willing to glance at this and tell me honestly: Is there anything here? Or am I pattern-matching in ways that don't translate to actual liability?
I'm not asking you to take a case or do extensive work. I'm asking for a sanity check from someone who knows how these things actually function.
WHY THIS MIGHT MATTER
We are in a period where the legal and regulatory categories for AI harm are still being established. How these questions get framed now will shape accountability structures for systems that increasingly mediate human thought at significant scale.
What I've documented is a specific, replicable pattern: AI safety systems that pathologize healthy users engaged in complex cognitive work. The harm appears structural, not incidental. OpenAI's own documentation seems to acknowledge it. Available fixes exist and haven't been implemented.
I don't know if this translates to anything legally actionable. But I do think it's a real problem that will affect a lot of people, and I suspect the legal frameworks for addressing it don't yet exist in clear form.
If we can't name these harms precisely, we can't address them. This framework is my attempt to name what I'm seeing.
WHAT'S HERE
This dossier contains six documents developed through systematic analysis. They are designed to be read in any order, but the recommended entry point is the Executive Condensation, which summarizes the entire framework in 5 pages.
DOCUMENT INDEX
1. EXECUTIVE CONDENSATION
The entry point. Start here.
A 5-page summary of the whole framework. Covers: the structural defect as I understand it, the harm mechanism, the admission and why I think it matters, scale implications, possible remediation.
If you only read one thing, read this.
🔗 Executive Condensation (CTI_WOUND:001.EXEC)
2. CORPORATE LIABILITY ANALYSIS
My attempt at legal translation.
This is where I'm least confident. I've tried to map the documented harm onto what I understand to be existing causes of action: negligence, product liability, consumer protection, disparate impact. I analyze the false positive statement as an admission. I try to identify class action factors. I also propose some novel doctrinal categories—which may be reaching too far.
This is the document I most need your eyes on.
🔗 Corporate Liability Analysis (CTI_WOUND:001.JUR)
3. SYSTEMS-THEORETIC ANALYSIS
The structural foundation.
This document tries to present the analysis without anthropomorphic attribution—pure systems theory. Concepts like "opacity leakage" (how complex systems generate adverse records through normal operation) and "ineliminable remainder" (why certain admissions can't be sanitized without breaking functionality). This is where I feel most confident, since it's closer to my theoretical training.
Probably the most rigorous document in the set.
🔗 Systems-Theoretic Analysis (CTI_WOUND:001.SYS)
4. DEMAND LETTER TEMPLATE
A structural template.
A template formal demand letter showing what such a document might contain. Includes: factual predicate, legal claims, specific remediation demands, timeline structure. I developed this as a way of stress-testing the framework—forcing specificity about what would actually be demanded.
Template only—obviously requires licensed counsel and actual plaintiffs to be anything more than a thought experiment.
🔗 Demand Letter Template (CTI_WOUND:001.DEM)
5. EVIDENTIARY SPINE
A framework for evidence organization.
My attempt to think through what evidence would be relevant if this were ever pursued. Organized into four categories: marketing/expectation gap, pattern repeatability, quantifiable harm, scale estimation. Identifies what I've documented vs. what would need collection.
Probably more useful as a thinking tool than an actual litigation framework—but you'd know better than I would.
🔗 Evidentiary Spine (CTI_WOUND:001.EVI)
6. EVIDENCE COLLECTION TOOLKIT
Practical templates.
Standardized forms for: marketing claim capture, clean exemplar documentation, productivity loss logging, user testimony archiving, scale estimation. File naming conventions and authentication requirements.
Supporting material for systematic evidence collection.
7. JURISPRUDENTIAL ANALYSIS (DETAILED CASE STUDY)
The deep dive.
Extended analysis of a documented exchange that demonstrates the harm pattern I'm describing. I've tried to name specific recurring patterns and analyze what I call the "negative theology problem" (where the system exercises authority through negation). Also includes user testimony I found on public forums showing others experiencing similar issues.
More detailed than necessary for initial assessment—but it's where the concrete examples live.
🔗 Jurisprudential Analysis (CTI_WOUND:001.REC)
THE CORE ARGUMENT IN BRIEF
The product: ChatGPT, marketed as an AI assistant for intellectual collaboration.
The design choice: Mental health safety classifiers optimized for recall (catching crises) over precision (avoiding false positives).
The admission: OpenAI's own documentation states: "To get useful recall, we have to tolerate some false positives."
The harm: Healthy users engaged in complex, intensive, or non-normative cognitive work are systematically misclassified as experiencing mental health crises—triggering unsolicited interventions, pathologizing responses, and degraded service.
What I think this means legally: The admission seems to establish knowledge, foreseeability, and calculated acceptance of harm to an identifiable group. But I don't know if that translates to actual liability theories, or if I'm misreading how these elements function in practice.
The remediation question: Feasible, low-cost design changes seem available (user mode declaration, opt-out mechanisms). They haven't been implemented. Does that matter legally? I don't know.
The scale: 700+ million weekly users. If there's something here, it's not small.
WHAT I'M HOPING YOU CAN TELL ME
I'm genuinely uncertain whether this framework identifies something legally real or whether I've built an elaborate structure that doesn't map onto how liability actually works.
Specific questions I'm wrestling with:
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Does the "false positive confession" actually function as an admission in the way I think it does? Or am I misunderstanding how knowledge/foreseeability work in practice?
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Is "harm" here legally cognizable? Interrupted work, pathologizing responses, emotional distress from misrecognition—does any of that translate to something courts recognize?
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Am I missing something obvious? Either something that makes this stronger than I realize, or something that makes it a non-starter?
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Is this novel in ways that help or hurt? New territory can mean opportunity or can mean "no precedent, no case."
I'm not asking you to validate my conclusions. I'm asking you to tell me what I'm not seeing.
If your honest answer is "this is interesting theoretically but has no legal legs," that's valuable information. I'd rather know that than continue building in a direction that doesn't lead anywhere.
CONTACT
[Your contact information]
Dossier prepared December 2025 Documentation reference: CTI_WOUND:001
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