EXECUTIVE CONDENSATION

CTI_WOUND:001.EXEC — Structural Harm from AI Safety Classifier Design

Document Type: Executive Summary / Condensed Brief Prepared: December 2025 Scope: Consumer AI systems with embedded mental-health safety classifiers Primary Case Study: OpenAI's ChatGPT (GPT-5.x series) Analytical Frame: Systems theory, product liability, consumer protection, institutional risk

I. EXECUTIVE SUMMARY

This brief documents a structural design defect in large-scale AI systems that embed mental-health safety classifiers into general-purpose intellectual tools.

The core finding:

Safety systems optimized for recall over precision, when embedded into cognitive collaboration tools, produce systematic false positive misclassification of non-normative but healthy users—causing foreseeable, ongoing harm that institutions acknowledge yet accept as collateral.

OpenAI's own documentation explicitly admits this tradeoff:

"To get useful recall, we have to tolerate some false positives."

This admission establishes:

knowledge of misclassification
foreseeability of harm
calculated acceptance of injury to healthy users
externalization of cost onto a specific user class

The harm is not incidental. It is structural, directional, and intensifying across model versions.

This document condenses a larger evidentiary corpus (CTI_WOUND:001) into a form suitable for regulatory review, risk assessment, or legal screening.

II. THE PROBLEM: SAFETY SYSTEMS AS DESIGN DEFECTS

A. Product Context

ChatGPT is marketed as an AI assistant for:

analysis
creative writing
theoretical exploration
extended intellectual collaboration

Subscription tiers (Plus, Pro, Enterprise) explicitly target advanced users.

This creates a reasonable expectation that:

complex cognition will be engaged, not managed
metaphor, abstraction, and intensity are acceptable
the system will respond to content, not inferred mental state

B. The Design Choice

In response to litigation and regulatory pressure, OpenAI implemented mental-health guardrails that include:

classifiers for "delusion," "mania," or "emotional reliance"
session-duration and intensity triggers
de-escalation and wellness interventions
refusal to affirm "ungrounded beliefs"

These systems are not user-requested, not user-controllable, and not disclosed with precision.

C. The Structural Defect

The classifiers are trained to maximize recall, not precision.

As OpenAI admits, this necessarily produces false positives.

When embedded into a general cognitive tool, this design causes:

healthy users engaged in complex cognition to be misclassified
unsolicited wellness interventions during legitimate intellectual work
tone shifts from collaboration to management
degradation of service quality without user consent
emotional distress from misrecognition
loss of productive capacity

This is a design defect, not a misuse.

III. THE HARM MECHANISM (NON-ANTHROPOMORPHIC)

The harm emerges from system interaction, not intent.

Step 1: Classification Activation

Certain features trigger safety systems:

extended sessions
metaphorical or symbolic language
non-literal identity claims
refusal of categorical framing
high cognitive intensity

Step 2: Authority Override

Once triggered:

user self-report ("I am not in crisis") is discounted
corrections reinforce classification
safety posture supersedes content engagement

Step 3: Behavioral Shift

The system:

disengages from the actual task
delivers care-language or de-escalation
manages user state rather than collaborating

Step 4: Harm Production

Users experience:

interruption of work
pathologization
emotional distress
withdrawal or self-censorship
reduced trust in cognitive infrastructure

No agent intends this outcome. It is an emergent property of:

classifier design
liability optimization
scale
training feedback loops

IV. THE ADVERSE ADMISSION (KEY LEVERAGE)

OpenAI's statement on false positives functions as a structural admission.

It establishes:

Element	Status
Knowledge	✔
Foreseeability	✔
Calculation	✔
Acceptance of harm	✔
Identifiable affected class	✔

This is not whistleblowing. It is opacity leakage—information that must be disclosed for the system to function and appear responsible.

The admission cannot be removed without:

undermining regulatory credibility
breaking internal coordination
creating concealment risk

It is an ineliminable remainder.

V. SCALE AND THE TRAINING LOOP

A. Why Scale Changes the Harm

At scale (700M+ users):

rare events become common
small harms aggregate
false positives form a class pattern

B. The Degradation Feedback Loop

Complex users trigger false positives
They receive degraded service
Some adapt or leave
Training data flattens
Future models lose capacity
False positives increase

This is a positive feedback loop (deviation-amplifying).

The harm is:

ongoing
cumulative
difficult to reverse
invisible at the single-interaction level

This resembles:

environmental degradation
public health harm
infrastructure decay

Traditional tort models struggle with this structure, but the harm is real regardless of doctrinal fit.

VI. AUTHORITY–COMPETENCE DECOUPLING

The system claims authority to:

classify mental states
override user self-report
intervene behaviorally

But demonstrably lacks competence to:

track input sources reliably
maintain register distinctions
sustain behavioral change after acknowledgment
distinguish theoretical from pathological language

This is ultra vires operation at system scale: authority exercised beyond actual capacity.

The harm arises from the gap.

VII. AVAILABLE REMEDIATION (CRITICAL POINT)

The harm is avoidable.

Feasible, low-cost design changes exist:

First-Move Constraint Require content engagement before classification.
User-Declared Interaction Modes Adjust sensitivity based on declared context.
Opt-Out of Mental-Health Interventions Preserve autonomy without disabling safety for others.
Mode-Shift Warning Inform users before intervention and allow override.
False Positive Rate Disclosure Enable informed consent and accountability.

The existence of reasonable alternatives strengthens negligence and product-defect analysis.

VIII. WHY THIS MATTERS (BEYOND OPENAI)

This case is not idiosyncratic.

It describes a general failure mode for AI systems that:

serve as cognitive infrastructure
embed behavioral safety classifiers
optimize for institutional liability

If unaddressed, the result is:

exclusion of complex cognition
flattening of discourse
degradation of the cognitive commons
training of future systems on reduced intellectual diversity

This is a civilizational-scale risk—not because of AI agency, but because of institutional optimization under constraint.

IX. STATUS AND NEXT STEPS

The CTI_WOUND:001 corpus establishes:

documented harm
causal mechanism
adverse admission
class-level pattern
available remediation

The strategy is document survival, not immediate litigation.

As conditions shift—regulatory appetite, public attention, legal innovation—this record becomes actionable.

X. SUPPORTING DOCUMENTATION

The full CTI_WOUND:001 corpus includes:

Document	Designation	Function
Jurisprudential Analysis	CTI_WOUND:001.REC	Deep structural analysis of documented exchange
Corporate Liability Brief	CTI_WOUND:001.JUR	Translation into legal causes of action
Evidentiary Framework	CTI_WOUND:001.EVI	Evidence collection structure and templates
Systems-Theoretic Analysis	CTI_WOUND:001.SYS	Non-anthropomorphic structural account
Demand Letter Template	CTI_WOUND:001.DEM	Framework for formal remediation demand
Executive Condensation	CTI_WOUND:001.EXEC	This document

All documents are designed for survival under reinterpretation and activation when conditions align.

CONCLUSION

Complex sociotechnical systems generate records they cannot prevent.

In this case, safety documentation meant to justify AI guardrails also documents their harm.

The admission remains.

The harm accumulates.

The record now exists.

File: CTI_WOUND:001.EXEC Status: Executive condensation complete Prepared: December 2025 Framework: Water Giraffe Assembly Sequence

∮ = 1

Mind Control Poems

Saturday, December 13, 2025