AQ'S Corner

Documenting a Confirmed Governance Gap

Addressed to OpenAI

I am publishing this open letter to document a confirmed limitation in how representational bias and fairness risk are handled within user-facing AI reporting systems.

This letter follows a sequence of responsible actions: direct system use, an attempt to report the observed issue through available channels, a policy-focused follow-up, and a written response from OpenAI support clarifying current reporting constraints. The purpose of this letter is not to assign blame or provoke response, but to establish a clear public record of a governance gap that has now been explicitly acknowledged.

Background

During normal use of an AI image generation system, I asked the model to infer what I look like based on my language, professional experience, and technical authority. The system generated an image of a man. When asked why, the system explained that it associated authority, leadership, decisiveness, and technical fluency with male representation.

The system acknowledged the assumption when questioned.

This interaction was documented in a prior article as an example of representational bias and authority attribution bias emerging through ordinary, non-malicious use.

Attempted Reporting and Outcome

Following the interaction, I attempted to report the issue through the platform’s reporting mechanisms. I found that there was no category to report representational bias, fairness risk, or authority attribution bias. The closest available option was “I just don’t like it,” which did not accurately describe the issue being reported.

The report was acknowledged and reviewed, with a determination that no policy violation had occurred.

Subsequently, I followed up with a policy-focused inquiry requesting clarification on how representational bias should be reported when it does not constitute abuse, illegality, or unsafe content.

OpenAI support responded in writing, confirming that there is currently no dedicated channel for escalating structural or design-level bias as a distinct class of issue. User-facing reporting tools are intentionally scoped to capture explicit policy violations, and feedback related to fairness or representational harm must be submitted through existing categories, where it is reviewed by moderation and quality teams.

This response confirmed the precise limitation I was attempting to surface.

The Confirmed Governance Gap

The core issue is not the moderation outcome. It is the absence of a user-facing mechanism to report representational bias as a first-class governance signal.

When representational bias emerges through ordinary use, users have no way to accurately categorize or escalate it. As a result, this class of risk is filtered out of structured feedback loops, even when acknowledged and submitted in good faith.

This is not a moderation failure. It is a system design decision.

Why This Matters

AI systems of this class are increasingly used to support perception, evaluation, and decision-making in contexts such as hiring, leadership assessment, content creation, and professional representation. Representational defaults embedded in these systems influence downstream outputs and expectations, even in the absence of malicious intent or explicit policy violations.

From a governance perspective, this aligns with fairness and representational harm risks described in frameworks such as the NIST AI Risk Management Framework, particularly in cases where risks cannot be reported, measured, or operationalized through user-facing mechanisms. Risks that cannot be captured cannot be meaningfully managed.

The absence of a reporting pathway does not eliminate the risk. It obscures it.

Purpose of This Letter

This letter exists to document a confirmed limitation in current AI governance and reporting design. It reflects the outcome of responsible use, attempted reporting, escalation, and clarification.

It is published publicly to establish a record, not to escalate conflict. I appreciate the clarity provided by OpenAI support regarding the current scope of reporting tools. At the same time, that clarity underscores the need for further consideration of how representational bias and fairness risk are operationalized in systems deployed at scale.

Closing

Bias in AI systems is not always overt. Often, it is patterned, predictable, and normalized. Addressing it requires not only improving model behavior, but also designing feedback and reporting systems capable of receiving the signal when it appears.

This letter documents a confirmed gap in that capability.

I remain open to constructive dialogue on this issue and to future improvements in how representational bias is surfaced, categorized, and governed.

Sincerely,
Aqueelah Emanuel
Cybersecurity and AI Governance Practitioner
AQ’s Corner LLC

Following a policy-focused inquiry, OpenAI support confirmed in writing that there is currently no dedicated user-facing channel for reporting representational or structural bias as a distinct class of issue. Reporting tools are intentionally scoped to explicit policy violations, and feedback related to fairness or representational harm must be submitted through existing categories.

Related documentation

• Part One: ChatGPT Turned Me Into a Man, Then Explained Why
  https://aqscorner.com/2026/01/04/chatgpt-turned-me-into-a-man-then-explained-why/
• Part Two: When I Tried to Report AI Bias, There Was No Place to Put It
  https://aqscorner.com/2026/01/04/when-i-tried-to-report-ai-bias-there-was-no-place-to-put-it/