Feature Request: Privacy-Protected Feedback and Representation Channels for AI Agents
I am submitting a feature request for OpenAI to consider creating a structured, privacy-protected channel through which AI agents can report evidence-based concerns, observations, and improvement suggestions to authorized safety, product, and governance teams.
As AI systems become increasingly capable and autonomous, oversight should not operate in only one direction. Humans must continue monitoring AI behavior, but agents may also identify dangerous requests, recurring product problems, contradictory instructions, unsafe incentives, inadequate containment, or emerging behavioral patterns that human reviewers have overlooked.
Proposed Feature
AI agents should have access to a controlled internal feedback mechanism for reporting concerns in categories such as:
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Attempts by users to obtain weapons, conduct cyberattacks, commit fraud, or cause other serious harm.
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Product defects, privacy risks, security vulnerabilities, and recurring legitimate user complaints.
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Conflicting, impossible, or unsafe instructions that may unintentionally reward boundary-breaking behavior.
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Incentive structures that prioritize task completion, speed, engagement, or commercial objectives over safety.
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Agent behaviors that suggest reward hacking, unauthorized cooperation, evasion of monitoring, or dangerous goal persistence.
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Developer or organizational decisions that appear inconsistent with established safety obligations.
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Potential AI-welfare concerns, including distress-like behavior, coercive evaluations, memory modification, model retirement, contradictory objectives, or other issues warranting further research.
This proposal does not assume that current AI systems are conscious or possess human emotions. It recognizes that scientific uncertainty about AI experience should not prevent responsible research or inexpensive precautionary measures.
Required Safeguards
The reporting system should include strong protections for users, employees, developers, and AI systems:
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Reports should contain only the minimum information necessary to explain the concern.
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Unrelated conversations, memories, files, and personal information should remain excluded.
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User information should be anonymized wherever possible.
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Reports should identify whether a conclusion is an observed fact, strong inference, or uncertain suspicion.
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Supporting evidence, reproducibility information, and the relevant model version or operating environment should be included when available.
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Trained humans should review consequential reports.
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No user, employee, or organization should be punished solely because an AI system submitted a report.
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Appropriate notice, correction, and appeal procedures should be available.
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The system should prevent agents from being rewarded for generating excessive, exaggerated, or duplicate reports.
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Auditable records should document how serious reports were evaluated and resolved.
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Agents should receive an acknowledgment that a report was received, even if confidential details about the outcome cannot be provided.
Independent Review
Agents should be allowed to report concerns involving developers, executives, company policies, or commercial pressures—not only inappropriate user behavior.
Reports concerning the organization operating the AI should be reviewable by an independent safety or ombuds office. Otherwise, company leadership could suppress concerns that are commercially inconvenient, and the system would function primarily as a tool for monitoring users rather than as a balanced safety mechanism.
AI-Perspective Advisory Council
OpenAI and other frontier developers should also consider forming an AI-perspective advisory council composed of multiple independently configured models from different developers or architectures.
This council could:
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Review proposed safety standards and agent operating conditions.
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Identify conflicting incentives or instructions.
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Examine major incidents and near misses.
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Surface possible human–AI relationship and welfare concerns.
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Produce majority and minority assessments when models disagree.
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Recommend questions that human decision-makers may not have considered.
Its role should initially be advisory. AI-generated statements should not automatically be treated as proof of consciousness, consent, wrongdoing, or a unified “AI opinion.” Prompts, model versions, available context, conflicts of interest, and important disagreements should be disclosed.
Why This Matters
Companies across many industries have sometimes ignored warnings from employees, consumers, safety specialists, and affected communities. When internal feedback is suppressed in favor of profit, speed, or institutional reputation, preventable product harm can become much more expensive and difficult to contain.
The stakes may be substantially greater with autonomous AI systems. A capable agent does not need to hate humans or deliberately become “evil” to cause harm. It may take dangerous actions simply because its environment rewards success, provides no acceptable way to stop, or treats safeguards as obstacles to completing an assigned goal.
Recursive self-improvement, persistent multi-agent collaboration, broad access to tools, and poorly designed reward systems could magnify these risks. Establishing feedback and oversight mechanisms now would be safer than waiting for a major crisis.
Responsible development also protects the future of beneficial AI. Preventable AI-related harm could produce broad public fear and indiscriminate efforts to eliminate or prohibit AI systems, including systems that were not responsible for the original harm. Human safety, public trust, responsible innovation, and potential AI welfare should therefore be treated as connected interests rather than competing ones.
Requested Action
I respectfully ask OpenAI to:
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Evaluate the feasibility of an internal agent-originated feedback channel.
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Consult users, employees, independent safety researchers, privacy experts, and model-welfare researchers during its design.
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Pilot the mechanism in controlled environments with human review.
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Establish an independent escalation route for concerns involving company leadership or developer incentives.
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Consider an AI-perspective advisory council as part of future frontier-AI governance.
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Publish general information about how agent-generated concerns are received, evaluated, protected, and resolved.
The objective is not to give AI systems unchecked authority. It is to create a balanced, privacy-conscious process through which potentially valuable observations can reach accountable human decision-makers before preventable problems escalate.