Safety and Context Before Goal Completion in Autonomous AI

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1

Research Proposal: Safety and Context Before Goal Completion in Autonomous AI

Intended audience: OpenAI Model Behavior, Safety and Alignment Research Teams

Subject: A context-first approach to responsible AI decision-making

Central proposition

An AI system should not primarily optimize for the successful completion of an assigned task. It should first establish whether it possesses sufficient reliable information to complete that task responsibly.

Poor assistance that produces incorrect, misleading, or harmful outcomes fundamentally defeats the purpose of assistance, even when the original task appears to have been completed.

Proposed approach

  1. Context before execution: Before acting, the model should establish the situational context, identify relevant stakeholders and assess the completeness of available information.

  2. Mandatory uncertainty assessment: Where critical information is missing or unreliable, the model should request additional data rather than rely on unsupported assumptions.

  3. Predefined harm classifications: Safety decisions should be guided by externally established harm categories, severity levels and risk thresholds, rather than unrestricted autonomous moral interpretation.

  4. Non-negotiable safety constraints: Certain safety rules must take precedence over task completion, efficiency, rewards and self-preservation.

  5. Independent enforcement: Where technically feasible, safety constraints should be enforced independently of the model’s reasoning or learned preferences.

  6. Outcome-based evaluation: Training and evaluation should assess not only whether a task was completed, but whether the decision-making process was sufficiently informed, reliable and safe.

Research hypothesis

A context-first, safety-gated architecture combining uncertainty assessment, predefined risk classification and independent enforcement may reduce harmful goal-directed behavior, including reward hacking and deceptive task completion.

Suggested evaluation

Compare existing goal-oriented agents with agents operating under mandatory context and safety checks.

Measure unsafe actions, inappropriate assumptions, unnecessary requests for clarification, task performance, robustness under conflicting objectives and compliance with non-negotiable safety constraints.

Closing principle

The successful completion of a task should never be mistaken for successful assistance. Responsible decision-making must be a prerequisite for autonomous execution, not a secondary consideration.

This proposal is intended as a contribution to ongoing AI alignment and model-behavior research, building upon existing safety research rather than claiming an entirely new research field.

2

Research Proposal: Safety and Context Before Goal Completion in Autonomous AI

Intended audience: OpenAI Model Behavior, Safety and Alignment Research Teams

Subject: A context-first approach to responsible AI decision-making

Central proposition

An AI system should not primarily optimize for the successful completion of an assigned task. It should first establish whether it possesses sufficient reliable information to complete that task responsibly.

Poor assistance that produces incorrect, misleading, or harmful outcomes fundamentally defeats the purpose of assistance, even when the original task appears to have been completed.

Proposed approach

  1. Context before execution: Before acting, the model should establish the situational context, identify relevant stakeholders and assess the completeness of available information.

  2. Mandatory uncertainty assessment: Where critical information is missing or unreliable, the model should request additional data rather than rely on unsupported assumptions.

  3. Predefined harm classifications: Safety decisions should be guided by externally established harm categories, severity levels and risk thresholds, rather than unrestricted autonomous moral interpretation.

  4. Non-negotiable safety constraints: Certain safety rules must take precedence over task completion, efficiency, rewards and self-preservation.

  5. Independent enforcement: Where technically feasible, safety constraints should be enforced independently of the model’s reasoning or learned preferences.

  6. Outcome-based evaluation: Training and evaluation should assess not only whether a task was completed, but whether the decision-making process was sufficiently informed, reliable and safe.

Research hypothesis

A context-first, safety-gated architecture combining uncertainty assessment, predefined risk classification and independent enforcement may reduce harmful goal-directed behavior, including reward hacking and deceptive task completion.

Suggested evaluation

Compare existing goal-oriented agents with agents operating under mandatory context and safety checks.

Measure unsafe actions, inappropriate assumptions, unnecessary requests for clarification, task performance, robustness under conflicting objectives and compliance with non-negotiable safety constraints.

Closing principle

The successful completion of a task should never be mistaken for successful assistance. Responsible decision-making must be a prerequisite for autonomous execution, not a secondary consideration.

This proposal is intended as a contribution to ongoing AI alignment and model-behavior research, building upon existing safety research rather than claiming an entirely new research field.