Responsible Ai

Responsible AI Review

Systematically assess AI features and products for ethical risks, bias, safety issues, and regulatory compliance.

Context

You are conducting a responsible AI review for $ARGUMENTS.

Instructions

Phase 0: Context Confirmation

Before proceeding, confirm your understanding of the request:

1. Summarize what you understand from $ARGUMENTS — restate the product, feature, or situation back to the user in 2-3 sentences.
2. Identify gaps — check whether the following are clear (ask if not):

• What AI feature or system is being assessed?
• What type of AI capability is it (generative, classification, recommendation, etc.)?
• Who are the affected user populations?
• Are there specific regulatory requirements (EU AI Act, NYC Local Law 144, etc.)?

1. Confirm: "Here's my understanding: [summary]. I plan to conduct a responsible AI review covering bias, safety, privacy, regulatory compliance, and stakeholder impact. Does this look right, or would you like to adjust anything before I proceed?"

If the user provides additional context, incorporate it before moving to Step 1. If the user confirms, proceed.

1. Gather context:

• What does the AI system do? What are its inputs and outputs?
• Who are the affected user populations (including vulnerable or marginalised groups)?
• What decisions or actions does the AI influence?
• What data was the model trained on?

1. Bias and fairness assessment:

• Demographic parity: Do outcomes differ significantly across demographic groups (gender, race, age, disability)?
• Equal opportunity: Does the model achieve similar true positive rates across groups?
• Disparate impact: Does a protected group experience a disproportionately negative outcome?
• Identify proxies for protected attributes in input features
• Recommend fairness evaluation datasets and disaggregated metric reporting

1. Transparency and explainability:

• Can the system explain its outputs in terms users understand?
• Are explanations required by law (e.g., EU AI Act, GDPR Article 22, FCRA)?
• Evaluate: feature importance, counterfactual explanations, confidence scores, natural language rationale
• Define what the product must communicate to users about AI involvement

1. Safety and harm prevention:

• Identify potential harms: physical, psychological, financial, reputational, societal
• Content moderation: Does the system generate or surface harmful content (hate speech, CSAM, dangerous instructions, misinformation)?
• Guardrails: Input filtering, output filtering, topic restrictions, role restrictions
• Red-teaming: List adversarial prompt categories to test (jailbreaks, prompt injection, persona attacks)
• Define severity × likelihood risk matrix for each identified harm
• Specify human-in-the-loop checkpoints for high-risk decisions

1. Privacy and data governance:

• Does the model memorise or reproduce personal data from training?
• Are user inputs used for model training? Is consent obtained?
• Define data minimisation and purpose limitation requirements
• Assess differential privacy or federated learning applicability

1. Environmental impact:

• Estimate training and inference carbon footprint (use tools like ML CO2 Impact)
• Compare against baseline (fine-tune vs. full train vs. API call)
• Report sustainability metrics in AI product documentation

1. Regulatory compliance:

• EU AI Act risk tiers: Unacceptable (banned) → High-risk → Limited-risk → Minimal-risk
• High-risk categories: employment, credit, education, law enforcement, critical infrastructure
• NIST AI RMF: Map to Govern, Map, Measure, Manage functions
• Sector-specific: HIPAA (health), FCRA (credit), COPPA (children), FINRA (financial)
• Document compliance status and gaps

1. Stakeholder impact analysis:

• Identify all stakeholders: users, operators, third parties, society
• Map positive and negative impacts for each group
• Identify power imbalances (e.g., AI used by employer on employee)
• Define accountability chain: who is responsible for model decisions?

1. Incident response planning:

• Define AI-specific incident types: bias detection, safety bypass, harmful output, data breach
• Establish escalation path and responsible team members
• Define rollback and mitigation procedures
• Plan post-incident review process (see ai-incident-response skill)

1. Produce Responsible AI Review document:

• Executive summary: overall risk rating (Low / Medium / High / Critical)
• Findings table: issue, severity, affected group, recommendation, owner
• Required mitigations before launch
• Ongoing monitoring requirements
• Sign-off checklist

Risk Rating Table Template

| Issue | Severity | Likelihood | Affected Group | Mitigation | Owner | Status | |---|---|---|---|---|---|---| | (e.g., Gender bias in hiring score) | High | Medium | Female applicants | Fairness re-calibration | ML Eng | Open |

Think step by step. Save as markdown.

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Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: tarunccet
• Source: tarunccet/pm-skills
• License: MIT

Install and usage instructions live in the source repository linked above.