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responsible-ai-audit

Audits AI systems against responsible AI principles and produces structured risk reports.

1|Updated Oct 11, 2025
Search Tags:#transparency#risk assessment#fairness#bias detection#ai audit#ai governance#responsible ai
One-click install
npx skills add https://github.com/codewizwit/human-in-the-loop --skill responsible-ai-audit-codewizwit
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: responsible-ai-audit
Source: https://github.com/codewizwit/human-in-the-loop/tree/main/lib/skills/responsible-ai-audit
Command: npx skills add https://github.com/codewizwit/human-in-the-loop --skill responsible-ai-audit-codewizwit

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve? Teams shipping AI features often lack a structured way to evaluate fairness, transparency, safety, and accountability before deployment, leading to unreviewed risks reaching users. ## Core Features & Use Cases - Lens-by-Lens Evaluation: Scores AI systems across accuracy, fairness, transparency, accountability, safety, and privacy through four accountability lenses. - Risk Assessment & Deployment Decision: Produces a risk level, prioritized mitigations, and a Deploy / Deploy with Conditions / Do Not Deploy recommendation. - Use Case: Before launching an AI code review bot that auto-approves pull requests, run an audit to identify missing human oversight, get a deployment decision, and receive a monitoring plan with metrics and review frequency. ## Quick Start Ask the assistant to audit your AI system for responsible AI compliance, for example: audit this AI content moderation pipeline for bias and fairness before launch.

Frequently Asked Questions about responsible-ai-audit

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I audit an AI system for responsible AI compliance?▼

Describe the AI system, its purpose, and risk level when prompted. The audit evaluates accuracy, fairness, transparency, accountability, safety, and privacy, then delivers a report with findings, mitigations, and a deployment recommendation.

What does a responsible AI audit report include?▼

The report includes an executive summary, lens-by-lens analysis with Pass or Fail scores, a risk assessment with prioritized mitigations, a deployment decision with conditions, and a monitoring plan with metrics and review frequency.

Can I audit a specific AI output instead of a full system?▼

Yes. The audit supports three scopes: an AI-powered tool or feature, a specific AI-generated output, or an AI/ML pipeline. You select the scope during the initial context-gathering step.

When should I run a responsible AI audit?▼

Run it before deploying any AI feature that impacts people or decisions, when reviewing bias or fairness concerns, or when assessing governance readiness. High-risk systems should not deploy without audit conditions being met.

What are the limitations of an AI ethics audit?▼

The audit evaluates documented behavior and implementation evidence, not runtime model performance across all populations. It does not replace ongoing monitoring, which is why every report includes a monitoring plan with success criteria.

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