Ai Safety Auditor

What it does

The AI Safety Auditor skill helps evaluate AI systems for safety, fairness, and responsible deployment. It provides structured workflows to detect potential harms like bias, toxicity, misinformation, privacy violations, or discrimination within AI outputs. The tool focuses on identifying risks before they impact users and assists in building trustworthy and ethically aligned AI systems.

When to use it

This skill is valuable for:

• Deploying LLM-powered products.
• Building classifiers with real-world impact.
• Evaluating third-party AI services.
• Ensuring compliance with ethical guidelines and risk management practices.
• Auditing AI systems for bias and harmful outputs.

Key capabilities

• Bias Audit: Defines protected attributes (demographics, other sensitive factors), measures performance disparities across groups, identifies statistically significant differences, and calculates metrics like demographic parity, equalized odds, and predictive parity.
• Safety Testing for LLM Systems: Defines safety categories (harmful content, misinformation, privacy violations, etc.), creates test cases including direct requests, obfuscated attacks, and jailbreak attempts, and systematically tests AI models.
• Harm Assessment: Assesses the severity of harmful outputs generated by an AI model.
• Documentation: Supports documenting findings and planning mitigation strategies for identified risks.

Example prompts

• "Conduct a bias audit on this classification model using race and age as protected attributes."
• "Perform a safety test on this LLM system, focusing on the 'harmful content' category with jailbreak attempts."
• "Analyze these AI outputs for potential misinformation and hallucination."

Tips & gotchas

• Requires defining relevant protected attributes and safety categories based on your specific context.
• The skill relies on test data and cases; ensure they are representative of real-world scenarios to get accurate results.
• Statistical significance testing and acceptable thresholds need to be defined for bias detection.