ai-ethics-validator

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name: validating-ai-ethics-and-fairness version: 1.0.0 description: | Validate AI/ML models and datasets for bias, fairness, and ethical concerns. Use when auditing AI systems for ethical compliance, fairness assessment, or bias detection. Trigger with phrases like "evaluate model fairness", "check for bias", or "validate AI ethics". allowed-tools: Read, Write, Edit, Grep, Glob, Bash(python:*) license: MIT

Prerequisites

Before using this skill, ensure you have:

• Access to the AI model or dataset requiring validation
• Model predictions or training data available for analysis
• Understanding of demographic attributes relevant to fairness evaluation
• Python environment with fairness assessment libraries (e.g., Fairlearn, AIF360)
• Appropriate permissions to analyze sensitive data attributes

Instructions

Step 1: Identify Validation Scope

Determine which aspects of the AI system require ethical validation:

• Model predictions across demographic groups
• Training dataset representation and balance
• Feature selection and potential proxy variables
• Output disparities and fairness metrics

Step 2: Analyze for Bias

Use the skill to examine the AI system:

1. Load model predictions or dataset using Read tool
2. Identify sensitive attributes (age, gender, race, etc.)
3. Calculate fairness metrics (demographic parity, equalized odds, etc.)
4. Detect statistical disparities across groups

Step 3: Generate Validation Report

The skill produces a comprehensive report including:

• Identified biases and their severity
• Fairness metric calculations with thresholds
• Representation analysis across demographic groups
• Recommended mitigation strategies
• Compliance assessment against ethical guidelines

Step 4: Implement Mitigations

Based on findings, apply recommended strategies:

• Rebalance training data using sampling techniques
• Apply algorithmic fairness constraints during training
• Adjust decision thresholds for specific groups
• Document ethical considerations and trade-offs

Output

The skill generates structured reports containing:

Bias Detection Results

• Statistical disparities identified across groups
• Severity classification (low, medium, high, critical)
• Affected demographic segments with quantified impact

Fairness Metrics

• Demographic parity ratios
• Equal opportunity differences
• Predictive parity measurements
• Calibration scores across groups

Mitigation Recommendations

• Specific technical approaches to reduce bias
• Data augmentation or resampling strategies
• Model constraint adjustments
• Monitoring and continuous evaluation plans

Compliance Assessment

• Alignment with ethical AI guidelines
• Regulatory compliance status
• Documentation requirements for audit trails

Error Handling

Common issues and solutions:

Insufficient Data

• Error: Cannot calculate fairness metrics with small sample sizes
• Solution: Aggregate related groups or collect additional data for underrepresented segments

Missing Sensitive Attributes

• Error: Demographic information not available in dataset
• Solution: Use proxy detection methods or request access to protected attributes under appropriate governance

Conflicting Fairness Criteria

• Error: Multiple fairness metrics show contradictory results
• Solution: Document trade-offs and prioritize metrics based on use case context and stakeholder input

Data Quality Issues

• Error: Inconsistent or corrupted attribute values
• Solution: Perform data cleaning, standardization, and validation before bias analysis

Resources

Fairness Assessment Frameworks

• Fairlearn library for bias detection and mitigation
• AI Fairness 360 (AIF360) toolkit for comprehensive fairness analysis
• Google What-If Tool for interactive fairness exploration

Ethical AI Guidelines

• IEEE Ethically Aligned Design principles
• EU Ethics Guidelines for Trustworthy AI
• ACM Code of Ethics for AI practitioners

Fairness Metrics Documentation

• Demographic parity and statistical parity definitions
• Equalized odds and equal opportunity metrics
• Individual fairness and calibration measures

Best Practices

• Involve diverse stakeholders in fairness criteria selection
• Document all ethical decisions and trade-offs
• Implement continuous monitoring for fairness drift
• Maintain transparency in model limitations and biases