--- name: validating-ai-ethics-and-fairness-3a86e646f7d2 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:*) version: 1.0.0 author: Jeremy Longshore license: MIT --- # Ai Ethics Validator This skill provides automated assistance for ai ethics validator tasks. ## 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 ## Overview This skill provides automated assistance for ai ethics validator tasks. This skill provides automated assistance for the described functionality. ## Examples Example usage patterns will be demonstrated in context. --- Source attribution: Dicklesworthstone Original project: https://github.com/Dicklesworthstone/pi_agent_rust/blob/f28fc46ee0db8e710d95b8b55592cd38d60f2b26/tests/ext_conformance/artifacts/plugins-community/plugins/ai-ml/ai-ethics-validator/skills/validating-ai-ethics-and-fairness/SKILL.md License: MIT License information: https://opensource.org/license/mit Original upstream instructions; downloaded from Mega AI Hub. ## Upstream license notice MIT License Copyright (c) 2025 Claude Code Plugins Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. # Mega AI Hub resource metadata Creator / publisher: Dicklesworthstone Original project: https://github.com/Dicklesworthstone/pi_agent_rust/blob/f28fc46ee0db8e710d95b8b55592cd38d60f2b26/tests/ext_conformance/artifacts/plugins-community/plugins/ai-ml/ai-ethics-validator/skills/validating-ai-ethics-and-fairness/SKILL.md Attribution basis: Source repository record Fields of work: Data & Analytics, Development & Engineering, Legal, Product Management Roles: Compliance Officer, Data Analyst, Data Scientist, ML / AI Engineer, Product Manager Specialties: Specialty not specified Tools: Aif360, Fairlearn Country applicability: Country not specified Classification basis: Source catalog labels Instruction SHA-256: 80033d33f0298b99be8af48c459a527ce85f0783993428faa618c53aaed10c00