Individual skill
validating-ai-ethics-and-fairness
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".
plugins/ai-ml/ai-ethics-validator/skills/validating-ai-ethics-and-fairness/SKILL.md
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- ai Repository topic
- automation Repository topic
- claude-code Repository topic
- devops Repository topic
- mcp Repository topic
- ai-agents Repository topic
- anthropic Repository topic
- claude-code-plugins Repository topic
- developer-tools Repository topic
- llm Repository topic
- ai Declared skill tag
- compliance Declared skill tag
- audit Declared skill tag
- security Derived category
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SKILL.mdcore · 6,064 bytesassets/README.mddoc · 287 bytesassets/example_dataset.csvasset · 2,355 bytesassets/example_model.pklasset · 1,819 bytesassets/report_template.mddoc · 7,161 bytesreferences/README.mddoc · 62 bytesscripts/README.mddoc · 386 bytesscripts/generate_report.pyruns · 11,058 bytesscripts/validate_dataset.pyruns · 12,178 bytesscripts/validate_model.pyruns · 9,275 bytes
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# AI Ethics Validator
## Overview
Validate AI/ML models and datasets for bias, fairness, and ethical compliance using quantitative fairness metrics and structured audit workflows.
## Prerequisites
- Python 3.9+ with Fairlearn >= 0.9 (`pip install fairlearn`)
- IBM AI Fairness 360 toolkit (`pip install aif360`) for comprehensive bias analysis
- pandas, NumPy, and scikit-learn for data manipulation and model evaluation
- Model predictions (probabilities or binary labels) and corresponding ground truth labels
- Demographic attribute columns (age, gender, race, etc.) accessible under appropriate data governance
- Optional: Google What-If Tool for interactive fairness exploration on TensorFlow models
