ai-ethics_skill

Overview

This skill helps teams build and evaluate AI systems with ethical principles in mind, focusing on bias detection, fairness, transparency, privacy, and governance. It provides practical methods for measuring disparate impact, documenting model decisions, and aligning systems with regulatory requirements. Use it to turn ethical goals into repeatable processes and concrete artifacts.

How this skill works

The skill inspects model datasets, training pipelines, evaluation metrics, and deployment processes to surface sources of bias and risk. It recommends mitigation strategies across pre-, in-, and post-processing, and suggests explainability techniques and documentation templates such as model cards. It also maps system risk to governance controls and human oversight patterns for operational use.

When to use it

• Evaluating models for bias before release or retraining
• Designing fairness constraints into model training
• Preparing compliance evidence for high-risk AI (e.g., EU AI Act)
• Creating transparency artifacts like model cards and explanations
• Setting up human-in-the-loop workflows for high-stakes decisions

Best practices

• Measure fairness using multiple metrics (group and individual) and report subgroup performance
• Apply mitigation at the earliest stage possible (data reweighting/augmentation) and validate downstream effects
• Document purpose, data provenance, limitations, and update history in a model card
• Design clear human oversight: escalation paths, override controls, and audit trails
• Minimize data collection and use privacy-preserving techniques where feasible

Example use cases

• Bias audit for a hiring recommender showing disparate selection rates by gender
• Implementing threshold adjustments and calibration to reduce disparate impact in lending models
• Producing model cards and local explanations for a medical triage system
• Defining governance workflows and risk categorization for enterprise AI deployments
• Designing federated learning and differential privacy for a consumer data product