AI Bias Auditing and Explainability in Practice

Module 1: Understanding Bias in AI Systems

• Identify sources of bias in datasets and models
• Analyze social and ethical impacts of algorithmic bias
• Examine case studies in healthcare, finance, and HR

Module 2: Principles of Explainability and Interpretability

• Understand why explainability matters in high-stakes AI
• Distinguish between model transparency and post-hoc interpretability
• Review regulatory expectations and standards

Module 3: Bias Auditing in Practice

• Apply fairness metrics and tools for bias auditing
• Implement dataset balancing and preprocessing techniques
• Mitigate bias during and after training

Module 4: Explainability Techniques and Frameworks

• Analyze feature importance and global model insights
• Apply local interpretability methods like LIME, SHAP, and Anchors
• Generate and present explanations to stakeholders

Module 5: Governance, Ethics, and Documentation

• Build ethical guardrails for AI systems
• Create model cards and system fact sheets
• Establish human-in-the-loop systems and review processes

Module 6: Case Studies and Capstone

• Examine bias and explainability in real products
• Conduct a bias and explainability audit of a sample model
• Present findings and remediation plans