AI Bias Auditing and Explainability in Practice

Aim

Program Objectives

Program Structure

Week 1: Foundations of AI Bias and Explainability
Module 1: Understanding Bias in AI Systems

• Chapter 1.1: What is AI Bias? Definitions and Categories
• Chapter 1.2: Sources of Bias in Datasets and Models
• Chapter 1.3: Social and Ethical Impacts of Algorithmic Bias
• Chapter 1.4: Case Studies in Healthcare, Finance, and HR

Module 2: Principles of Explainability and Interpretability

• Chapter 2.1: Why Explainability Matters in High-Stakes AI
• Chapter 2.2: Model Transparency vs. Post-Hoc Interpretability
• Chapter 2.3: Regulatory Expectations (EU AI Act, FTC, EEOC, etc.)
• Chapter 2.4: Trade-offs Between Accuracy and Interpretability

Week 2: Methods and Tools for Bias Detection and Explainability
Module 3: Bias Auditing in Practice

• Chapter 3.1: Fairness Metrics: Demographic Parity, Equal Opportunity, etc.
• Chapter 3.2: Tools for Bias Auditing: AIF360, Fairlearn, What-If Tool
• Chapter 3.3: Dataset Balancing and Preprocessing Techniques
• Chapter 3.4: Bias Mitigation During and After Training

Module 4: Explainability Techniques and Frameworks

• Chapter 4.1: Feature Importance and Global Model Insights
• Chapter 4.2: LIME, SHAP, and Anchors: Local Interpretability Methods
• Chapter 4.3: Explaining Black-Box Models vs. Interpretable ML
• Chapter 4.4: Generating and Presenting Explanations to Stakeholders

Week 3: Strategy, Policy, and Real-World Implementation
Module 5: Governance, Ethics, and Documentation

• Chapter 5.1: Building Ethical Guardrails for AI Systems
• Chapter 5.2: Model Cards and System Fact Sheets
• Chapter 5.3: Human-in-the-Loop Systems and Review Processes
• Chapter 5.4: Internal Accountability and Reporting Frameworks

Module 6: Case Studies and Capstone

• Chapter 6.1: Bias and Explainability in Real Products (Banking, Hiring, Healthcare)
• Chapter 6.2: Organizational Structures for Responsible AI
• Chapter 6.3: Capstone: Conduct a Bias + Explainability Audit of a Sample Model
• Chapter 6.4: Presenting Findings and Remediation Plans

Who Should Enrol?

Program Outcomes

Fee Structure

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What You’ll Gain