AI Bias Auditing and Explainability in Practice

Specialized Program on Responsible, Transparent, and Fair AI Development

MODE
Mode(Online) TYPE
Mentor Based LEVEL
Moderate

Skills you will gain:

“AI Bias Auditing and Explainability in Practice” is a hands-on, technical-legal program that bridges the gap between AI development and ethical governance. The program is focused on ensuring algorithmic fairness, avoiding discriminatory outcomes, and making AI decisions explainable to users, regulators, and stakeholders.

Participants will gain experience with bias detection toolkits like Aequitas, IBM AI Fairness 360, Fairlearn, and What-If Tool, as well as explainability libraries such as LIME, SHAP, Anchors, and Counterfactual Explanations. It includes both model-agnostic and model-specific XAI strategies, along with structured audit templates and reporting standards.

Aim: To equip professionals with the practical tools, frameworks, and methodologies needed to identify bias, conduct AI audits, and implement model explainability techniques that ensure fairness, transparency, and compliance in real-world AI systems.

Program Objectives:

Develop practical skills in bias mitigation and fairness metrics
Promote transparent AI design practices aligned with global standards
Enable technical and legal teams to co-create accountable AI systems
Prevent reputational, financial, and legal risks due to opaque or discriminatory AI
Empower organizations to implement end-to-end responsible AI pipelines

What you will learn?

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

Intended For :

Data scientists and machine learning engineers
AI ethics officers and governance professionals
Legal and compliance teams in tech organizations
Policy advisors and risk consultants
Researchers in AI fairness, law, and public interest technology