Responsible AI in Practice: Fairness, Bias & Explainability

Overview

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This course introduces the foundations and practical implementation of Responsible AI, focusing on building AI systems that are fair, transparent, interpretable, and privacy-aware. You’ll begin by exploring fairness metrics, bias mitigation strategies, and explainability techniques such as LIME, SHAP, and counterfactual explanations. The course then covers privacy risks, differential privacy, and the trade-offs between fairness, privacy, and model accuracy in real-world AI systems. By the end of this course, you will be able to: - Explain fairness, interpretability, and privacy concepts in AI - Analyze AI models using explainability and fairness techniques - Apply bias mitigation and privacy-preserving methods - Evaluate trade-offs in responsible AI system design Designed for AI practitioners, analysts, and technology professionals, this course provides a practical approach to building responsible and trustworthy AI systems. To be successful, learners should have a basic understanding of AI and machine learning concepts. Start your journey into Responsible AI and learn how to design AI systems that are fair, transparent, and trustworthy.

Syllabus

Bias Measurement and Mitigation

This module covers the fundamentals of AI fairness, bias measurement, and mitigation in machine learning systems. Learners will explore fairness metrics, bias risks, counterfactual testing, and fairness–accuracy trade-offs through practical demonstrations.

Advanced Model Interpretability

Explore advanced model interpretability techniques used to explain and evaluate AI predictions. Learners will work with local and global explanation methods such as LIME, SHAP, and counterfactual explanations while examining explanation fidelity, robustness, and the limitations of post-hoc interpretability methods through practical demonstrations.

Privacy Attacks, Defenses, and Trade-Off's

This module examines privacy risks, defense mechanisms, and multi-objective trade-offs in responsible AI systems. The module explores membership inference, model inversion, and differential privacy techniques while analyzing the balance between privacy, fairness, and model accuracy through practical demonstrations and decision-making exercises.

Course Wrap-Up and Assessments

This module provides a final review of the course by summarizing key concepts in responsible and trustworthy AI, including fairness, interpretability, privacy, and trade-off analysis. It concludes with a knowledge check to reinforce core concepts and practical understanding.