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Responsible AI in Practice: Fairness, Bias & Explainability

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Responsible AI in Practice: Fairness, Bias & Explainability

This course is part of Responsible AI Specialization

Instructor: Edureka

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Gain insight into a topic and learn the fundamentals.
Beginner level

Recommended experience

8 hours to complete
Flexible schedule
Learn at your own pace
Gain insight into a topic and learn the fundamentals.
Beginner level

Recommended experience

8 hours to complete
Flexible schedule
Learn at your own pace

What you'll learn

  • Explain the core principles of fairness, interpretability, privacy, and accountability in Responsible AI systems.

  • Analyze AI models using fairness metrics, explainability methods, and privacy evaluation techniques.

  • Apply bias mitigation, interpretability, and privacy-preserving methods to improve AI system reliability.

  • Evaluate trade-offs between fairness, privacy, interpretability, and model performance in real-world AI solutions.

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Recently updated!

May 2026

Assessments

11 assignments¹

AI Graded see disclaimer
Taught in English

Build your subject-matter expertise

This course is part of the Responsible AI Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
  • Learn new concepts from industry experts
  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
  • Earn a shareable career certificate

There are 4 modules in this course

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.

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.

What's included

9 videos4 readings3 assignments

9 videos•Total 48 minutes
  • Course Introduction: Responsible AI in Practice: Fairness, Bias & Explainability•5 minutes
  • From Definitions to Metrics: Applying Fairness Metrics•4 minutes
  • Hands-On: Comparing Fairness Metrics on a Hiring Model•5 minutes
  • Hands-On: Interpreting Fairness Metrics Across Groups•5 minutes
  • Label Bias and Proxy Ground Truth Risks•5 minutes
  • Hands-On: Counterfactual Fairness Testing with Causal Graphs•7 minutes
  • Bias Mitigation Strategies•4 minutes
  • Hands-On: Comparing Mitigation Strategies on the Hiring Model•8 minutes
  • Fairness–Accuracy Trade-Offs•4 minutes
4 readings•Total 40 minutes
  • Course Syllabus: Responsible AI in Practice: Fairness, Bias & Explainability•10 minutes
  • Fairness Metrics Implementation Guide•10 minutes
  • Synthetic Data for Fairness: Methods & Risks•10 minutes
  • Module Summary: Bias Measurement and Mitigation•10 minutes
3 assignments•Total 27 minutes
  • Knowledge Check: Implementing Fairness Metrics•6 minutes
  • Bias Mitigation and Trade-Offs•6 minutes
  • Knowledge Check: Bias Measurement and Mitigation•15 minutes

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.

What's included

8 videos3 readings3 assignments

8 videos•Total 47 minutes
  • Model Interpretability: Foundations and Approaches•6 minutes
  • Explaining Model Predictions using LIME and SHAP•6 minutes
  • Hands-On: Debugging a Loan Model with SHAP•8 minutes
  • Counterfactual Explanations: Generation, Plausibility, and Sparsity•6 minutes
  • Evaluating Explanation Fidelity in Interpretable AI Systems•4 minutes
  • Stability and Robustness in AI Explanations•4 minutes
  • Hands-On: Detecting Unfaithful or Misleading Explanations•7 minutes
  • Limits of Post-Hoc Interpretability•6 minutes
3 readings•Total 30 minutes
  • Comparing and Understanding XAI Methods•10 minutes
  • Evaluating Explanation Quality: Metrics and Methods•10 minutes
  • Module Summary: Advanced Model Interpretability•10 minutes
3 assignments•Total 27 minutes
  • Local and Global Interpretability Methods•6 minutes
  • Explanation Quality and Evaluation•6 minutes
  • Knowledge Check: Local and Global Interpretability Methods•15 minutes

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.

What's included

10 videos3 readings3 assignments

10 videos•Total 54 minutes
  • Membership Inference Attacks•4 minutes
  • Hands-On: Running a Membership Inference Attack on a Trained Model•7 minutes
  • Model Inversion and Attribute Inference Attacks•5 minutes
  • Understanding Differential Privacy Mechanisms•4 minutes
  • Hands-On: Comparing Private vs. Non-Private Model Performance•6 minutes
  • Hands-On: Evaluating Privacy Leakage and Model Trade-offs•6 minutes
  • The Impossibility Triangle: Fairness, Privacy, and Accuracy•5 minutes
  • Hands-On: Interactive Pareto Frontier Explorer•7 minutes
  • Value-Sensitive Design•4 minutes
  • Hands-On: Building a Trade-Off Decision Record for Stakeholder Review•6 minutes
3 readings•Total 30 minutes
  • Privacy Attacks and Differential Privacy: Technical Handbook•10 minutes
  • Multi-Objective Optimization for Responsible AI•10 minutes
  • Module Summary: Privacy Attacks, Defenses, and Trade-Off's•10 minutes
3 assignments•Total 27 minutes
  • Technical Privacy Attacks and Defenses•6 minutes
  • Multi-Objective Trade-Offs•6 minutes
  • Knowledge Check: Privacy Attacks, Defenses, and Trade-Off's•15 minutes

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.

What's included

1 video1 reading2 assignments

1 video•Total 3 minutes
  • Course Summary: Responsible AI in Practice: Fairness, Bias & Explainability•3 minutes
1 reading•Total 30 minutes
  • Practice Project: Responsible AI Evaluation and Trade-Off Analysis•30 minutes
2 assignments•Total 60 minutes
  • End Course Knowledge Check: Responsible AI in Practice: Bias, Explainability & Privacy•30 minutes
  • Responsible AI in Practice: Bias, Explainability & Privacy•30 minutes

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Instructor

Edureka
203 Courses•185,724 learners

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Frequently asked questions

The course is designed to be completed in approximately 3 weeks, with an estimated 2–3 hours of study per week, including videos, readings, and practice assessments.

This course is designed for AI practitioners, analysts, researchers, compliance professionals, and learners interested in responsible AI systems.

Basic familiarity with AI and machine learning concepts is helpful, but advanced expertise is not required.

You will learn fairness evaluation, bias mitigation, explainable AI, privacy protection, and responsible AI trade-off analysis.

The course includes hands-on demos, fairness testing, SHAP analysis, privacy attack simulations, and trade-off evaluation exercises.

Yes, the course includes practical scenarios involving hiring models, interpretability analysis, and privacy risk evaluation.

Basic Python familiarity is helpful for demonstrations, but the course primarily focuses on responsible AI concepts and applications.

The course uses Google Colab, Python-based responsible AI libraries, and structured datasets for demonstrations.

The main objective is to help learners design, evaluate, and manage AI systems that are fair, interpretable, privacy-aware, and trustworthy.

Yes, the course includes fairness metrics, bias testing, mitigation strategies, and fairness–accuracy trade-off analysis.

Yes, you will learn interpretability methods such as LIME, SHAP, and counterfactual explanations.

To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.

When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

Financial aid available,

¹ Some assignments in this course are AI-graded. For these assignments, your data will be used in accordance with Coursera's Privacy Notice.