What skills will I gain by the end of this course?

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

What practical activities are included in the course?

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

Will I work on real-world responsible AI scenarios?

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

Do I need coding knowledge for this course?

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

What tools or platforms are used in this course?

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

What is the main objective of this Responsible AI course?

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

Will I learn how to detect and mitigate bias in AI systems?

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

Does the course cover explainable AI techniques?

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

What will I get if I subscribe to this Specialization?

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.

Is financial aid available?

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.

Responsible AI in Practice: Fairness, Bias & Explainability

This course is part of Responsible AI Specialization

Instructor: Edureka

Included with

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4 modules

Gain insight into a topic and learn the fundamentals.

Beginner level

Recommended experience

8 hours to complete

Flexible schedule

Learn at your own pace

4 modules

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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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 videosTotal 48 minutes

Course Introduction: Responsible AI in Practice: Fairness, Bias & Explainability5 minutes
From Definitions to Metrics: Applying Fairness Metrics4 minutes
Hands-On: Comparing Fairness Metrics on a Hiring Model5 minutes
Hands-On: Interpreting Fairness Metrics Across Groups5 minutes
Label Bias and Proxy Ground Truth Risks5 minutes
Hands-On: Counterfactual Fairness Testing with Causal Graphs7 minutes
Bias Mitigation Strategies4 minutes
Hands-On: Comparing Mitigation Strategies on the Hiring Model8 minutes
Fairness–Accuracy Trade-Offs4 minutes

4 readingsTotal 40 minutes

Course Syllabus: Responsible AI in Practice: Fairness, Bias & Explainability10 minutes
Fairness Metrics Implementation Guide10 minutes
Synthetic Data for Fairness: Methods & Risks10 minutes
Module Summary: Bias Measurement and Mitigation10 minutes

3 assignmentsTotal 27 minutes

Knowledge Check: Implementing Fairness Metrics6 minutes
Bias Mitigation and Trade-Offs6 minutes
Knowledge Check: Bias Measurement and Mitigation15 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 videosTotal 47 minutes

Model Interpretability: Foundations and Approaches6 minutes
Explaining Model Predictions using LIME and SHAP6 minutes
Hands-On: Debugging a Loan Model with SHAP8 minutes
Counterfactual Explanations: Generation, Plausibility, and Sparsity6 minutes
Evaluating Explanation Fidelity in Interpretable AI Systems4 minutes
Stability and Robustness in AI Explanations4 minutes
Hands-On: Detecting Unfaithful or Misleading Explanations7 minutes
Limits of Post-Hoc Interpretability6 minutes

3 readingsTotal 30 minutes

Comparing and Understanding XAI Methods10 minutes
Evaluating Explanation Quality: Metrics and Methods10 minutes
Module Summary: Advanced Model Interpretability10 minutes

3 assignmentsTotal 27 minutes

Local and Global Interpretability Methods6 minutes
Explanation Quality and Evaluation6 minutes
Knowledge Check: Local and Global Interpretability Methods15 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 videosTotal 54 minutes

Membership Inference Attacks4 minutes
Hands-On: Running a Membership Inference Attack on a Trained Model7 minutes
Model Inversion and Attribute Inference Attacks5 minutes
Understanding Differential Privacy Mechanisms4 minutes
Hands-On: Comparing Private vs. Non-Private Model Performance6 minutes
Hands-On: Evaluating Privacy Leakage and Model Trade-offs6 minutes
The Impossibility Triangle: Fairness, Privacy, and Accuracy5 minutes
Hands-On: Interactive Pareto Frontier Explorer7 minutes
Value-Sensitive Design4 minutes
Hands-On: Building a Trade-Off Decision Record for Stakeholder Review6 minutes

3 readingsTotal 30 minutes

Privacy Attacks and Differential Privacy: Technical Handbook10 minutes
Multi-Objective Optimization for Responsible AI10 minutes
Module Summary: Privacy Attacks, Defenses, and Trade-Off's10 minutes

3 assignmentsTotal 27 minutes

Technical Privacy Attacks and Defenses6 minutes
Multi-Objective Trade-Offs6 minutes
Knowledge Check: Privacy Attacks, Defenses, and Trade-Off's15 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 videoTotal 3 minutes

Course Summary: Responsible AI in Practice: Fairness, Bias & Explainability3 minutes

1 readingTotal 30 minutes

Practice Project: Responsible AI Evaluation and Trade-Off Analysis30 minutes

2 assignmentsTotal 60 minutes

End Course Knowledge Check: Responsible AI in Practice: Bias, Explainability & Privacy30 minutes
Responsible AI in Practice: Bias, Explainability & Privacy30 minutes

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Instructor

Edureka

195 Courses177,741 learners

Offered by

Edureka

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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.

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.