Home>Courses>Responsible AI: Principles and Practices

Intermediate

20h

Certificate of Completion

Responsible AI: Principles and Practices

Learn how to master responsible AI. Learn fairness, bias mitigation, explainable AI, and data privacy to design ethical AI systems. Future-proof your skills in trustworthy AI practices.

AI-POWERED

Explanations

Adaptive Learning

AI-POWERED

Explanations

Adaptive Learning

This course includes

40 Lessons

1 Project

46 Playgrounds

5 Quizzes

Course Overview

What You'll Learn

Course Content

Recommendations

Course Overview

This responsible AI course provides an in-depth exploration of ethical AI development, equipping you with tools and strategies to build transparent, fair, and secure AI systems. Begin by understanding the core principles of responsible AI, including fairness and transparency. Explore real-world examples to identify and mitigate biases across the AI life cycle, ensuring equitable solutions in critical domains like healthcare. Next, dive into explainable AI techniques to interpret and communicate AI model d...Show More

This responsible AI course provides an in-depth exploration of ethical AI development, equipping you with tools and strategies t...Show More

What You'll Learn

A deep understanding of responsible AI principles, including fairness, transparency, and accountability

The ability to identify biases in AI solutions and implement effective bias mitigation strategies

Proficiency in explainable AI techniques for interpreting and communicating AI decisions

Knowledge of best practices for ensuring data privacy, safety, and security in AI development

An understanding of innovative techniques like synthetic data generation and active learning for ethical AI

The ability to apply responsible AI principles to real-world applications across industries

A deep understanding of responsible AI principles, including fairness, transparency, and accountability

Course Content

Introduction to Responsible AI

7 Lessons

Get familiar with AI ethics, fairness, transparency, accountability, and trust in AI systems.

Introduction to the Course

Overview of Responsible AI Principles

Case Study: Credit Loan Approval Data

Case Study: Predict Loan Approval Decisions Using AI

Challenges in Trusting AI Decisions

Key Principles of Responsible AI

Quiz: Introduction to Responsible AI

Fairness of AI Solutions

8 Lessons

Unpack the core of fairness in AI, understanding biases, and mitigation strategies through real-world case studies.

Introduction to Fairness in AI

Bias Across the AI Life Cycle

Understanding the Bias in Model Development

Case Study: Identifying Bias in Personal and Sensitive Data

Case Study: Identify Bias in Model Training

Case Study: Bias Mitigation for Credit Loan Data

Case Study: Navigating Fairness in the Healthcare Domain

Quiz: Fairness of AI Solutions

Explainable AI

11 Lessons

Examine techniques and methods to enhance AI transparency and understanding for various stakeholders.

Introduction to Explainable AI

Case Study: Getting Started with Model Explainability

Demystifying Explainability Themes for Black Box Models

Explainability Techniques for Black Box Models

Case Study: Measure Feature Importance for Healthcare

Case Study: Explore Feature Impact with Partial Dependence Plots

Case Study: Local Explanations with ICE Curves

Case Study: Local Explanations for Regression Problem with LIME

Case Study: Local Explanations for Classification Problem

Case Study: Explain Model Decisions with the SHAP Framework

Quiz: Explainable AI

Data Privacy, Safety, and Security for Responsible AI

5 Lessons

Grasp the fundamentals of data privacy, safety, and security in responsible AI development.

Importance of Data Privacy in Responsible AI

Best Practices for Preserving Data Privacy

Data Safety and Security for Responsible AI

Enhancing Security of AI Solutions

Quiz: Data Privacy, Safety, and Security for Responsible AI

Innovations in Responsible AI: Charting New Frontiers

8 Lessons

Explore innovations in AI focusing on ethical practices, human involvement, and privacy-preserving techniques.

Innovations in Responsible AI

The Role of Human Input inRole of Human Input in Machine Learning

Data Annotation: Bridging the Gap between AI and HI

Active Learning: Optimizing Data Labeling Efforts

Differential Privacy: Securing Personal Information

Significance of Synthetic Data Generation

Case Study: Synthetic Data Generation

Quiz: Innovations in Responsible AI

Final Project

Project

Conclusion

1 Lessons

Investigate the importance of fairness, explainability, privacy, and ethical AI practices.

What’s Next

Course Author

Gaurav Shekhar

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Frequently Asked Questions

What is responsible AI?

Responsible AI designs and deploys artificial intelligence systems that prioritize fairness, transparency, accountability, and ethics while minimizing risks and societal harm.

What is the difference between responsible AI and explainable AI?

Responsible AI encompasses broader principles like fairness, ethics, and data privacy, while Explainable AI focuses specifically on making AI decisions transparent and understandable to build trust and accountability.