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.

Intermediate

40 Lessons

20h

Certificate of Completion

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

AI-POWERED

Explanations

This course includes

1 Project

46 Playgrounds

5 Quizzes

This course includes

1 Project

46 Playgrounds

5 Quizzes

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

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

What You'll Learn

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

Course Content

Introduction to Responsible AI

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

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

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

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

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 Lesson

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

Course Author

Trusted by 1.4 million developers working at companies

Anthony Walker

@_webarchitect_

Emma Bostian 🐞

@EmmaBostian

Evan Dunbar

ML Engineer

Carlos Matias La Borde

Software Developer

Souvik Kundu

Front-end Developer

Vinay Krishnaiah

Software Developer

Eric Downs

Musician/Entrepeneur

Kenan Eyvazov

DevOps Engineer

Anthony Walker

@_webarchitect_

Emma Bostian 🐞

@EmmaBostian

Hands-on Learning Powered by AI

See how Educative uses AI to make your learning more immersive than ever before.

Instant Code Feedback

Evaluate and debug your code with the click of a button. Get real-time feedback on test cases, including time and space complexity of your solutions.

AI-Powered Mock Interviews

Put your skills to the test in a simulated interview setting. Receive personalized feedback based on your performance. Available in Premium & Premium Plus plans.

Adaptive Learning

At various checkpoints throughout Educative courses, you will be prompted to take a quick assessment. Receive a condensed curriculum tailored to your strengths and skill gaps.

Explain with AI

Select any text within any Educative course, and get an instant explanation — without ever leaving your browser.

AI Code Mentor

AI Code Mentor helps you quickly identify errors in your code, learn from your mistakes, and nudge you in the right direction — just like a 1:1 tutor!