Responsible AI: Principles and Practices

INTERACTIVE COURSE

Responsible AI: Principles and Practices

Intermediate

40 Lessons

20h

Certificate of Completion

AI Explanations

AI Explanations

Responsible AI: Principles and Practices

1 Project

46 Playgrounds

5 Quizzes

42 Illustrations

Takeaway Skills

An understanding of ethical considerations in artificial intelligence development

The ability to learn how to evaluate and make responsible decisions throughout the AI life cycle

The ability to identify biases in AI solutions and implement strategies to mitigate them effectively

Familiarity with interpreting and explaining AI model decisions, enhancing transparency and accountability in AI systems

Knowledge of best practices for preserving data privacy and enhancing the security of AI solutions, mitigating risks, and safeguarding sensitive information

The ability to explore emerging technologies and methodologies in Responsible AI, such as data annotation and synthetic data generation

Course Overview

This course offers a comprehensive exploration of the ethical considerations and best practices surrounding the development and deployment of artificial intelligence (AI) systems, all within the context of Python. In this course, you will focus on the key pillars of Responsible AI, starting with the notion of fairness. You’ll gain insight into the presence of biases across the AI life cycle and learn strategies for identifying and mitigating bias in AI solutions. The course emphasizes the significance of ...Show More

Course Content

1

Introduction to Responsible AI

Introduction to the Course

Overview of Responsible AI Case Study: Exploring 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

2

Fairness of AI Solutions

Overview of Fairness in AI Solution Presence of Bias across the AI Life Cycle Understanding Bias in AI Solution 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

3

Explainable AI

Overview of 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

4

Data Privacy, Safety, and Security for Responsible AI

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

5

Innovations in Responsible AI: Charting New Frontiers

Innovations in Responsible AI Role 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

6

Conclusion

1 Lesson

COURSE AUTHOR

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