Lead the GenAI revolution by learning fairness, bias mitigation, and Explainable AI in Responsible AI: Principles and Practices. Future-proof your skills in ethical AI development.

rai.tar.gz

fairness

jupyter

respai

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 fairness in various domains, including healthcare, and equips learners with the tools to navigate fairness considerations effectively. Furthermore, exploring Explainable AI sheds light on methods for interpreting and understanding AI model decisions, which are crucial for ensuring transparency and accountability in AI systems. 

After taking this course, you’ll be equipped with the knowledge and tools to promote ethical AI development and uphold principles of responsibility and accountability.

Responsible AI: Principles and Practices

In this lesson, we’ll build a predictive classification model on the loan approval dataset. We train a machine learning model to predict loan approval decisions. Then, we’ll explain the model prediction on individual examples using the LIME framework.


Learn how to explain individual model decisions using the LIME framework for a classification case study.

Introduction to Responsible AI

Fairness of AI Solutions

Explainable AI

Data Privacy, Safety, and Security for Responsible AI

Innovations in Responsible AI: Charting New Frontiers

Final Project

Conclusion

Case Study: Local Explanations for Classification Problem