This course equips you with tools to develop responsible AI systems that are fair, transparent, accountable, and beneficial to society as a whole.

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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

# AI solution life cycle

Have you wondered what goes on behind the scenes when we receive decisions from the AI model?

The AI solution life cycle encompasses the entire process, from data preparation to model management. Here’s a brief overview of each stage:

Learn about the AI solution development life cycle and build a machine learning model to predict loan approval decisions.

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: Predict Loan Approval Decisions Using AI

AI solution life cycle