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


# Factors undermining trust in AI solutions

Building trust in AI systems is crucial for their widespread adoption. Responsible AI practices that prioritize fairness, transparency, and user-centric design are essential for fostering trust among users, stakeholders, and the public.

In this lesson we focus on some key factors that undermine building trust in AI solutions. 

Learn about the key challenges in building trust in AI decisions. 

Challenges in Trusting AI Decisions

Learn about the key challenges in building trust in AI 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

Challenges in Trusting AI Decisions

Factors undermining trust in AI solutions