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

# Responsible AI principles

Responsible AI refers to the integration of key principles that ensure fairness, explainability, human-AI collaboration, privacy, and security in AI solution development.

These principles address the challenges associated with AI, such as bias, lack of interpretability, ethical concerns, and data privacy.





Learn the key principles of Responsible AI and how they overcome trust challenges for AI solutions.

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

Key Principles of Responsible AI

Responsible AI principles