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

The safety and security of data is a fundamental necessity in today’s digital age. It arises from the need to safeguard sensitive information from unauthorized access, ensuring data confidentiality, integrity, and availability. 

AI’s disruptive impact across various industries underscores the increasing significance of data security within AI systems. With the increasing volume of personal, financial, and business data stored and exchanged online, the need for data security has never been more critical. Data breaches can have devastating consequences, including identity theft, financial losses, and reputational damage. Moreover, stringent data protection regulations, such as GDPR and HIPAA, make it imperative for organizations to implement robust data security measures to avoid legal liabilities. 


Learn techniques to protect AI systems and data assets from threats, ensuring the integrity, confidentiality, and availability of critical information.

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

Data Safety and Security for Responsible AI