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.

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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

# Understanding key data security controls

In the realm of artificial intelligence, upholding data security principles assumes paramount importance. 

Data security controls form the cornerstone of safeguarding data integrity and confidentiality within AI systems. These controls encompass data encryption, data loss prevention (DLP), access controls, user training and awareness, patch management, data masking, and data-level access control.

Let’s delve deeper into the key controls:





Learn data security controls, strategies for enhancing AI model robustness, and ways in which AI can be harnessed to strengthen overall security.

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

Enhancing Security of AI Solutions

Understanding key data security controls