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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fairness

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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

What does the term PII stand for in the context of data privacy?

What does the term PII stand for in the context of data privacy?

Impact of the AI decision on users and stakeholders


In the context of AI development and deployment, what is a key consideration when determining the level of risk associated with an AI decision?

In the context of AI development and deployment, what is a key consideration when determining the level of risk associated with an AI decision?

Maximizing the amount of data collected from users

Collecting only the necessary data for a specific purpose

In the context of data privacy regulations like GDPR (General Data Protection Regulation), what does data minimization refer to?

In the context of data privacy regulations like GDPR (General Data Protection Regulation), what does data minimization refer to?

Evaluating the environmental impact of AI systems

Assessing the potential risks to individuals’ data privacy and security

Measuring the computational performance of AI models

Determining the accuracy of AI predictions

What is the role of a Data Protection Impact Assessment (DPIA) in Responsible AI?

What is the role of a Data Protection Impact Assessment (DPIA) in Responsible AI?

Collecting as much data as possible to improve AI accuracy.

Sharing sensitive data freely with third parties for research purposes.

Implementing access controls to restrict data access to authorized personnel.

Storing all data in a single, easily accessible location for convenience.

What is one of the key data security controls to ensure responsible AI practices?

What is one of the key data security controls to ensure responsible AI practices?

Test your understanding of concepts on data privacy, safety, and 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

Quiz: Data Privacy, Safety, and Security for Responsible AI