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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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

To improve model performance on training data

To understand how models make predictions and make them more transparent

What is the primary purpose of model explainability in machine learning?

What is the primary purpose of model explainability in machine learning?

To show how features interact with each other

To visualize the relationship between a single feature and model predictions while keeping other features constant

To measure the accuracy of a machine learning model

To explain the randomness in model predictions

What is the purpose of partial dependence plots (PDPs) in model explainability?

What is the purpose of partial dependence plots (PDPs) in model explainability?

To evaluate the performance of machine learning models

To provide local explanations for individual predictions made by complex models

To provide global explanations for individual predictions made by complex models

How can Local Interpretable Model-Agnostic Explanations (LIME) be used in model explainability?

How can Local Interpretable Model-Agnostic Explanations (LIME) be used in model explainability?

A financial institution employs a model for loan approvals. Customers seek personalized reasons for their loan denials. Which method would provide these explanations?

A financial institution employs a model for loan approvals. Customers seek personalized reasons for their loan denials. Which method would provide these explanations?

A retail company uses a recommendation system for its online store. Customers want to know why a particular product was recommended to them. What aspect of model explainability addresses this?

A retail company uses a recommendation system for its online store. Customers want to know why a particular product was recommended to them. What aspect of model explainability addresses this?

Test your understanding of concepts on Explainable AI topics.

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: Explainable AI