What Is Explainable AI? | Explainable vs Interpretable Machine Learning

The video discusses the importance of explainable AI, highlighting the challenges of understanding complex machine learning models. It explains the need for trust in AI systems, especially in regulated fields like healthcare. The video outlines two main approaches to explainability: analyzing black box models and creating inherently interpretable models. It also addresses the challenges of bias and the difficulty of translating complex models into understandable explanations. The video concludes with a discussion on the importance of effectively communicating model insights to the public.

What is the significance of explainable AI in understanding machine learning models?

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Why is trust important when deploying machine learning models at large scale?

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How can explainability contribute to gaining new insights in fields like cancer diagnostics?

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What are the two overarching approaches to explain a deep neural network?

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Discuss the challenges associated with analyzing black box models.

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What are the implications of using interpretable models versus black box models?

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In what ways can creativity enhance the communication of machine learning models to the public?

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