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 primary goal of explainable AI?

• To make AI models cheaper

• To make AI models understandable to humans

• To make AI models more accurate

• To make AI models faster

Why is trust important in the deployment of machine learning models?

• Because it makes models more complex

• Because it reduces the cost of models

• Because it ensures models are used correctly and safely

• Because it increases the speed of the models

What is one of the main challenges in explaining black box models?

• They are too simple to analyze

• They are already well understood

• They are too expensive to run

• They are proprietary and not accessible

What is a common method to analyze black box models?

• Ignoring the model's output

• Creating heat maps to visualize important features

• Focusing on the model's speed

• Using only the model's input data

Why might interpretable models be preferred over black box models?

• They are less accurate

• They are faster to execute

• They provide a clearer understanding of decision-making processes

• They are easier to construct

What is a significant challenge in creating interpretable models?

• They are too fast

• They are not regulated

• They are computationally complex

• They are too simple

What is an important aspect of communicating model explanations to the public?

• Ignoring the audience's understanding

• Making the information engaging and understandable

• Using technical jargon

• Focusing on the model's speed