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Feature Importance and Explainability

Explainability is considered a fundamental ingredient of practical AI development to address issues such as fairness. The latest blog in our responsible AI research series presents an in-depth review of machine learning explainability models and methods.

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Explainability I: local post-hoc explanations

In part I of our blog series on explainability we cover the importance of explainability for AI systems, methods for creating local post-hoc explanations and a taxonomy of approaches.

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Responsible AI

Explainability II: global explanations, proxy models, and interpretable models

In part II of our blog series on explainability we discuss the remaining three types of approaches to explainability. These approaches include global post-hoc explanations, proxy models and interpretable models.

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Responsible AI