Explainable AI

Current research on explainability and interpretability of machine learning algorithms

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Evaluating and Aggregating Feature-based Model Explanations

Umang Bhatt, Adrian Weller, José M. F. Moura. 2020

[ArXiv]    

A feature-based model explanation denotes how much each input feature contributes to a model’s output for a given data point. As the number of proposed explanation functions grows, we lack quantitative evaluation criteria to help practitioners know when to use which explanation function. This paper proposes quantitative evaluation criteria for feature-based explanations: low sensitivity, high faithfulness, and low complexity. We devise a framework for aggregating explanation functions. We develop a procedure for learning an aggregate explanation function with lower complexity and then derive a new aggregate Shapley value explanation function that minimizes sensitivity.