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  • 2022
  • Article
  • Advances in Neural Information Processing Systems (NeurIPS)

Which Explanation Should I Choose? A Function Approximation Perspective to Characterizing Post hoc Explanations

By: Tessa Han, Suraj Srinivas and Himabindu Lakkaraju
  • Format:Print
  • | Pages:30
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Abstract

A critical problem in the field of post hoc explainability is the lack of a common foundational goal among methods. For example, some methods are motivated by function approximation, some by game theoretic notions, and some by obtaining clean visualizations. This fragmentation of goals causes not only an inconsistent conceptual understanding of explanations but also the practical challenge of not knowing which method to use when.
In this work, we begin to address these challenges by unifying eight popular post hoc explanation methods (LIME, C-LIME, KernelSHAP, Occlusion, Vanilla Gradients, Gradients × Input, SmoothGrad, and Integrated Gradients). We show that these methods all perform local function approximation of the black-box model, differing only in the neighbourhood and loss function used to perform the approximation. This unification enables us to (1) state a no free lunch theorem for explanation methods, demonstrating that no method can perform optimally across all neighbourhoods, and (2) provide a guiding principle to choose among methods based on faithfulness to the black-box model. We empirically validate these theoretical results using various real-world datasets, model classes, and prediction tasks.
By bringing diverse explanation methods into a common framework, this work (1) advances the conceptual understanding of these methods, revealing their shared local function approximation objective, properties, and relation to one another, and (2) guides the use of these methods in practice, providing a principled approach to choose among methods and paving the way for the creation of new ones.

Keywords

Mathematical Methods; Decision Choices and Conditions; Analytics and Data Science

Citation

Han, Tessa, Suraj Srinivas, and Himabindu Lakkaraju. "Which Explanation Should I Choose? A Function Approximation Perspective to Characterizing Post hoc Explanations." Advances in Neural Information Processing Systems (NeurIPS) (2022). (Best Paper Award, International Conference on Machine Learning (ICML) Workshop on Interpretable ML in Healthcare.)
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About The Author

Himabindu Lakkaraju

Technology and Operations Management
→More Publications

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More from the Authors
  • When Algorithms Explain Themselves: AI Adoption and Accuracy of Experts' Decisions By: Himabindu Lakkaraju and Chiara Farronato
  • Efficiently Training Low-Curvature Neural Networks By: Suraj Srinivas, Kyle Matoba, Himabindu Lakkaraju and Francois Fleuret
  • OpenXAI: Towards a Transparent Evaluation of Model Explanations By: Chirag Agarwal, Satyapriya Krishna, Eshika Saxena, Martin Pawelczyk, Nari Johnson, Isha Puri, Marinka Zitnik and Himabindu Lakkaraju
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