Skip to Main content Skip to Navigation
Conference papers

Fair and Efficient Alternatives to Shapley-based Attribution Methods

Abstract : Interpretability of predictive machine learning models is crit- ical for numerous application contexts that require decisions to be un- derstood by end-users. It can be studied through the lens of local ex- plainability and attribution methods that focus on explaining a specific decision made by a model for a given input, by evaluating the contri- bution of input features to the results, e.g. probability assigned to a class. Many attribution methods rely on a game-theoretic formulation of the attribution problem based on an approximation of the popular Shapley value, even if the underlying rationale motivating the use of this specific value is today questioned. In this paper we introduce the FESP - Fair-Efficient-Symmetric-Perturbation - attribution method as an alternative approach sharing relevant axiomatic properties with the Shapley value, and the Equal Surplus value (ES) commonly applied in cooperative games. Our results show that FESP and ES produce better attribution maps compared to state-of-the-art approaches in image and text classification settings.
Document type :
Conference papers
Complete list of metadata

https://hal.mines-ales.fr/hal-03781033
Contributor : Administrateur IMT - Mines Alès Connect in order to contact the contributor
Submitted on : Tuesday, September 20, 2022 - 8:22:14 AM
Last modification on : Thursday, September 22, 2022 - 4:50:29 AM

Identifiers

  • HAL Id : hal-03781033, version 1

Citation

Charles Condevaux, Sébastien Harispe, Stéphane Mussard. Fair and Efficient Alternatives to Shapley-based Attribution Methods. ECMLPKDD 2022 - The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, Sep 2022, Grenoble, France. ⟨hal-03781033⟩

Share

Metrics

Record views

0