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Local Explanations via Necessity and Sufficiency: Unifying Theory and Practice

Watson, David S; Gultchin, Limor; Taly, Ankur; Floridi, Luciano; (2022) Local Explanations via Necessity and Sufficiency: Unifying Theory and Practice. Minds and Machines , 32 (1) pp. 185-218. 10.1007/s11023-022-09598-7. Green open access

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Abstract

Necessity and sufficiency are the building blocks of all successful explanations. Yet despite their importance, these notions have been conceptually underdeveloped and inconsistently applied in explainable artificial intelligence (XAI), a fast-growing research area that is so far lacking in firm theoretical foundations. In this article, an expanded version of a paper originally presented at the 37th Conference on Uncertainty in Artificial Intelligence (Watson et al., 2021), we attempt to fill this gap. Building on work in logic, probability, and causality, we establish the central role of necessity and sufficiency in XAI, unifying seemingly disparate methods in a single formal framework. We propose a novel formulation of these concepts, and demonstrate its advantages over leading alternatives. We present a sound and complete algorithm for computing explanatory factors with respect to a given context and set of agentive preferences, allowing users to identify necessary and sufficient conditions for desired outcomes at minimal cost. Experiments on real and simulated data confirm our method’s competitive performance against state of the art XAI tools on a diverse array of tasks.

Type: Article
Title: Local Explanations via Necessity and Sufficiency: Unifying Theory and Practice
Open access status: An open access version is available from UCL Discovery
DOI: 10.1007/s11023-022-09598-7
Publisher version: https://doi.org/10.1007/s11023-022-09598-7
Language: English
Additional information: Open Access: This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
Keywords: Science & Technology, Technology, Computer Science, Artificial Intelligence, Computer Science, Explainable artificial intelligence, Interpretable machine learning, Shapley values, Rule lists, Counterfactuals, CAUSAL, PARADOX
UCL classification: UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL
URI: https://discovery.ucl.ac.uk/id/eprint/10148319
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