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Comparison of Contextual Importance and Utility with LIME and Shapley Values.

Främling, K., Westberg, M., Jullum, M., Madhikermi, M. and Malhi, A., 2021. Comparison of Contextual Importance and Utility with LIME and Shapley Values. In: EXTRAAMAS 2021: Third International Workshop on Explainable, Transparent Autonomous Agents and Multi-Agent Systems, 3-7 May 2021, Virtual, 39 - 54.

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DOI: 10.1007/978-3-030-82017-6_3

Abstract

Different explainable AI (XAI) methods are based on different notions of ‘ground truth’. In order to trust explanations of AI systems, the ground truth has to provide fidelity towards the actual behaviour of the AI system. An explanation that has poor fidelity towards the AI system’s actual behaviour can not be trusted no matter how convincing the explanations appear to be for the users. The Contextual Importance and Utility (CIU) method differs from currently popular outcome explanation methods such as Local Interpretable Model-agnostic Explanations (LIME) and Shapley values in several ways. Notably, CIU does not build any intermediate interpretable model like LIME, and it does not make any assumption regarding linearity or additivity of the feature importance. CIU also introduces the value utility notion and a definition of feature importance that is different from LIME and Shapley values. We argue that LIME and Shapley values actually estimate ‘influence’ (rather than ‘importance’), which combines importance and utility. The paper compares the three methods in terms of validity of their ground truth assumption and fidelity towards the underlying model through a series of benchmark tasks. The results confirm that LIME results tend not to be coherent nor stable. CIU and Shapley values give rather similar results when limiting explanations to ‘influence’. However, by separating ‘importance’ and ‘utility’ elements, CIU can provide more expressive and flexible explanations than LIME and Shapley values.

Item Type:Conference or Workshop Item (Paper)
ISSN:0302-9743
Uncontrolled Keywords:Explainable AI; Contextual Importance and Utility; Outcome explanation; Post hoc explanation
Group:Faculty of Science & Technology
ID Code:36356
Deposited By: Unnamed user with email symplectic@symplectic
Deposited On:13 Dec 2021 08:39
Last Modified:13 Dec 2021 08:39

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