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How close is too close? Role of feature attributions in discovering counterfactual explanations.

Wijekoon, Anjana; Wiratunga, Nirmalie; Nkisi-Orji, Ikechukwu; Palihawadana, Chamath; Corsar, David; Martin, Kyle

Authors



Contributors

Mark T. Keane
Editor

Abstract

Counterfactual explanations describe how an outcome can be changed to a more desirable one. In XAI, counterfactuals are "actionable" explanations that help users to understand how model decisions can be changed by adapting features of an input. A case-based approach to counterfactual discovery harnesses Nearest-unlike Neighbours as the basis to identify the minimal adaptations needed for outcome change. This paper presents the DisCERN algorithm which uses the query, its NUN and substitution-based adaptation operations to create a counterfactual explanation case. DisCERN uses feature attribution as adaptation knowledge to order substitutions operations and to bring about the desired outcome with as fewer changes as possible. We find our novel approach for Integrated Gradients using the NUN as the baseline against which the feature attributions are calculated outperforms other techniques like LIME and SHAP. DisCERN also uses feature attributions to bring the NUN closer by which the total change needed is further minimised, but the number of feature changes can increase. Overall, DisCERN outperforms other counterfactual algorithms such as DiCE and NICE in generating valid counterfactuals with fewer adaptations.

Citation

WIJEKOON, A., WIRATUNGA, N., NKISI-ORJI, I., PALIHAWADANA, C., CORSAR, D. and MARTIN, K. 2022. How close is too close? Role of feature attributions in discovering counterfactual explanations. In Keane, M.T. and Wiratunga, N. (eds.) Case-based reasoning research and development: proceedings of the 30th International conference on case-based reasoning (ICCBR 2022), 12-15 September 2022, Nancy, France. Lecture notes in computer science, 13405. Cham: Springer [online], pages 33-47. Available from: https://doi.org/10.1007/978-3-031-14923-8_3

Conference Name 30th International conference on case-based reasoning (ICCBR 2022)
Conference Location Nancy, France
Start Date Sep 12, 2022
End Date Sep 15, 2022
Acceptance Date May 30, 2022
Online Publication Date Aug 14, 2022
Publication Date Aug 31, 2022
Deposit Date Jul 6, 2022
Publicly Available Date Aug 15, 2023
Publisher Springer
Pages 33-47
Series Title Lecture notes in computer science
Series Number 13405
Series ISSN 0302-9743 ; 1611-3349
Book Title Case-based reasoning research and development: proceedings of the 30th International conference on case-based reasoning (ICCBR 2022), 12-15 September 2022, Nancy, France
ISBN 9783031149221
DOI https://doi.org/10.1007/978-3-031-14923-8_3
Keywords Counterfactuals (Computing); Explainable artificial intelligence (XAI); Nearest-unlike neighbours (NUN); Machine learning; Artificial intelligence; Counterfactual XAI; Feature attribution; Integrated gradients; Adaptation
Public URL https://rgu-repository.worktribe.com/output/1706142

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