DisCERN: discovering counterfactual explanations using relevance features from neighbourhoods.

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

Abstract

Counterfactual explanations focus on 'actionable knowledge' to help end-users understand how a machine learning outcome could be changed to a more desirable outcome. For this purpose a counterfactual explainer needs to discover input dependencies that relate to outcome changes. Identifying the minimum subset of feature changes needed to action an output change in the decision is an interesting challenge for counterfactual explainers. The DisCERN algorithm introduced in this paper is a case-based counter-factual explainer. Here counterfactuals are formed by replacing feature values from a nearest unlike neighbour (NUN) until an actionable change is observed. We show how widely adopted feature relevance-based explainers (i.e. LIME, SHAP), can inform DisCERN to identify the minimum subset of 'actionable features'. We demonstrate our DisCERN algorithm on five datasets in a comparative study with the widely used optimisation-based counterfactual approach DiCE. Our results demonstrate that DisCERN is an effective strategy to minimise actionable changes necessary to create good counterfactual explanations.

Citation

WIRATUNGA, N., WIJEKOON, A., NKISI-ORJI, I., MARTIN, K., PALIHAWADANA, C. and CORSAR, D. 2021. DisCERN: discovering counterfactual explanations using relevance features from neighbourhoods. In Proceedings of 33rd IEEE (Institute of Electrical and Electronics Engineers) International conference on tools with artificial intelligence 2021 (ICTAI 2021), 1-3 November 2021, Washington, USA [virtual conference]. Piscataway: IEEE [online], pages 1466-1473. Available from: https://doi.org/10.1109/ICTAI52525.2021.00233

• Conference Name: 33rd IEEE (Institute of Electrical and Electronics Engineers) International conference on tools with artificial intelligence 2021 (ICTAI 2021)
• Conference Location: Washington, USA [virtual conference]
• Start Date: Nov 1, 2021
• End Date: Nov 3, 2021
• Acceptance Date: Sep 10, 2021
• Online Publication Date: Dec 21, 2021
• Publication Date: Dec 31, 2021
• Deposit Date: Sep 16, 2021
• Publicly Available Date: Sep 16, 2021
• Publisher: IEEE Computer Society
• Pages: 1466-1473
• Series ISSN: 2375-0197
• ISBN: 9781665408998
• DOI: https://doi.org/10.1109/ICTAI52525.2021.00233
• Keywords: Explainable AI; Counterfactuals; Case-based reasoning
• Public URL: https://rgu-repository.worktribe.com/output/1457005