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Mathematical optimization modelling for group counterfactual explanations

Author

Listed:
  • Carrizosa, Emilio
  • Ramírez-Ayerbe, Jasone
  • Romero Morales, Dolores

Abstract

Counterfactual Analysis has shown to be a powerful tool in the burgeoning field of Explainable Artificial Intelligence. In Supervised Classification, this means associating with each record a so-called counterfactual explanation: an instance that is close to the record and whose probability of being classified in the opposite class by a given classifier is high. While the literature focuses on the problem of finding one counterfactual for one record, in this paper we take a stakeholder perspective, and we address the more general setting in which a group of counterfactual explanations is sought for a group of instances. We introduce some mathematical optimization models as illustration of each possible allocation rule between counterfactuals and instances, and we identify a number of research challenges for the Operations Research community.

Suggested Citation

  • Carrizosa, Emilio & Ramírez-Ayerbe, Jasone & Romero Morales, Dolores, 2024. "Mathematical optimization modelling for group counterfactual explanations," European Journal of Operational Research, Elsevier, vol. 319(2), pages 399-412.
    • Handle: RePEc:eee:ejores:v:319:y:2024:i:2:p:399-412
    DOI: 10.1016/j.ejor.2024.01.002
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