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Explainability is not a game
The societal and economic significance of machine learning (ML) cannot be overstated, with many remarkable advances made in recent years. However, the operation of complex ML models is most often inscrutable, with the consequence that decisions taken by ML models cannot be fathomed by human decision makers.
We identified two reasons that justify why SHAP scores can provide misleading information: the contributions of all possible subsets of fixed features are considered; and class values are explicitly accounted for. ANR-19-PI3A-0004, and by the H2020-ICT38 project COALA “Cognitive Assisted agile manufacturing for a Labor force supported by trustworthy Artificial intelligence.” This work was motivated in part by discussions with several colleagues including L. Bertossi, A. Ignatiev, N. Narodytska, M. Cooper, Y. Izza, O. Létoffé, R. Passos, A. Morgado, J. Get Involved By opening CACM to the world, we hope to increase engagement among the broader computer science community and encourage non-members to discover the rich resources ACM has to offer.
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