Abstract
Attribution scores reflect how important the feature values in an input entity are for the output of a machine learning model. One of the most popular attribution scores is the SHAP score, which is an instantiation of the general Shapley value used in coalition game theory. The definition of this score relies on a probability distribution on the entity population. Since the exact distribution is generally unknown, it needs to be assigned subjectively or be estimated from data, which may lead to misleading feature scores. In this paper, we propose a principled framework for reasoning on SHAP scores under unknown entity population distributions. In our framework, we consider an uncertainty region that contains the potential distributions, and the SHAP score of a feature becomes a function defined over this region. We study the basic problems of finding maxima and minima of this function, which allows us to determine tight ranges for the SHAP scores of all features. In particular, we pinpoint the complexity of these problems, and other related ones, showing them to be intractable. Finally, we present experiments on a real-world dataset, showing that our framework may contribute to a more robust feature scoring.
Original language | English |
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Title of host publication | ECAI 2024 |
Subtitle of host publication | 27th European Conference on Artificial Intelligence, 19–24 October 2024, Santiago de Compostela, Spain – Including 13th Conference on Prestigious Applications of Intelligent Systems (PAIS 2024) |
Editors | Ulle Endriss, Francisco S. Melo, Kerstin Bach, Alberto Bugarín-Diz, José M. Alonso-Moral, Senén Barro, Fredrik Heintz |
Publisher | IOS Press |
Pages | 971-978 |
Number of pages | 8 |
Volume | 392 |
ISBN (Electronic) | 9781643685489 |
DOIs | |
Publication status | Published - 19 Oct 2024 |
Externally published | Yes |
Event | 27th European Conference on Artificial Intelligence - Including 13th Conference on Prestigious Applications of Intelligent Systems (PAIS 2024) - Santiago de Compostela, Spain Duration: 19 Oct 2024 → 24 Oct 2024 Conference number: 27 |
Publication series
Name | Frontiers in Artificial Intelligence and Applications |
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Publisher | IOS Press |
Number | 2024 |
Volume | 392 |
ISSN (Print) | 0922-6389 |
ISSN (Electronic) | 1879-8314 |
Conference
Conference | 27th European Conference on Artificial Intelligence - Including 13th Conference on Prestigious Applications of Intelligent Systems (PAIS 2024) |
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Abbreviated title | ECAI 2024 |
Country/Territory | Spain |
City | Santiago de Compostela |
Period | 19/10/24 → 24/10/24 |