Beyond Shapley Values: Cooperative Games for the Interpretation of Machine Learning Models
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866915347016187904 |
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| author | Idrissi, Marouane Il Machado, Agathe Fernandes Charpentier, Arthur |
| author_facet | Idrissi, Marouane Il Machado, Agathe Fernandes Charpentier, Arthur |
| contents | Cooperative game theory has become a cornerstone of post-hoc interpretability in machine learning, largely through the use of Shapley values. Yet, despite their widespread adoption, Shapley-based methods often rest on axiomatic justifications whose relevance to feature attribution remains debatable. In this paper, we revisit cooperative game theory from an interpretability perspective and argue for a broader and more principled use of its tools. We highlight two general families of efficient allocations, the Weber and Harsanyi sets, that extend beyond Shapley values and offer richer interpretative flexibility. We present an accessible overview of these allocation schemes, clarify the distinction between value functions and aggregation rules, and introduce a three-step blueprint for constructing reliable and theoretically-grounded feature attributions. Our goal is to move beyond fixed axioms and provide the XAI community with a coherent framework to design attribution methods that are both meaningful and robust to shifting methodological trends. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_13900 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Beyond Shapley Values: Cooperative Games for the Interpretation of Machine Learning Models Idrissi, Marouane Il Machado, Agathe Fernandes Charpentier, Arthur Machine Learning Artificial Intelligence Cooperative game theory has become a cornerstone of post-hoc interpretability in machine learning, largely through the use of Shapley values. Yet, despite their widespread adoption, Shapley-based methods often rest on axiomatic justifications whose relevance to feature attribution remains debatable. In this paper, we revisit cooperative game theory from an interpretability perspective and argue for a broader and more principled use of its tools. We highlight two general families of efficient allocations, the Weber and Harsanyi sets, that extend beyond Shapley values and offer richer interpretative flexibility. We present an accessible overview of these allocation schemes, clarify the distinction between value functions and aggregation rules, and introduce a three-step blueprint for constructing reliable and theoretically-grounded feature attributions. Our goal is to move beyond fixed axioms and provide the XAI community with a coherent framework to design attribution methods that are both meaningful and robust to shifting methodological trends. |
| title | Beyond Shapley Values: Cooperative Games for the Interpretation of Machine Learning Models |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2506.13900 |