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Pré-Publication, Document De Travail Année : 2023

Multi-choice Explanations: A New Cooperative Game Structure for XAI

Résumé

Cooperative game theorists propose the following attractive process: (1) capture the abstract value of each possible coalition of individuals, (2) write down some principles, or axioms, on how to distribute the value (e.g., allocate importance to features or parameters), and then, (3) find a set of allocations that satisfy the principles. The Shapley value has received much attention -- but it is just one solution concept, satisfying one set of principles, in one class of games. It is popular among game theorists because the axioms, and the class of TU-games, are reasonable in game theory. In AI and ML, we should choose carefully what is reasonable for our own purposes. In this paper, we highlight solution concepts in the class of multi-choice games (MC-games). These are model agnostic, and unique to their own set of axioms, just like the Shapley value. This paper offers a general algorithm for constructing any MC-game framework with polynomial time complexity in the number of parameter levels, and an application of this algorithm that is transparent, and can be readily generalised to local explanation frameworks such as SHapley Additive exPlanations (SHAP).
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Dates et versions

hal-04254509 , version 1 (24-10-2023)

Identifiants

  • HAL Id : hal-04254509 , version 1

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Daniel Fryer, David Lowing, Inga Strümke, Hien Duy Nguyen. Multi-choice Explanations: A New Cooperative Game Structure for XAI. 2023. ⟨hal-04254509⟩
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