AI 中文总结
该研究针对可解释AI中权力指数选择不足的问题，将权力指数分为三类维度，分析其性质并通过示例指导从业者选择合适指数。
AI 中文摘要
权力指数起源于合作博弈论，用于量化每个博弈参与者对给定博弈结果的影响。它们最初旨在在参与者之间分配利润或成本，以及分析投票系统的公平性，最近作为将基于AI的系统的输出归因于输入、从而促进可解释性的方法而受到关注。然而，为特定解释任务选择合适的权力指数是一个研究不足的问题。为解决该问题，我们将权力指数沿三个归因维度组织：单参与者、基于集合和基于基数。对于每个维度，我们综述相应的权力指数，在适用时对现有指数进行概括，并分析它们满足哪些形式原则。我们对文献中缺失的性质提供证明，并表明转向基于基数的设置会消除参与者身份信息，同时保留一些指数级别的区分。我们使用具体示例说明维度和指数的选择如何在实践中影响最终的归因结果，并为寻求为特定应用场景选择合适权力指数的从业者提供指导。
英文摘要
Power indices, originating in cooperative game theory, quantify each player's influence on the outcome of a given game. Originally designed to distribute profits or costs among players and to analyse the fairness of voting systems, power indices have recently gained prominence as methods for attributing outputs of AI-based systems to inputs, thus facilitating explainability. However, selecting the appropriate power index for a given explanation task is an understudied problem. To address this, we organise power indices along three attribution dimensions: single-player, set-based, and cardinality-based. For each dimension, we review the corresponding power indices, generalise existing ones where applicable, and analyse which formal principles they satisfy. We provide proofs for properties that are missing in the literature and show that moving to the cardinality-based setting removes player-identity information while preserving some index-level distinctions. Using concrete examples, we illustrate how the choice of dimension and index affects the resulting attributions in practice, and offer guidance for practitioners seeking to select a suitable power index for a given application context.
