用投票理论公理检验神经网络决策，发现其结果准确但逻辑不符。
Learning How to Vote with Principles: Axiomatic Insights Into the Collective Decisions of Neural Networks
- 以投票公理为标准构建神经网络投票模型
- 仅优化公理满足度可生成超越现有规则的新投票机制
- 适合关注AI公平性与投票机制设计的研究者
神经网络能否在满足集体决策透明性需求的前提下应用于投票理论？我们提出公理化深度投票框架，通过投票理论中的公理方法构建和评估偏好聚合的神经网络。研究发现：(1) 尽管神经网络预测准确，却常违背投票规则的核心公理，暴露了结果模仿与推理逻辑之间的脱节；(2) 使用特定公理数据训练无法提升对相应公理的契合度；(3) 仅优化公理满足度时，神经网络能合成新投票规则，这些规则通常优于且显著区别于现有规则。该研究为人工智能领域提供了偏见与价值对齐的数学严谨分析路径，也为投票理论开辟了新的规则空间探索方向。
原文摘要 · Abstract (English)
Can neural networks be applied in voting theory, while satisfying the need for transparency in collective decisions? We propose axiomatic deep voting: a framework to build and evaluate neural networks that aggregate preferences, using the well-established axiomatic method of voting theory. Our findings are: (1) Neural networks, despite being highly accurate, often fail to align with the core axioms of voting rules, revealing a disconnect between mimicking outcomes and reasoning. (2) Training with axiom-specific data does not enhance alignment with those axioms. (3) By solely optimizing axiom satisfaction, neural networks can synthesize new voting rules that often surpass and substantially differ from existing ones. This offers insights for both fields: For AI, important concepts like bias and value-alignment are studied in a mathematically rigorous way; for voting theory, new areas of the space of voting rules are explored.
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