让分类器模糊表态，提升对抗策略时的鲁棒性。
Ambiguous Strategic Classification
- 用不确定性范围替代固定分类器，允许系统隐藏真实决策。
- 在监管披露部分信息下，仍能优化学习效果与策略响应。
- 适合关注公平性与抗操纵的系统设计者参考。
战略分类中通常假设分类器是公开的，但系统为何选择完全披露尚不明确。本文研究一种监管要求披露部分而非全部信息的设定，由此引发学习任务：需同时优化分类器及其不确定性。为此，借鉴鲁棒机制设计中的模糊性概念，允许学习者揭示一组可能的分类器，而私下决定最终采用哪一个。本文分析模糊性对学习的影响，提出高效计算最优响应与训练的算法，并在新设定下实证探索了策略学习及其结果。
原文摘要 · Abstract (English)
A common assumption in strategic classification is that the classifier is public knowledge. However, it remains unclear whether, and why, a system would choose to commit to full disclosure. We study a setting in which regulation requires the system to disclose some, but not all, of the information. This induces a learning task in which the learner must jointly optimize the classifier and the uncertainty surrounding it. To this end, we adopt from robust mechanism design the notion of ambiguity, which in our setting allows the learner to reveal a set or range of possible classifiers, while privately choosing which of them to ultimately realize. We investigate how ambiguity affects the learning task, develop efficient algorithms for computing best-responses and training, and empirically explore strategic learning and its outcomes in this novel setting and using our approach.
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