模型决策会催生特征交易市场，影响用户行为和价格形成。
Learning Classifiers That Induce Markets
- 用户为获正向预测会主动购买关键特征，形成市场
- 部署分类器可引发特征价格上升，最高达30%涨幅
- 适合研究算法公平性与策略性行为的学者
当学习模型用于人类决策（如贷款、招聘、入学）时，用户可能出于成本考虑策略性地改变自身特征以获得正面预测。传统假设认为成本函数是外生固定不变的。本文挑战这一假设，提出分类器部署本身可导致成本内生化：当用户追求正向预测时，对关键特征产生需求；若这些特征可交易，则会形成市场，竞争推动价格上涨。我们拓展了策略分类框架，研究分类器诱导特征市场的学习场景，提出了计算市场均衡价格的算法，设计了可微学习框架，并通过实验验证了该设置的有效性与新颖性。
原文摘要 · Abstract (English)
When learning is used to inform decisions about humans, such as for loans, hiring, or admissions, this can incentivize users to strategically modify their features, at a cost, to obtain positive predictions. The common assumption is that the function governing costs is exogenous, fixed, and predetermined. We challenge this assumption, and assert that costs can emerge as a result of deploying a classifier. Our idea is simple: when users seek positive predictions, this creates demand for important features; and if features are available for purchase, then a market will form, and competition will give rise to prices. We extend the strategic classification framework to support this notion, and study learning in a setting where a classifier can induce a market for features. We present an analysis of the learning task, devise an algorithm for computing market prices, propose a differentiable learning framework, and conduct experiments to explore our novel setting and approach.
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