研究算法选择中公平与效率的权衡，发现短期公平可能加剧长期不公。
Price of Fairness in Short-Term and Long-Term Algorithmic Selections
- 构建短期与长期群体公平性概念，分析公平代价（PoF）
- 即使群体分布接近，短期公平的代价也可能很大
- 简单投资策略可消除长期不公，且代价低
高风险场景中的算法决策对个人和群体有深远影响。尽管已有大量研究关注静态环境下的公平性，但近期研究表明，强制执行静态公平约束可能加剧长期不平等。为此，我们研究了一个简化的序列选择问题：决策者反复选择个体，其行为既影响即时效用，也随时间改变群体分布。本文提出短期与长期群体公平性的定义，并通过公平代价（PoF）理论分析公平与效用之间的权衡。我们刻画了短期最优与公平策略，发现即使群体分布几乎相同，PoF仍可能很大。相反，我们证明在简单投资策略下，长期不平等可消失，且保持较低的PoF。我们在合成数据和真实数据集上实证验证了这些理论发现。
原文摘要 · Abstract (English)
Algorithmic decision-making in high-stakes settings can have profound impacts on individuals and populations. While much prior work studies fairness in static settings, recent results show that enforcing static fairness constraints may exacerbate long-run disparities. Motivated by this tension, we study a stylized sequential selection problem in which a decision-maker repeatedly selects individuals, affecting both immediate utility and the population distribution over time. We introduce notions of group fairness for both the short and long term and theoretically analyze the trade-off between fairness and utility via the Price of Fairness (PoF). We characterize optimal and fair policies in the short term and show that the PoF can be large even when group distributions are nearly identical. In contrast, we show that long-term disparities can vanish under simple investment policies that achieve a low PoF. We also empirically validate these theoretical observations using both synthetic and real datasets.
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