List-wise Fairness Criterion for Point Processes
List-wise Fairness Criterion for Point Processes
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DOI：
10.1145/3394486.3403246
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发表时间：
2020-07
期刊：
影响因子：
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通讯作者：
Jin Shang;Mingxuan Sun;N. Lam
中科院分区：
文献类型：
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作者：
Jin Shang;Mingxuan Sun;N. Lam
许多类型的事件序列数据在空间和时间上表现出触发和聚类特性。点过程被广泛应用于预测警务和灾难事件预测等事件数据的建模。虽然目前的算法可以实现显著的事件预测精度，但历史数据或自激特性可能会引入偏差预测。例如，根据事件危险率对热点进行排名可以使弱势群体（例如，少数民族或社会经济地位较低的社区）的可见度更加明显。现有的方法已经探索了通过使用几个组公平指标惩罚目标函数来实现组间平等的方法。然而，这些指标无法衡量排名的每个前缀的公平性。本文提出了一种新的基于表的点过程公平性准则，该准则能够有效地评价事件预测中的排序公平性。我们还给出了公平度量的不公平一致性的严格定义，并证明了我们的单表公平准则满足这一性质。在多个真实时空序列数据集上的实验证明了我们的列表公平性准则的有效性。
Many types of event sequence data exhibit triggering and clustering properties in space and time. Point processes are widely used in modeling such event data with applications such as predictive policing and disaster event forecasting. Although current algorithms can achieve significant event prediction accuracy, the historic data or the self-excitation property can introduce biased prediction. For example, hotspots ranked by event hazard rates can make the visibility of a disadvantaged group (e.g., racial minorities or the communities of lower social economic status) more apparent. Existing methods have explored ways to achieve parity between the groups by penalizing the objective function with several group fairness metrics. However, these metrics fail to measure the fairness on every prefix of the ranking. In this paper, we propose a novel list-wise fairness criterion for point processes, which can efficiently evaluate the ranking fairness in event prediction. We also present a strict definition of the unfairness consistency property of a fairness metric and prove that our list-wise fairness criterion satisfies this property. Experiments on several real-world spatial-temporal sequence datasets demonstrate the effectiveness of our list-wise fairness criterion.