This paper studies algorithmic fairness when the protected attribute is location. To handle protected attributes that are continuous, such as age or income, the standard approach is to discretize the domain into predefined groups, and compare algorithmic outcomes across groups. However, applying this idea to location raises concerns of gerrymandering and may introduce statistical bias. Prior work addresses these concerns but only for regularly spaced locations, while raising other issues, most notably its inability to discern regions that are likely to exhibit spatial unfairness. Similar to established notions of algorithmic fairness, we define spatial fairness as the statistical independence of outcomes from location. This translates into requiring that for each region of space, the distribution of outcomes is identical inside and outside the region. To allow for localized discrepancies in the distribution of outcomes, we compare how well two competing hypotheses explain the observed outcomes. The null hypothesis assumes spatial fairness, while the alternate allows different distributions inside and outside regions. Their goodness of fit is then assessed by a likelihood ratio test. If there is no significant difference in how well the two hypotheses explain the observed outcomes, we conclude that the algorithm is spatially fair.
翻译:本文研究在受保护属性位置时的算法公平性。 为了处理持续受保护的属性, 如年龄或收入, 标准方法是将域分解成预先定义的组群, 并比较不同组群的算法结果。 但是, 将这一想法应用到位置会引起对色调的关切, 并可能引入统计偏差。 先前的工作解决了这些关切, 但只针对固定的间距位置, 但也提出了其他问题, 特别是它无法辨别可能显示空间不公平的区域。 与既定的算法公平性概念相似, 我们把空间公平性定义为从位置到结果的统计独立性。 这转化为要求每个空间区域, 结果的分布在区域内外是相同的。 为了允许结果分布上的局部差异, 我们比较了两个相互竞争的假设如何很好地解释观察到的结果。 无效假设以空间公平性为假设, 而其他假设则允许不同区域内外的分布。 然后通过概率比率测试来评估它们是否适合。 如果两种假设在解释观察到的结果方面没有显著的差异, 我们的结论是, 算法在空间上是公平的。</s>