Assessing the spatial fairness of predictive models involves establishing whether they are statistically penalizing (favoring) individuals associated with certain geographical locations. Literature on this topic makes the fundamental assumption that each individual is assigned to a single geographical location (e.g., place of residence). However, fairness with respect to the set of locations where one has been, i.e., their movement patterns over different regions, also matters when fairness is considered. Consequently, we argue that it is necessary to generalize the notion of spatial fairness to also include movement patterns, leading to the novel problem of assessing predictive models for fairness relative to the movements of individuals. To deal with this problem, we propose an approach that first associates the movements of individuals to certain geographic regions, considering multiple spatial partitions with different resolutions and alignments, and then employs a suitable spatial scan statistic to assess whether a predictive model is fair based on movement patterns. In the experimental evaluation, we study the performance of our approach over thousands of synthetic unfair datasets, showing that it is effective at detecting this new type of unfairness and at retrieving the set of objects treated unfairly, while localization performance exhibits a consistent multi-resolution trade-off.

翻译：评估预测模型的空间公平性涉及确定模型是否在统计上惩罚（或偏袒）与特定地理位置相关的个体。相关文献的基本假设是个体被分配至单一地理区位（如居住地）。然而，当考虑公平性时，个体活动范围（即不同区域的移动模式）所对应的位置集合同样具有重要意义。因此，我们认为有必要将空间公平性概念泛化以纳入移动模式，从而衍生出基于个体移动轨迹评估预测模型公平性的新问题。针对该问题，我们提出一种方法：首先将个体移动轨迹关联至特定地理区域（考虑不同粒度和对齐方式的空间划分），然后采用适用的空间扫描统计量，基于移动模式评估预测模型的公平性。实验评估中，我们在数千个合成的不公平数据集上研究方法性能，结果表明该方法能有效检测此类新型不公平现象，并识别遭受不公平对待的对象集合，同时定位性能呈现一致的多分辨率权衡特性。