雷达下--人道主义制图ML中的公平性审计
Under the Radar -- Auditing Fairness in ML for Humanitarian Mapping
摘要：
利用机器学习从太空进行人道主义测绘有助于决策者及时准确地识别有需要的人。然而，最近对算法决策的公平性和透明度的担忧是将这些方法应用于实践的一个重大障碍。在本文中，我们研究，如果从空间人道主义测绘方法容易在他们的预测偏见。我们绘制乡村一级的贫困和电力价格在印度的基础上夜间灯光（NTLs）与线性回归和随机森林和分析，如果预测系统地显示对预定种姓或部落社区的偏见。为了实现这一点，我们设计了一个因果关系的方法来衡量反事实的公平性的基础上倾向得分匹配。这允许将利益共同体中的村庄与合成反事实进行比较。我们的研究结果表明，贫困是系统性的高估和电力系统低估了预定部落相比，一个合成的反事实的村庄。对贱民种姓的影响则相反，因为这些种姓的贫困被低估，而电气化被高估。这些结果是一个警告信号，在人道主义绘图的各种应用中，公平问题会损害政策目标。
英文摘要：
Humanitarian mapping from space with machine learning helps policy-makers to timely and accurately identify people in need. However, recent concerns around fairness and transparency of algorithmic decision-making are a significant obstacle for applying these methods in practice. In this paper, we study if humanitarian mapping approaches from space are prone to bias in their predictions. We map village-level poverty and electricity rates in India based on nighttime lights (NTLs) with linear regression and random forest and analyze if the predictions systematically show prejudice against scheduled caste or tribe communities. To achieve this, we design a causal approach to measure counterfactual fairness based on propensity score matching. This allows to compare villages within a community of interest to synthetic counterfactuals. Our findings indicate that poverty is systematically overestimated and electricity systematically underestimated for scheduled tribes in comparison to a synthetic counterfactual group of villages. The effects have the opposite direction for scheduled castes where poverty is underestimated and electrification overestimated. These results are a warning sign for a variety of applications in humanitarian mapping where fairness issues would compromise policy goals.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
