A vast number of systems across the world use algorithmic decision making (ADM) to (partially) automate decisions that have previously been made by humans. When designed well, these systems promise more objective decisions while saving large amounts of resources and freeing up human time. However, when ADM systems are not designed well, they can lead to unfair decisions which discriminate against societal groups. The downstream effects of ADMs critically depend on the decisions made during the systems' design and implementation, as biases in data can be mitigated or reinforced along the modeling pipeline. Many of these design decisions are made implicitly, without knowing exactly how they will influence the final system. It is therefore important to make explicit the decisions made during the design of ADM systems and understand how these decisions affect the fairness of the resulting system. To study this issue, we draw on insights from the field of psychology and introduce the method of multiverse analysis for algorithmic fairness. In our proposed method, we turn implicit design decisions into explicit ones and demonstrate their fairness implications. By combining decisions, we create a grid of all possible "universes" of decision combinations. For each of these universes, we compute metrics of fairness and performance. Using the resulting dataset, one can see how and which decisions impact fairness. We demonstrate how multiverse analyses can be used to better understand variability and robustness of algorithmic fairness using an exemplary case study of predicting public health coverage of vulnerable populations for potential interventions. Our results illustrate how decisions during the design of a machine learning system can have surprising effects on its fairness and how to detect these effects using multiverse analysis.

翻译：全球众多系统采用算法决策（ADM）来（部分）自动化此前由人类完成的决策。设计得当的算法决策系统有望实现更客观的决策，同时节省大量资源并释放人力时间。然而，若算法决策系统设计不当，可能导致歧视社会群体的不公平决策。算法决策系统的下游效应关键取决于系统设计与实施过程中的决策——数据中的偏差可能在建模流程中被缓解或强化。许多此类设计决策是在隐式状态下做出的，难以预知其对最终系统的确切影响。因此，明确算法决策系统设计过程中做出的决策，并理解这些决策如何影响最终系统的公平性至关重要。为研究该问题，我们借鉴心理学领域的洞见，将多宇宙分析方法引入算法公平性研究。在所提出的方法中，我们将隐式设计决策转化为显式决策，并揭示其公平性影响。通过组合不同决策，我们构建了所有决策组合“宇宙”的网格，并为每个宇宙计算公平性与性能指标。借助生成的数据集，可观察决策如何影响公平性及哪些决策产生影响。我们以一项针对弱势群体公共卫生覆盖率的预测干预案例研究，演示如何运用多宇宙分析更深入地理解算法公平性的变异性与鲁棒性。研究结果揭示了机器学习系统设计过程中的决策如何对其公平性产生出人意料的影响，以及如何通过多宇宙分析检测这些影响。