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A vast number of systems across the world use algorithmic decision making(ADM) to (partially) automate decisions that have previously been made byhumans. When designed well, these systems promise more objective decisionswhile saving large amounts of resources and freeing up human time. However,when ADM systems are not designed well, they can lead to unfair decisions whichdiscriminate against societal groups. The downstream effects of ADMs criticallydepend on the decisions made during the systems' design and implementation, asbiases in data can be mitigated or reinforced along the modeling pipeline. Manyof these design decisions are made implicitly, without knowing exactly how theywill influence the final system. It is therefore important to make explicit thedecisions made during the design of ADM systems and understand how thesedecisions affect the fairness of the resulting system.  To study this issue, we draw on insights from the field of psychology andintroduce the method of multiverse analysis for algorithmic fairness. In ourproposed method, we turn implicit design decisions into explicit ones anddemonstrate their fairness implications. By combining decisions, we create agrid of all possible "universes" of decision combinations. For each of theseuniverses, we compute metrics of fairness and performance. Using the resultingdataset, one can see how and which decisions impact fairness. We demonstratehow multiverse analyses can be used to better understand variability androbustness of algorithmic fairness using an exemplary case study of predictingpublic health coverage of vulnerable populations for potential interventions.Our results illustrate how decisions during the design of a machine learningsystem can have surprising effects on its fairness and how to detect theseeffects using multiverse analysis.

Everything, Everywhere All in One Evaluation: Using Multiverse Analysis to Evaluate the Influence of Model Design Decisions on Algorithmic Fairness