From Individual Experience to Collective Evidence: A Reporting-Based Framework for Identifying Systemic Harms

When an individual reports a negative interaction with some system, how can their personal experience be contextualized within broader patterns of system behavior? We study the reporting database problem, where individual reports of adverse events arrive sequentially, and are aggregated over time. In this work, our goal is to identify whether there are subgroups—defined by any combination of relevantfeatures—that are disproportionately likely to experience harmful interactions with the system. We formalize this problem as a sequential hypothesis test, and identify conditions on reporting behavior that are sufficient for making inferences about disparities in true rates of harm across subgroups. We show that algorithms for sequential hypothesis tests can be applied to this problem with a standard multiple testingcorrection. We then demonstrate our method on real-world datasets, including mortgage decisions and vaccine side effects; on each, our method (re-)identifies subgroups known to experience disproportionate harm using only a fraction of the data that was initially used to discover them.

When an individual reports a negative interaction with some system, how can their personal experience be contextualized within broader patterns of system behavior? In this work, we propose individual reporting as a pathway to identifying fairness problems in deployed systems. In our model, where individual reports of adverse events arrive sequentially to a reporting database, and are aggregated over time. We show how to use these reports to identify systemic performance issues that disproportionately affect particular subgroups. Finally, we use real-world data (from COVID vaccine side effects and mortgage decisions) as case studies for the effectiveness of our method.