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

TL;DR

This paper introduces a sequential hypothesis testing framework to identify subgroups experiencing disproportionate harm from systems, effectively analyzing individual reports over time to detect systemic disparities.

Contribution

It formalizes the reporting database problem as a sequential hypothesis test and demonstrates its effectiveness on real-world datasets for detecting systemic harms.

Findings

Successfully identified known harm subgroups with less data

Applicable to real-world datasets like mortgage decisions and vaccine side effects

Provides a method for ongoing monitoring of systemic disparities

Abstract

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 relevant features--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 testing correction. 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.