From Individual Experience to Collective Evidence: A Reporting-Based Framework for Identifying Systemic Harms
Jessica Dai, Paula Gradu, Inioluwa Deborah Raji, Benjamin Recht
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.
BibTeX
@inproceedings{
dai2025from,
title={From Individual Experience to Collective Evidence: A Reporting-Based Framework for Identifying Systemic Harms},
author={Jessica Dai and Paula Gradu and Inioluwa Deborah Raji and Benjamin Recht},
booktitle={Forty-second International Conference on Machine Learning},
year={2025},
url={https://openreview.net/forum?id=ywmoIu5t9i}
}