Algorithmic Auditing
Overview
Algorithmic auditing studies how to test automated decisions and the systems around them, especially when errors or disparities affect people. Common approaches compare subgroup outcomes, use counterfactual or causal tests, probe model behavior under perturbations, and apply statistical methods such as sequential testing and uncertainty analysis. Audits increasingly examine deployed pipelines, data and component interactions, strategic responses, and evidence trails—not just a model’s aggregate accuracy. A central challenge is making conclusions reliable and meaningful despite limited access, shifting conditions, incomplete data, and metrics that can be gamed or fail to capture the harms that matter.
Latest papers
143 papers27 papers in the last four weeks, up 59% on the four weeks before. 0.2% of all new papers.