Access Denied: Meaningful Data Access for Quantitative Algorithm Audits

TL;DR

This paper investigates how limited data access affects the accuracy of independent algorithm audits, highlighting that data minimization practices can significantly impair audit reliability and proposing ways to improve data access for better accountability.

Contribution

It provides an empirical analysis of data access restrictions on audit accuracy and discusses strategies to enhance data sharing for more reliable algorithm evaluations.

Findings

Data minimization increases error rates in audits.

Limited access impairs accurate estimation of group parity.

Even simple tasks are affected by data restrictions.

Abstract

Independent algorithm audits hold the promise of bringing accountability to automated decision-making. However, third-party audits are often hindered by access restrictions, forcing auditors to rely on limited, low-quality data. To study how these limitations impact research integrity, we conduct audit simulations on two realistic case studies for recidivism and healthcare coverage prediction. We examine the accuracy of estimating group parity metrics across three levels of access: (a) aggregated statistics, (b) individual-level data with model outputs, and (c) individual-level data without model outputs. Despite selecting one of the simplest tasks for algorithmic auditing, we find that data minimization and anonymization practices can strongly increase error rates on individual-level data, leading to unreliable assessments. We discuss implications for independent auditors, as well as potential avenues for HCI researchers and regulators to improve data access and enable both reliable and holistic evaluations.