Access Denied: Meaningful Data Access for Quantitative Algorithm Audits

要旨

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.

受賞
Honorable Mention
著者
Juliette Zaccour
University of Oxford, Oxford, United Kingdom
Reuben Binns
University of Oxford, Oxford, United Kingdom
Luc Rocher
University of Oxford, Oxford, United Kingdom
DOI

10.1145/3706598.3713963

論文URL

https://dl.acm.org/doi/10.1145/3706598.3713963

動画

会議: CHI 2025

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2025.acm.org/)

セッション: Engaging with Data

Annex Hall F206
7 件の発表
2025-04-30 01:20:00
2025-04-30 02:50:00
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