AI auditing: The Broken Bus on the Road to AI Accountability
Abstract
One of the most concrete measures to take towards meaningful AI accountability is to consequentially assess and report the systems' performance and impact. However, the practical nature of the "AI audit" ecosystem is muddled and imprecise, making it difficult to work through various concepts and map out the stakeholders involved in the practice. First, we taxonomize current AI audit practices as completed by regulators, law firms, civil society, journalism, academia, consulting agencies. Next, we assess the impact of audits done by stakeholders within each domain. We find that only a subset of AI audit studies translate to desired accountability outcomes. We thus assess and isolate practices necessary for effective AI audit results, articulating the observed connections between AI audit design, methodology and institutional context on its effectiveness as a meaningful mechanism for accountability.
Keywords
Cite
@article{arxiv.2401.14462,
title = {AI auditing: The Broken Bus on the Road to AI Accountability},
author = {Abeba Birhane and Ryan Steed and Victor Ojewale and Briana Vecchione and Inioluwa Deborah Raji},
journal= {arXiv preprint arXiv:2401.14462},
year = {2024}
}
Comments
To appear in the proceedings of the 2nd IEEE Conference on Secure and Trustworthy Machine Learning (SaTML) 2024