AI Audit Washing and Accountability

Abstract

Algorithmic decision systems, many using artificial intelligence, are reshaping the provision of private and public services across the globe. There is an urgent need for algorithmic governance. Jurisdictions are adopting or considering mandatory audits of these systems to assess compliance with legal and ethical standards or to provide assurance that the systems work as advertised. The hope is that audits will make public agencies and private firms accountable for the harms their algorithmic systems may cause, and thereby lead to harm reductions and more ethical tech. This hope will not be realized so long as the existing ambiguity around the term “audit” persists, and until audit standards are adequate and well-understood. The tacit expectation that algorithmic audits will function like established financial audits or newer human rights audits is fanciful at this stage. In the European Union, where algorithmic audit requirements are most advanced, to the United States, where they are nascent, core questions need to be addressed for audits to become reliable AI accountability mechanisms. In the absence of greater specification and more independent auditors, the risk is that AI auditing becomes AI audit washing. This paper first reports on proposed and enacted transatlantic AI or algorithmic audit provisions. It then draws on the technical, legal, and sociotechnical literature to address the who, what, why, and how of algorithmic audits, contributing to the literature advancing algorithmic governance.