Can model attribution bridge AI's accountability gap in safety-critical domains?

The current trend of deploying machine learning models as remote services obscures which specific models users are interacting with, exacerbating an accountability gap that becomes ever more pressing as these technologies make their way into safety-critical domains. In this paper, we examine how model attribution, a recently proposed accountability property, can address this gap. We review recent technical approaches to provide model attribution and identify limitations, such as computational costs, latency overheads and insufficient guarantees, that render them inadequate for many safety-critical applications. We conclude that although model attribution is an important step towards accountability, current technical solutions fall short of the requirements of safety-critical domains. This article is part of the theme issue 'Safe, secure and robust AI for safety-critical systems'.