From Transparency to Accountability and Back: A Discussion of Access and Evidence in AI Auditing

Artificial intelligence (AI) is increasingly intervening in our lives, raising widespread concern about its unintended and undeclared side effects. These developments have brought attention to the problem of AI auditing: the systematic evaluation and analysis of an AI system, its development, and its behavior relative to a set of predetermined criteria. Auditing can take many forms, including pre-deployment risk assessments, ongoing monitoring, and compliance testing. It plays a critical role in providing assurances to various AI stakeholders, from developers to end users. Audits may, for instance, be used to verify that an algorithm complies with the law, is consistent with industry standards, and meets the developer’s claimed specifications. However, AI developers and companies will rarely grant auditors unfettered access to their systems.