Who Audits the Auditors? Recommendations from a Field Scan of the Algorithmic Auditing Ecosystem
Through a field scan, this paper identifies emerging best practices as well as methods and tools that are becoming commonplace, and enumerates common barriers to leveraging algorithmic audits as effective accountability mechanisms.
The document reviews the current landscape of algorithmic (AI) auditing, emphasizing the absence of standardized practices and regulatory oversight, as well as the challenges auditors face in holding AI systems accountable.
It notes consensus on the need for mandatory independent audits, transparency for affected individuals, and public disclosure of audit results, while also highlighting barriers such as limited enforcement, lack of auditee cooperation, and a gap between best practices and real-world implementation. The authors propose six policy recommendations, including greater stakeholder involvement and formalized auditor accreditation, to strengthen AI audit effectiveness.
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