Who Audits the Auditors? Recommendations from a field scan of the algorithmic auditing ecosystem

Abstract

Algorithmic audits (or 'AI audits') are an increasingly popular mechanism for algorithmic accountability; however, they remain poorly defined. Without a clear understanding of audit practices, let alone widely used standards or regulatory guidance, claims that an AI product or system has been audited, whether by first-, second-, or third-party auditors, are difficult to verify and may potentially exacerbate, rather than mitigate, bias and harm. To address this knowledge gap, we provide the first comprehensive field scan of the AI audit ecosystem. We share a catalog of individuals (N=438) and organizations (N=189) who engage in algorithmic audits or whose work is directly relevant to algorithmic audits; conduct an anonymous survey of the group (N=152); and interview industry leaders (N=10). We identify emerging best practices as well as methods and tools that are becoming commonplace, and enumerate common barriers to leveraging algorithmic audits as effective accountability mechanisms. We outline policy recommendations to improve the quality and impact of these audits, and highlight proposals with wide support from algorithmic auditors as well as areas of debate. Our recommendations have implications for lawmakers, regulators, internal company policymakers, and standards-setting bodies, as well as for auditors. They are: 1) require the owners and operators of AI systems to engage in independent algorithmic audits against clearly defined standards; 2) notify individuals when they are subject to algorithmic decision-making systems; 3) mandate disclosure of key components of audit findings for peer review; 4) consider real-world harm in the audit process, including through standardized harm incident reporting and response mechanisms; 5) directly involve the stakeholders most likely to be harmed by AI systems in the algorithmic audit process; and 6) formalize evaluation and, potentially, accreditation of algorithmic auditors.

Commentary on Healthcare and Disruptive Innovation

“…It was noted in the vein of predictive medicine through Hermansson and Kahan (2018) that overtraining an AI healthcare algorithm on Caucasian data produced poorer predictive care for non-Caucasian groups. This issue falls in line with the poor application of AI in other areas and is an example of how their use has far to go in suitability for routine, un-aided use without perpetuating negative or unhelpful feedback loops of care (Costanza-Chock et al 2022;Hoffman,2021;Gebru,2020;Kong,2022). Many sources of electronic medical records systems hold incomplete connectedness, communication, and completeness, making it difficult for them to be used optimally (Patch et al,2019).…”

“Hey SyRI, tell me about algorithmic accountability”: Lessons from a landmark case

“…AI auditing can be done internally (ibid.) or by hiring an impartial third-party auditor (Costanza-Chock et al, 2022). However, as Constanza-Chock et al (ibid.)…”

Identifying Sociotechnical Harms of Algorithmic Systems: Scoping a Taxonomy for Harm Reduction

“…More generally, these harms may erode democracy [87], through election interference or censorship [182]. Moreover, algorithmic systems may exacerbate social inequalities and reduction of civil liberties within legal systems [120,160], such as unreasonable searches [132], wrongful arrest [52,52,107], or court transcription errors [107]. These harms adversely impact how a nation's institutions or services function [3] and increase societal polarization [182].…”