Goodman & Tréhu - Algorithmic Auditing - AI Audit-Washing and Accountability - Policy Paper

Oct 23, 2026

AI audit-washing and accountability explores how algorithmic decision systems increasingly face audit demands amid rapid AI governance developments. It argues that the “algorithmic audit” remains ill-defined, so inadequate or unstandardized audits can hide harms, provide false compliance assurance, and legitimize problematic practices. The paper lays out core reliability questions—who audits, what is audited and its scope, why audits are conducted, and how standards should be articulated—so audits can prevent audit-washing and support trustworthy AI accountability.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages26

Reading Time1 h 6 min

AccessFree to download

Summary

Introduction

Motivations for algorithmic audits

Risks of ill-defined audits and audit-washing

Core questions for reliable algorithmic audits

Who: auditor qualifications and guardrails

What: audit type, scope, and sociotechnical context

Why: audit objectives and comparability

How: audit standards and certification baselines

Conclusion

November 2022 AI Audit-Washing and Accountability Ellen P. Goodman and Julia Tréhu Policy Paper November 2022 Summary We are still some distance from a worldwide robot takeover, but artificial intelligence (AI)—the training of computer systems with large data sets to make decisions and solve problems—is revolutionizing the way governments and societies function. AI has enormous potential: accelerating innovation, unlocking new value from data, and increasing productivity by freeing us from mundane tasks. AI can draw new inferences from health data to foster breakthroughsin cancer screening or improve climate modeling and early-warning systems for extreme weather or emergency situations. As we seek solutions to today’s vexing problems—climate disruption, social inequality, health crises—AI will be central. Its centrality requires that stakeholders exercise greater governance over AI and hold AI systems accountable for their potential harms, including discriminatory impact, opacity, error, insecurity, privacy violations, and disempowerment. In this context, calls for audits to assess the impact of algorithmic decision-making systems and expose and mitigate related harms are proliferating, accompanied by the rise of an algorithmic auditing industry and legal codification. These are welcome developments. Audits can provide a flexible co-regulatory solution, allowing necessary innovation in AI while increasing transparency and accountability. AI is a crucial element of the growing tech competition between authoritarian and democratic states—and ensuring that AI is accountable and trusted is a key part of ensuring democratic advantage. Clear standards for trustworthy AI will help the United States remain a center of innovation and shape technology to democratic values. The “algorithmic audit” nevertheless remains ill-defined and inexact, whether concerning social media platforms or AI systems generally. The risk is significant that inadequate audits will obscure problems with algorithmic systems and create a permission structure around poorly designed or implemented AI. A poorly designed or executed audit is at best meaningless and at worst even excuses harms that the audits claim to mitigate. Inadequate audits or those without clear standards provide false assurance of compliance with norms and laws,“audit-washing” problematic or illegal practices. Like green-washing and ethicswashing before, the audited entity can claim credit without doing the work. To address these risks, this paper identifies the core questions that need answering to make algorithmic auditsa reliable AI accountability mechanism. The “who” of audits includes the person or organization conducting the audit, with clearly defined qualifications, conditions for data access, and guardrails for internal audits. The “what” includes the type and scope of audit, including its position within a larger sociotechnical system. The “why” covers audit objectives, whether narrow legal standards or broader ethical goals, essential for audit comparison. Finally, the “how” includes a clear articulation of audit standards, an important baseline for the development of audit certification mechanisms and to guard against audit-washing. Algorithmic audits have the potential to transform the way technology works in the 21st century, much as financial audits transformed the way businesses operated in the 20th century. They will take different forms, either within a sector or across sectors, especially for systems which pose the highest risk. But as algorithmic audits are encoded into law or adopted voluntarily as part of corporate social responsibility, the audit industry must arrive at shared understandings and expectations of audit goals and procedures. This paper provides such an outline so that truly meaningful algorithmic audits can take their deserved place in AI governance frameworks.1 1 “Aversion of this paper will be published in the Santa Clara High Tech

FAQs

Why are algorithmic audits becoming more common?

Calls for audits to expose and mitigate harms from algorithmic decision systems are growing, supported by legal and regulatory provisions such as the EU Digital Services Act. An expanding audit industry also reflects these concerns.

What risk does the paper highlight about poorly designed audits?

The paper warns that inadequate audits can obscure problems in algorithmic systems and create a permission structure for poorly designed AI. At best they are meaningless, and at worst they excuse harms while claiming mitigation.

What core questions should reliable algorithmic audits answer?

Reliable audits must address who conducts the audit, what type and scope of audit is performed, why the audit is carried out (objectives and standards), and how audit standards are clearly articulated to prevent audit-washing.

Oct 23, 2026

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