Outsider Oversight: Designing a Third Party Audit Ecosystem for AI Governance (2206.04737v1)

Abstract: Much attention has focused on algorithmic audits and impact assessments to hold developers and users of algorithmic systems accountable. But existing algorithmic accountability policy approaches have neglected the lessons from non-algorithmic domains: notably, the importance of interventions that allow for the effective participation of third parties. Our paper synthesizes lessons from other fields on how to craft effective systems of external oversight for algorithmic deployments. First, we discuss the challenges of third party oversight in the current AI landscape. Second, we survey audit systems across domains - e.g., financial, environmental, and health regulation - and show that the institutional design of such audits are far from monolithic. Finally, we survey the evidence base around these design components and spell out the implications for algorithmic auditing. We conclude that the turn toward audits alone is unlikely to achieve actual algorithmic accountability, and sustained focus on institutional design will be required for meaningful third party involvement.

• The paper identifies the limitations of internal audits and details a framework for third-party oversight to uncover biases in AI deployments.
• It applies empirical evidence from financial, environmental, and health sectors to define key audit design elements like auditor independence and post-audit transparency.
• The paper advocates establishing oversight boards and national reporting systems to ensure unbiased, effective accountability in AI governance.

Third Party Audit Ecosystems for AI Governance

The paper by Raji, Xu, Honigsberg, and Ho explores the conceptual framework required for establishing robust third-party audit systems to govern algorithmic deployments. The authors recognize the pivotal role that external oversight bodies play in unearthing biases within AI systems, pointing out the deficiency in current policy frameworks that predominantly rely on internal audits. They embark on an interdisciplinary exploration of how external audit systems are designed in various non-algorithmic domains to offer a blueprint for AI governance.

The discussion begins by highlighting the limitations faced by existing AI accountability policies. The authors stress the excessive reliance on internal audits conducted by organizations themselves, which overlooks the critical insights that independent third-party audits bring. These internal audits are often misaligned with the broad scope of AI ethics, failing to address nuanced accountability issues raised by AI deployments.

The authors use empirical evidence from diverse sectors, including financial, environmental, and health regulation audit frameworks, to underpin their synthesis. They argue for a detailed institutional design of algorithmic audits, focusing on clear audit scopes, auditor independence, privileged access, professionalization, and post-audit transparency. Among the stark revelations is the significant shortcomings of audit precision and selection, which necessitate a national incident reporting system to prioritize audit targets and focus auditor resources on substantive AI-related issues.

Interestingly, the paper stresses the nuanced interplay between independence and audit quality, underscoring the potential conflicts of interest when audit entities are remunerated by auditees. It advocates for reforms such as the creation of an audit oversight board to preclude conflicts and align auditors' actions with broader accountability objectives.

Moreover, the issue of obtaining adequate auditor access to vital data and systems forms a substantial barrier to effective AI auditing. The authors call for structured but safeguarded access arrangements, similar to other regulated domains, to navigate proprietary concerns while ensuring comprehensive audits.

An intriguing observation is the comparison of AI products with existing systems already subject to regulatory mechanisms, such as self-driving cars and medical AI tools. This perspective aligns with the paper's overarching recommendation for AI governance to draw on lessons learned from established audit systems, adding depth to debates within AI policy circles.

The implications extend to potential policy interventions for enabling a vibrant third-party audit landscape, suggesting a shift from mere advocacy for auditing to intentional and precise institutional policy designs. The paper positions this perspective as critical to enhancing AI accountability, striving for an environment where third-party auditing is not only feasible but integral to the algorithmic governance framework.

Finally, Raji et al. articulate the pivotal impact of explicit post-audit disclosure requirements and the public registration of audit results, enhancing transparency and stimulating corrective actions from AI vendors. The provision of evidence-based audit outcomes also offers a template for evolving performance standards in algorithmic contexts. The paper pushes for a trajectory that allows third parties to contribute significantly to the rectification and illumination of opaque AI-driven systems, thus safeguarding vulnerable populations from system-induced harms.

Overall, this paper provides a comprehensive academic investigation into designing a viable third-party audit ecosystem for AI governance, appealing to regulators and policymakers to embrace a holistic approach informed by validated audit practices in non-algorithmic disciplines. The implications highlighted serve as a cornerstone for future AI policy developments aimed at fortifying third-party oversight capabilities.