Towards AI Accountability Infrastructure: Gaps and Opportunities in AI Audit Tooling (2402.17861v2)

Abstract: Audits are critical mechanisms for identifying the risks and limitations of deployed AI systems. However, the effective execution of AI audits remains incredibly difficult. As a result, practitioners make use of various tools to support their efforts. Drawing on interviews with 35 AI audit practitioners and a landscape analysis of 390 tools, we map the current ecosystem of available AI audit tools. While there are many tools designed to assist practitioners with setting standards and evaluating AI systems, these tools often fell short of supporting the accountability goals of AI auditing in practice. We thus highlight areas for future tool development beyond evaluation -- from harms discovery to advocacy -- and outline challenges practitioners faced in their efforts to use AI audit tools. We conclude that resources are lacking to adequately support the full scope of needs for many AI audit practitioners and recommend that the field move beyond tools for just evaluation, towards more comprehensive infrastructure for AI accountability.

Bridging Gaps in AI Audit Tooling: Towards Comprehensive Accountability Infrastructure

The manuscript titled "Towards AI Accountability Infrastructure: Gaps and Opportunities in AI Audit Tooling" addresses the significant yet underexplored domain of AI auditing mechanisms. It emphasizes the critical role audits play in identifying potential risks, limitations, and ineffectiveness of AI systems. Despite the increasing policy enthusiasm surrounding AI audits, effective execution remains challenging, largely due to the current shortcomings of available audit tools.

The paper delineates the existing ecosystem of AI audit tools, drawing from interviews with 35 practitioners and a detailed analysis of 390 tools. The authors identify a prevailing focus on evaluation-centric tools, which assist primarily in setting standards and evaluating AI systems. However, these tools fall short of supporting the broader accountability goals associated with AI audits in practice.

Key Findings and Contributions

1. Audit Tool Taxonomy: The authors develop a taxonomy grounded in their exploration of audit tools, identifying a seven-stage process in AI auditing: Harms Discovery, Standards Identification and Management, Data Collection, Transparency Infrastructure, Performance Analysis, Audit Communication, and Advocacy. This framework serves as a basis for understanding the comprehensive tool needs for effective AI auditing.
2. Tool Availability and Gaps: While a substantial number of tools aid in system evaluation—particularly those from large for-profit organizations aimed at performance analysis—there is a dearth of resources focused on critical stages like Harms Discovery, Audit Communication, and Advocacy. This imbalance points to the need for a shift beyond mere evaluation towards developing infrastructure that supports comprehensive accountability.
3. Practitioner Experiences: The insights provided by practitioners reveal existing tool limitations, specifically emphasizing the need for methods and metrics that assure consistency, transparency, and methodological integrity. Practitioners often craft ad hoc solutions or adapt existing tools to meet context-specific demands, underscoring the lack of holistic auditing tools.
4. Challenges of Data Access: A persistent challenge highlighted is the access to quality and untampered data necessary for audits, especially for external auditors. The opacity maintained by AI system operators hinders comprehensive evaluations, raising calls for more transparent and standardized information sharing practices.
5. Potential for Participatory Methods: The paper touches upon the potential of participatory methodologies to enhance auditing by involving affected parties more comprehensively. These approaches could bring essential insights into the harms and impacts of AI systems that might otherwise go unnoticed.

Implications and Future Directions

The paper advocates for a strategic reorientation from toolkits focused solely on evaluation towards an integrated infrastructure conducive to accountable AI auditing. This entails the development of tools and methods that not only assess fairness, explainability, and other performance metrics but also engage in harms discovery, enforce compliance through audit communication, and amplify advocacy through effective stakeholder collaboration.

For future research and development, building open, shared infrastructures for data access will be crucial—ensuring external auditors have the means to critically engage with AI systems. Moreover, fostering collaborative environments where AI developers, auditors, and affected communities can curate shared goals and auditing standards is vital for furthering systemic accountability.

Policymakers and funding agencies must allocate resources to sustain the long-term development and maintenance of audit tools, incorporating provisions to safeguard independent auditors legally and structurally. The community must envisage a collective move towards accountability that incorporates participatory elements, leveraging both technological and non-technological solutions to realign AI practices with societal expectations.

In conclusion, the paper serves as a significant contribution towards recognizing and addressing the gaps in AI audit tooling. It calls for a comprehensive approach that weaves diverse tools and methodologies into a robust accountability infrastructure, ensuring AI systems responsibly serve societal needs and expectations.