Towards AI Accountability Infrastructure: Gaps and Opportunities in AI Audit Tooling (2402.17861v3)

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, and practitioners often need to make use of various tools to support their efforts. Drawing on interviews with 35 AI audit practitioners and a landscape analysis of 435 tools, we compare the current ecosystem of AI audit tooling to practitioner needs. While many tools are designed to help set standards and evaluate AI systems, they often fall short in supporting accountability. We outline challenges practitioners faced in their efforts to use AI audit tools and highlight areas for future tool development beyond evaluation -- from harms discovery to advocacy. We conclude that the available resources do not currently support the full scope of AI audit practitioners' needs and recommend that the field move beyond tools for just evaluation and towards more comprehensive infrastructure for AI accountability.

• The paper identifies that current AI audit tools are concentrated on performance analysis and standards, with significant gaps in harm discovery, transparency, and audit communication.
• The study reveals that the lack of comprehensive, context-specific frameworks impedes effective risk assessment and bias evaluation in AI systems.
• The research advocates for a shift towards accountable audit infrastructures supported by open-source tools, legal safeguards, and enhanced institutional collaboration.

Towards AI Accountability Infrastructure: Gaps and Opportunities in AI Audit Tooling

The paper "Towards AI Accountability Infrastructure: Gaps and Opportunities in AI Audit Tooling" surveys the current landscape of AI audit tools and proposes paths forward for achieving meaningful accountability. It highlights the challenges that audit practitioners face and identifies a need to move beyond evaluation-centric tools toward more comprehensive AI accountability mechanisms.

Current Landscape of AI Audit Tools

AI audit tools currently fall into seven stages within an audit process: Harms Discovery, Standards Identification and Management, Transparency Infrastructure, Data Collection, Performance Analysis, Audit Communication, and Advocacy. A landscape analysis of 390 tools showcased that most tools are concentrated in Performance Analysis and Standards Identification, with fewer tools aiding in Harms Discovery, Transparency Infrastructure, and crucial stages that ensure accountability, such as Audit Communication and Advocacy.

Figure 1: Stages of the tool-supported audit process surfaced in our survey of AI audit tooling. We taxonomize tools by the stage of the AI audit process in which they are meant to be used.

Tool-related Challenges

Identification and Discovery of Harms

Audit practitioners often struggle with identifying AI systems and understanding potential harms due to limited accessible information. Participatory approaches are necessary for comprehensive audits, but few tools are available for engaging affected communities in the identification of harms. The paper suggests tools that promote participation and transparency in harm discovery can fill this gap.

Standards and Evaluation Framework

Though many standards and evaluation frameworks exist, they are often perceived as lacking context specificity and comprehensiveness. Practitioners emphasized the need for frameworks that are grounded in practical, holistic, and context-specific guidelines. The lack of a clear framework means practitioners are often left to devise their methods for risk assessment and bias evaluations.

Figure 2: Number of tools in each category of our taxonomy, grouped by type of organization.

Data Access and Transparency

External auditors face significant hurdles in accessing necessary data for meaningful audits. Existing transparency tools are often proprietary and limited in scope. There is a strong need for tools that provide uncompromised and secure access to both data and model APIs, enabling more robust and independent evaluations.

Pathways Towards Comprehensive Infrastructure

Expanding From Evaluation to Accountability

The current focus on evaluation tools needs to shift towards comprehensive infrastructure that encompasses all stages of the audit process. Particularly, developing tools that facilitate harms discovery, audit communication, and advocacy are crucial for moving beyond evaluation and achieving accountability.

Open Source and Legal Protections

The paper argues for more open-source audit tools to allow independent verification and improvement of methodologies. Legal protections are also critical to shield auditors from the potential legal risks that can arise from using such tools, fostering a more open and innovative landscape for audit tools.

Institutional Support and Collaboration

There needs to be a strong commitment to maintaining audit tools through institutional support and collaboration. Initiatives that centralize tool resources, encourage cross-discipline communication, and establish an ongoing dialogue between practitioners and tool developers are vital for creating a sustainable audit tool ecosystem.

Conclusion

The paper calls for a paradigm shift from ad hoc tools to a well-supported infrastructure that fully supports AI accountability. This will require collaborative efforts to create centralized resources, secure funding, and protect the auditor's role within the ecosystem. This transformation is essential for evolving audit tools from a focus solely on evaluation to facilitating the complete cycle of accountability in AI systems.