Actionable Meta-Requirements for AI Robustness Auditing

A systematic literature review retained 74 of 2,890 records and derived 43 meta-requirements across 16 categories for AI robustness auditing frameworks, according to an arXiv paper (2610.14356v1). The authors evaluated the requirements with 128 practitioners, collecting 5,504 ratings and 586 substantive comments, with 81.20% of ratings indicating agreement or strong agreement and 41 of the 43 literature-derived meta-requirements classified as strongly supported. Practitioner feedback added one requirement concerning external components and supply chain dependencies, bringing the final set to 44 meta-requirements across 16 categories, 43 of which received quantitative validation.

arXiv:2610.14356v1 Announce Type: new Abstract: AI-enabled systems must remain dependable when the conditions under which they operate change e.g., due to input perturbations, distribution shifts, or changes in external dependencies . This makes robustness auditing of such systems important for judging whether the available evidence is sufficient to support a defined robustness conclusion. However, designing an AI robustness auditing framework is challenging. A key unresolved issue is determining the requirements such a framework should support; these requirements have not yet been consolidated. We address this gap through meta-requirements: higher-order requirements specifying what a class of auditing frameworks should support. In design science research, literature and domain experts are common sources of meta-requirements; accordingly, we derive the initial set from literature and validate and refine it with practitioners. Our systematic literature review retained 74 of 2,890 records and derived 43 meta-requirements across 16 categories. We evaluated them with 128 practitioners, obtaining 5,504 ratings and 586 substantive comments. Overall, 81.20% of ratings indicated agreement or strong agreement. In particular, 41 of the 43 literature-derived meta-requirements were classified as strongly supported. Practitioner feedback refined the requirements and identified an additional requirement concerning external components and supply chain dependencies. The final set comprises 44 meta-requirements across 16 categories, of which 43 received quantitative validation. These meta-requirements can help organizations implement AI robustness auditing frameworks, assess existing frameworks for completeness, and refine them.