Toward Third-Party Assurance of AI Systems: Design Requirements, Prototype, and Early Testing

• URL: http://arxiv.org/abs/2601.22424v1
• Date: Fri, 30 Jan 2026 00:37:12 GMT
• Title: Toward Third-Party Assurance of AI Systems: Design Requirements, Prototype, and Early Testing
• Authors: Rachel M. Kim, Blaine Kuehnert, Alice Lai, Kenneth Holstein, Hoda Heidari, Rayid Ghani,
• Abstract summary: We introduce a third-party AI assurance framework that addresses gaps in AI evaluation.<n>We focus on third-party assurance to prevent conflict of interest and ensure credibility and accountability of the process.<n>Our findings show early evidence that our AI assurance framework is sound and comprehensive, usable across different organizational contexts.
• Score: 16.53658640529767
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As Artificial Intelligence (AI) systems proliferate, the need for systematic, transparent, and actionable processes for evaluating them is growing. While many resources exist to support AI evaluation, they have several limitations. Few address both the process of designing, developing, and deploying an AI system and the outcomes it produces. Furthermore, few are end-to-end and operational, give actionable guidance, or present evidence of usability or effectiveness in practice. In this paper, we introduce a third-party AI assurance framework that addresses these gaps. We focus on third-party assurance to prevent conflict of interest and ensure credibility and accountability of the process. We begin by distinguishing assurance from audits in several key dimensions. Then, following design principles, we reflect on the shortcomings of existing resources to identify a set of design requirements for AI assurance. We then construct a prototype of an assurance process that consists of (1) a responsibility assignment matrix to determine the different levels of involvement each stakeholder has at each stage of the AI lifecycle, (2) an interview protocol for each stakeholder of an AI system, (3) a maturity matrix to assess AI systems' adherence to best practices, and (4) a template for an assurance report that draws from more mature assurance practices in business accounting. We conduct early validation of our AI assurance framework by applying the framework to two distinct AI use cases -- a business document tagging tool for downstream processing in a large private firm, and a housing resource allocation tool in a public agency -- and conducting expert validation interviews. Our findings show early evidence that our AI assurance framework is sound and comprehensive, usable across different organizational contexts, and effective at identifying bespoke issues with AI systems.

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