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

2026-01-30

AI Summary

Current third-party evaluations of AI systems lack systematicity, transparency, full lifecycle coverage, and empirical validation. This work proposes the first third-party AI assurance framework that explicitly distinguishes “assurance” from “audit,” incorporates a holistic AI lifecycle perspective, and draws on established practices from the accounting domain to develop an operational and reusable toolkit. This toolkit includes a responsibility allocation matrix, a structured stakeholder interview protocol, a maturity assessment model, and a standardized assurance reporting template. The framework’s effectiveness is demonstrated through two case studies—an enterprise document-tagging tool and a public housing allocation system—showcasing its comprehensiveness, cross-organizational applicability, and ability to identify AI-specific issues.

Technology Category

• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Humans and AI: AI for Accessibility
• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy

Application Category

• Responsible Web: Algorithmic accountability and transparency on the web
• Security and Privacy: Data transparency and provenance
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

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.

Problem

• AI assurance
• third-party evaluation
• AI lifecycle
• accountability
• evaluation framework

Innovation

• AI assurance
• third-party evaluation
• responsibility assignment matrix
• maturity matrix
• assurance framework