Disclosure or Marketing? Analyzing the Efficacy of Vendor Self-reports for Vetting Public-sector AI
Blaine Kuehnert, Nari Johnson, Ravit Dotan, Hoda Heidari

TL;DR
This study examines how vendor self-reports like FactSheets are used in public-sector AI procurement, revealing their strengths and limitations in fostering trust and accountability.
Contribution
It provides empirical insights into the production, interpretation, and practical use of AI disclosure artifacts in government contexts.
Findings
FactSheets serve multiple purposes, including showcasing offerings and supporting evaluation.
Structural constraints limit FactSheets' effectiveness as standalone risk tools.
When viewed as relational artifacts, FactSheets can foster trust and ongoing dialogue.
Abstract
Documentation-based disclosure has become a central governance strategy for responsible AI, particularly in public-sector procurement. Tools such as model cards, datasheets, and AI FactSheets are increasingly expected to support accountability, risk assessment, and informed decision-making across organizational boundaries. Yet there is limited empirical evidence about how these artifacts are produced, interpreted, and used in practice. In this paper, we present a qualitative study of the GovAI Coalition FactSheet, a widely adopted transparency document designed to support AI procurement and governance in government contexts. Drawing on semi-structured interviews with vendors and public-sector practitioners, alongside a systematic analysis of completed FactSheets, we examine how FactSheets are used, what information they surface, and where they fall short. We find that FactSheets are…
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