Governments Need Better Safeguards Before Deploying AI Models for Policy
A new framework establishes how governments should test and verify AI agent-based models before using them to shape public policy. The research exposes critical gaps in how most institutions currently validate these models, creating risks of flawed decisions affecting millions of citizens and significant policy budgets.
Originaltitel: [In]Credible Models – Verification, Validation & Accreditation of Agent-Based Models to Support Policy-Making
<p>This paper explores the topic of model credibility of Agent-based Models and how they should be evaluated prior to application in policy-making. Specifically, this involves analyzing bordering literature from different fields to: (1) establish a definition of model credibility -- a measure of confidence in the model's inferential capability -- and to (2) assess how model credibility can be strengthened through Verification, Validation, and Accreditation (VV&A) prior to application, as well as through post-application evaluation. Several studies have highlighted severe shortcomings in how V&V of Agent-based Models is performed and documented, and few public administrations have an established process for model accreditation. To address the first issue, we examine the literature on model V&V and, based on this review, introduce and outline the usage of a V&V plan. To address the second issue, we take inspiration from a practical use case of model accreditation applied by a government institution to propose a framework for the accreditation of ABMs for policy-making. The paper concludes with a discussion of the risks associated with improper assessments of model credibility. </p>