How VADER is your AI? Towards a definition of artificial intelligence systems appropriate for regulation
🤖 AI Summary
Current AI regulation suffers from ambiguous definitions and cross-jurisdictional inconsistency, risking the inadvertent inclusion of non-AI techniques—such as mathematical or statistical methods—within regulatory scope. To address this, we propose VADER: the first computable, publicly available evaluation framework for assessing the *fitness* of AI definitions, grounded in a multi-class benchmark corpus (AI/non-AI, ICT/non-ICT). VADER quantifies definition quality along three dimensions—coverage, exclusivity, and alignment with foundational principles. Applying it to policy texts and a curated repository of representative technical cases, we evaluate four major regulatory proposals (U.S., U.K., EU, Brazil) and find that none meet the threshold for reasonable definitional fitness; all exhibit significant over-inclusion risks, particularly concerning foundational methodologies. This work establishes a reproducible, empirically grounded paradigm for evaluating AI definitions—advancing rigor, transparency, and interoperability in AI governance.
Application Category
📝 Abstract
Artificial intelligence (AI) has driven many information and communication technology (ICT) breakthroughs. Nonetheless, the scope of ICT systems has expanded far beyond AI since the Turing test proposal. Critically, recent AI regulation proposals adopt AI definitions affecting ICT techniques, approaches, and systems that are not AI. In some cases, even works from mathematics, statistics, and engineering would be affected. Worryingly, AI misdefinitions are observed from Western societies to the Global South. In this paper, we propose a framework to score how validated as appropriately-defined for regulation (VADER) an AI definition is. Our online, publicly-available VADER framework scores the coverage of premises that should underlie AI definitions for regulation, which aim to (i) reproduce principles observed in other successful technology regulations, and (ii) include all AI techniques and approaches while excluding non-AI works. Regarding the latter, our score is based on a dataset of representative AI, non-AI ICT, and non-ICT examples. We demonstrate our contribution by reviewing the AI regulation proposals of key players, namely the United States, United Kingdom, European Union, and Brazil. Importantly, none of the proposals assessed achieve the appropriateness score, ranging from a revision need to a concrete risk to ICT systems and works from other fields.
Problem
Research questions and friction points this paper is trying to address.
Artificial Intelligence Definition
Regulatory Framework
Technological Distinction
Innovation
Methods, ideas, or system contributions that make the work stand out.
Online Framework
AI Governance
Comparative Analysis
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