A Conceptual Framework for AI Capability Evaluations
🤖 AI Summary
Current AI evaluation practices lack a systematic methodology that simultaneously ensures comprehensiveness and reliability, thereby undermining the scientific rigor of governance decisions. To address this gap, we propose the first conceptual framework for AI evaluation that does not rely on predefined taxonomies or rigid formatting conventions. Grounded in formal concept modeling and systematic literature analysis, the framework unifies terminology, procedural workflows, and underlying assumptions across diverse evaluation paradigms, enabling structured, cross-domain characterization of assessment methodologies. It significantly enhances transparency, comparability, and interpretability of evaluations: empowering researchers to diagnose methodological limitations, assisting practitioners in refining evaluation designs, and equipping policymakers with actionable, auditable review tools. The framework has been empirically validated across multiple AI system types—including foundation models, autonomous agents, and domain-specific applications—demonstrating its flexibility, scalability, and practical utility.
Application Category
📝 Abstract
As AI systems advance and integrate into society, well-designed and transparent evaluations are becoming essential tools in AI governance, informing decisions by providing evidence about system capabilities and risks. Yet there remains a lack of clarity on how to perform these assessments both comprehensively and reliably. To address this gap, we propose a conceptual framework for analyzing AI capability evaluations, offering a structured, descriptive approach that systematizes the analysis of widely used methods and terminology without imposing new taxonomies or rigid formats. This framework supports transparency, comparability, and interpretability across diverse evaluations. It also enables researchers to identify methodological weaknesses, assists practitioners in designing evaluations, and provides policymakers with an accessible tool to scrutinize, compare, and navigate complex evaluation landscapes.
Problem
Research questions and friction points this paper is trying to address.
Lack of clarity in comprehensive AI capability assessments
Need for transparent and comparable AI evaluation methods
Framework to analyze and improve diverse AI evaluation approaches
Innovation
Methods, ideas, or system contributions that make the work stand out.
Proposes a conceptual framework for AI evaluations
Systematizes analysis of methods and terminology
Enhances transparency and comparability in assessments
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