A Conceptual Framework for AI Capability Evaluations
This provides a structured tool for researchers, practitioners, and policymakers to improve AI governance, but it is incremental as it builds on existing evaluation practices without introducing new taxonomies.
The paper tackles the lack of clarity in AI capability evaluations by proposing a conceptual framework that systematizes analysis of methods and terminology, aiming to support transparency, comparability, and interpretability across diverse evaluations.
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.