A Conceptual Framework for AI Capability Evaluations

TL;DR

This paper introduces a structured conceptual framework to improve the transparency, comparability, and reliability of AI capability evaluations, aiding researchers, practitioners, and policymakers in assessing AI systems effectively.

Contribution

It presents a novel descriptive framework that systematizes AI evaluation methods without imposing rigid classifications, enhancing clarity and utility across diverse evaluation practices.

Findings

Framework supports transparency and comparability.

Helps identify methodological weaknesses.

Aids in designing and scrutinizing evaluations.

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.