Towards Measurement Theory for Artificial Intelligence

TL;DR

This paper advocates for developing a formal measurement theory for AI to enable better comparison, evaluation, and understanding of AI capabilities through standardized and quantifiable metrics.

Contribution

It proposes a layered measurement framework and distinguishes observables to create a unified, calibratable taxonomy of AI phenomena.

Findings

Outlines a layered measurement stack for AI evaluation

Distinguishes direct and indirect observables in AI measurement

Suggests integrating AI evaluation with safety science techniques

Abstract

We motivate and outline a programme for a formal theory of measurement of artificial intelligence. We argue that formalising measurement for AI will allow researchers, practitioners, and regulators to: (i) make comparisons between systems and the evaluation methods applied to them; (ii) connect frontier AI evaluations with established quantitative risk analysis techniques drawn from engineering and safety science; and (iii) foreground how what counts as AI capability is contingent upon the measurement operations and scales we elect to use. We sketch a layered measurement stack, distinguish direct from indirect observables, and signpost how these ingredients provide a pathway toward a unified, calibratable taxonomy of AI phenomena.