The science and practice of proportionality in AI risk evaluations

Science 2026 Open access

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Authors: Carlos Mougán, Lauritz Morlock, Jair Aguirre, James R. M. Black, Jan Brauner, Siméon Campos, Sunishchal Dev, David Fernández Llorca, Alberto Franzin, Mario Fritz, Emilia Gómez, Friederike Grosse-Holz
+10 more Eloise Hamilton, Max Hasin, José Hernández-Orallo, Dan Lahav, Luca Massarelli, Vasilios Mavroudis, Malcolm Murray, Patricia Paskov, Jaime Raldua, Wout Schellaert
DOI: 10.1126/science.aea3835
Cited by: 2
References: 2

Abstract

AI evaluations should provide meaningful risk information without imposing excessive burden.

Fields of Study & Subjects

Artificial Intelligence in Healthcare and Education Explainable Artificial Intelligence (XAI) Ethics and Social Impacts of AI

Publication details

Journal / source
Science
Publisher
American Association for the Advancement of Science (AAAS)
Volume
391
Issue
6787
Pages
769–771
Publication date
19 February 2026
Language
EN