Machine understanding.
Trends Cogn Sci 2026 May 23. [Online ahead of print]

Abstract

What do artificial intelligence (AI) systems "understand"? This question arises not only in assessing a system's intelligence but also in evaluation practices to ensure the safe and responsible deployment of AI. Drawing on scholarship from philosophy and cognitive science, and informed by current practices in AI, we develop a framework for asking more precise questions and making more precise claims about machine understanding. We conceptualize understanding as a relation between a system (S) and a target of understanding (T), and we discuss how to specify the relation, the system, and the target, offering a landscape of options in each case. Our goal is not to defend a particular account of understanding, but to provide conceptual tools for those working to assess or advance machine understanding.

Authors+Show Affiliations

Chen HProgram in Cognitive Science, Princeton University, Princeton, NJ, USA; Faculty of Information, University of Toronto, Toronto, ON, Canada. Electronic address: huili.chen@utoronto.ca.
Grimm SRDepartment of Philosophy, Fordham University, New York, NY, USA.
Russakovsky ODepartment of Computer Science, Princeton University, Princeton, NJ, USA.
Lombrozo TDepartment of Psychology, Princeton University, Princeton, NJ, USA. Electronic address: lombrozo@princeton.edu.

Pub Type(s)

Journal Article
Review

Language

eng

PubMed ID

42177102