Work

Machine understanding

Year: 2026

Type: review

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.

Source: Trends in Cognitive Sciences

DOI: 10.1016/j.tics.2026.04.003

Authors: Huili Chen, Stephen R. Grimm, Olga Russakovsky, Tania Lombrozo

Institutions: University of Toronto, Princeton University, Fordham University

Language: English

Topic: Philosophy and Theoretical Science

Subfield: Experimental and Cognitive Psychology

Field: Psychology

Domain: Social Sciences

Open Access status: hybrid

Estimated APC paid: $7,030

Funders: National Science Foundation, Princeton University