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.
| Original language | English (US) |
|---|---|
| Journal | Trends in Cognitive Sciences |
| DOIs | |
| State | Accepted/In press - 2026 |
All Science Journal Classification (ASJC) codes
- Neuropsychology and Physiological Psychology
- Experimental and Cognitive Psychology
- Cognitive Neuroscience
Keywords
- AI evaluation
- Understanding
- benchmarking
- interpretability
- world models
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