@article{204646,
  keywords = {Understanding, world models, benchmarking, AI evaluation, interpretability},
  author = {Huili Chen and Stephen Grimm and Olga Russakovsky and Tania Lombrozo},
  title = {Machine understanding},
  abstract = {<p>What do artificial intelligence (AI) systems {\textquotedblleft}understand{\textquotedblright}? This question arises not only in assessing a system{\textquoteright}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.</p>},
  year = {2026},
  journal = {Trends in Cognitive Sciences},
  issn = {1364-6613},
  url = {https://doi.org/10.1016/j.tics.2026.04.003},
}