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Machine understanding
Huili Chen1, Stephen R Grimm2, Olga Russakovsky3
1Program in Cognitive Science, Princeton University, Princeton, NJ, USA; Faculty of Information, University of Toronto, Toronto, ON, Canada.
Trends in Cognitive Sciences
|May 23, 2026
Summary
This study introduces a framework to precisely assess artificial intelligence (AI) understanding. It offers conceptual tools for evaluating machine comprehension in AI systems.
Area of Science:
- Philosophy of AI
- Cognitive Science
- Artificial Intelligence
Background:
- Assessing artificial intelligence (AI) systems' "understanding" is crucial for evaluating intelligence.
- This evaluation is vital for the safe and responsible deployment of AI technologies.
- Current AI practices lack precise frameworks for discussing machine understanding.
Purpose of the Study:
- To develop a conceptual framework for precisely discussing and evaluating machine understanding in AI.
- To provide tools for researchers and developers to ask more specific questions about AI comprehension.
- To offer a structured approach for making more precise claims regarding AI understanding.
Main Methods:
- Drawing on scholarship from philosophy and cognitive science.
- Analyzing current practices within the field of artificial intelligence.
- Conceptualizing understanding as a relation between a system (S) and a target of understanding (T).
- Discussing methods to specify the relation, the system, and the target of understanding.
Main Results:
- A framework for conceptualizing machine understanding as a relational construct (S-T).
- Identification of options for specifying the components of the understanding relation.
- Development of precise questions and claims regarding AI comprehension.
Conclusions:
- The proposed framework offers conceptual tools, not a definitive theory, for machine understanding.
- Enhances the ability to assess and advance AI comprehension.
- Facilitates more rigorous evaluation practices for AI systems.
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