Ilievski, Filip;
Hammer, Barbara;
van Harmelen, Frank;
Paassen, Benjamin;
Saralajew, Sascha;
Schmid, Ute;
Biehl, Michael;
... Villmann, Thomas; + view all
(2025)
Aligning generalization between humans and machines.
Nature Machine Intelligence
, 7
(9)
pp. 1378-1389.
10.1038/s42256-025-01109-4.
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Abstract
Recent advances in artificial intelligence (AI)—including generative approaches—have resulted in technology that can support humans in scientific discovery and forming decisions, but may also disrupt democracies and target individuals. The responsible use of AI and its participation in human–AI teams increasingly shows the need for AI alignment, that is, to make AI systems act according to our preferences. A crucial yet often overlooked aspect of these interactions is the different ways in which humans and machines generalize. In cognitive science, human generalization commonly involves abstraction and concept learning. By contrast, AI generalization encompasses out-of-domain generalization in machine learning, rule-based reasoning in symbolic AI, and abstraction in neurosymbolic AI. Here we combine insights from AI and cognitive science to identify key commonalities and differences across three dimensions: notions of, methods for, and evaluation of generalization. We map the different conceptualizations of generalization in AI and cognitive science along these three dimensions and consider their role for alignment in human–AI teaming. This results in interdisciplinary challenges across AI and cognitive science that must be tackled to support effective and cognitively supported alignment in human–AI teaming scenarios.
| Type: | Article |
|---|---|
| Title: | Aligning generalization between humans and machines |
| DOI: | 10.1038/s42256-025-01109-4 |
| Publisher version: | https://doi.org/10.1038/s42256-025-01109-4 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | AI, CLASSIFICATION, Computer Science, Computer Science, Artificial Intelligence, Computer Science, Interdisciplinary Applications, DEEP, IDENTIFICATION, MODELS, NETWORKS, Science & Technology, Technology |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10216015 |
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