Aligning generalization between humans and machines
Journal
Nature Machine Intelligence
Type
journal article
Date Issued
2025-09-15
Author(s)
Filip Ilievski
;
Barbara Hammer
;
Frank van Harmelen
;
Benjamin Paaßen
;
Sascha Saralajew
;
Ute Schmid
;
Michael L. Biehl
;
Marianna Bolognesi
;
Xin Dong
;
Kiril Gashteovski
;
Pascal Hitzler
;
Giuseppe Marra
;
Pasquale Minervini
;
Martin Mundt
;
Axel-Cyrille Ngonga Ngomo
;
Alessandro Oltramari
;
Gabriella Pasi
;
Zeynep G. Saribatur
;
Luciano Serafini
;
John S. Shawe-Taylor
;
Vered Shwartz
;
Gabriella Skitalinskaya
;
;
Gido M. van de Ven
;
Thomas Villmann
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.
Language
English
HSG Classification
contribution to scientific community
Refereed
Yes
Volume
7
Number
9
Start page
1378
End page
1389
Subject(s)
Division(s)