Related papers: What Understanding Means in AI-Laden Astronomy

What Understanding Means in AI-Laden Astronomy

URL: http://arxiv.org/abs/2601.10038v1
Date: Thu, 15 Jan 2026 03:28:38 GMT
Title: What Understanding Means in AI-Laden Astronomy
Authors: Yuan-Sen Ting, André Curtis-Trudel, Siyu Yao,
Abstract summary: Artificial intelligence is rapidly transforming astronomical research.<n>This article argues that philosophy of science offers essential tools for navigating AI's integration into astronomy.<n>We propose "pragmatic understanding" as a framework for integration--recognizing AI as a tool that extends human cognition.
Score: 0.20336617819227906
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Artificial intelligence is rapidly transforming astronomical research, yet the scientific community has largely treated this transformation as an engineering challenge rather than an epistemological one. This perspective article argues that philosophy of science offers essential tools for navigating AI's integration into astronomy--conceptual clarity about what "understanding" means, critical examination of assumptions about data and discovery, and frameworks for evaluating AI's roles across different research contexts. Drawing on an interdisciplinary workshop convening astronomers, philosophers, and computer scientists, we identify several tensions. First, the narrative that AI will "derive fundamental physics" from data misconstrues contemporary astronomy as equation-derivation rather than the observation-driven enterprise it is. Second, scientific understanding involves more than prediction--it requires narrative construction, contextual judgment, and communicative achievement that current AI architectures struggle to provide. Third, because narrative and judgment matter, human peer review remains essential--yet AI-generated content flooding the literature threatens our capacity to identify genuine insight. Fourth, while AI excels at well-defined problem-solving, the ill-defined problem-finding that drives breakthroughs appears to require capacities beyond pattern recognition. Fifth, as AI accelerates what is feasible, pursuitworthiness criteria risk shifting toward what AI makes easy rather than what is genuinely important. We propose "pragmatic understanding" as a framework for integration--recognizing AI as a tool that extends human cognition while requiring new norms for validation and epistemic evaluation. Engaging with these questions now may help the community shape the transformation rather than merely react to it.

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