AI for Science paper calls for treating data pipelines as inference components

A new paper argues that AI for Science (AI4Science) workflows should treat measurement-to-dataset pipelines as inference components, rather than fixed interfaces. The authors identify three failure modes: a hidden hypothesis space, uncertified transportability, and ungoverned multiplicity. An empirical audit in neuroscience found a very low survival rate under a cross-dataset stability criterion, suggesting a need for computable observation frameworks to quantify pipeline adequacy and stability. AI

IMPACT Promotes more rigorous and reproducible AI for Science research by addressing uncertainty in data pipelines.

RANK_REASON The cluster contains an academic paper published on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]

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• AI for Science
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