Position: AI for Science Should Treat Measurement-to-Dataset Pipelines as Inference Components
arXiv:2605.24558v1 Announce Type: new Abstract: AI for Science (AI4Science) workflows often treat the released dataset as a fixed interface to the underlying system. However, in domains relying on \emph{indirect observation}, the learner observes a derivative representation produced by multi-stage measurement, reconstruction, and preprocessing pipelines. \textbf{We argue that these measurement-to-dataset pipelines are inference components: treating their outputs as ``given data'' freezes an observation model and obscures uncertainty over feasible pipeline choices.} We identify three failure mo
This paper highlights an evolving understanding of AI's integration into scientific discovery, moving beyond simplistic data input to treating the entire measurement pipeline as part of the inference process.
A sophisticated reader should care because this reframes how AI for Science (AI4Science) should be designed, emphasizing uncertainty propagation and robust model development, which can lead to more reliable scientific discoveries and AI applications.
AI for Science methodologies will shift from treating datasets as fixed inputs to incorporating data generation and preprocessing pipelines as integral inference components, requiring more complex and nuanced AI models.
- · AI/ML researchers specializing in uncertainty quantification
- · Scientific domains reliant on indirect observation (e.g., astrophysics, medical
- · Developers of robust AI4Science platforms
- · Developers of 'black box' AI models without pipeline transparency
- · Scientific workflows with poorly characterized measurement processes
More robust and less biased scientific discoveries based on AI interpretation of data.
Increased demand for explainable AI and uncertainty-aware machine learning in scientific applications.
New standards and best practices emerging for data generation and preprocessing within AI-driven scientific research, potentially influencing reproducibility and funding priorities.
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Read at arXiv cs.LG