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Oil & Water? Diffusion of AI Within and Across Scientific Fields

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arxiv 2405.15828 v1 pith:TQHGGDCO submitted 2024-05-24 cs.DL cs.AI

classification cs.DLcs.AI
keywords fieldsacrossai-engagedresearchengagementincreasingpublicationsubiquity
verification ladder T0 review T1 audit T2 compute T3 formal
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This study empirically investigates claims of the increasing ubiquity of artificial intelligence (AI) within roughly 80 million research publications across 20 diverse scientific fields, by examining the change in scholarly engagement with AI from 1985 through 2022. We observe exponential growth, with AI-engaged publications increasing approximately thirteenfold (13x) across all fields, suggesting a dramatic shift from niche to mainstream. Moreover, we provide the first empirical examination of the distribution of AI-engaged publications across publication venues within individual fields, with results that reveal a broadening of AI engagement within disciplines. While this broadening engagement suggests a move toward greater disciplinary integration in every field, increased ubiquity is associated with a semantic tension between AI-engaged research and more traditional disciplinary research. Through an analysis of tens of millions of document embeddings, we observe a complex interplay between AI-engaged and non-AI-engaged research within and across fields, suggesting that increasing ubiquity is something of an oil-and-water phenomenon -- AI-engaged work is spreading out over fields, but not mixing well with non-AI-engaged work.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. From inference to prediction: how machine learning is reconfiguring science

    cs.CY 2026-06 unverdicted novelty 6.0 of 10

    Predictive ML architectures are displacing inference-oriented techniques in interpretability-first fields, first via deep learning (2015–2021) and then via external commercial models (post-2022).

  2. Bridging AI and Science: Implications from a Large-Scale Literature Analysis of AI4Science

    cs.AI 2024-11 conditional novelty 6.0 of 10

    Using LLM-extracted labels from 162,656 papers, this analysis shows that AI4Science connections are sparse and uneven, with a few hub areas dominating while many scientific problems and AI methods remain weakly linked.

  3. Toward Effective AI Governance: A Review of Principles

    cs.SE 2025-05 reject novelty 4.0 of 10

    A rapid tertiary review of nine AI governance reviews finds a focus on high-level frameworks and principles, with little concrete guidance on governance mechanisms.

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