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Oil & Water? Diffusion of AI Within and Across Scientific Fields (2405.15828v1)

Published 24 May 2024 in cs.DL and cs.AI

Abstract: This study empirically investigates claims of the increasing ubiquity of 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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Authors (5)
  1. Eamon Duede (16 papers)
  2. William Dolan (1 paper)
  3. André Bauer (11 papers)
  4. Ian Foster (138 papers)
  5. Karim Lakhani (3 papers)
Citations (2)
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Summary

Diffusion of AI Within and Across Scientific Fields: Evaluating Ubiquity and Semantic Tension

The paper "Oil content Water? Diffusion of AI Within and Across Scientific Fields" presents an empirical investigation into the increasing ubiquity of AI across a diverse array of scientific fields over the period from 1985 to 2022. Using an extensive dataset comprising approximately 80 million research publications across 20 distinct disciplines, the paper scrutinizes the exponential growth in AI engagement, providing a nuanced understanding of AI's integration into the scientific landscape.

The authors approach the assessment of AI's ubiquity through three primary research questions. The first research question examines the temporal evolution of AI engagement percentage in scholarly research. Here, the findings reveal a 1293% increase in AI-engaged papers between 1985 and 2022, with nearly 9% of all papers being AI-engaged by the latter year. Such growth underscores AI's transition from a niche specialization to a pervasive element in research across multiple domains.

Addressing the second research question, the paper evaluates the extent of AI diffusion within individual fields by analyzing the distribution of AI-engaged publications across various publication venues. Utilizing the Gini coefficient to measure this distribution, the authors determine an increase in 'Ubiquity,' configuring it as the inverse of the Gini coefficient to more intuitively reflect diffusion. The results indicate that AI engagement is spreading across publication venues rather than being concentrated, indicating the substantive broadening of AI influence within disciplines.

The third research question explores changes in the semantic nature of AI-engaged and non-AI-engaged research as AI becomes more ubiquitous. Through document embeddings examined using SPECTER2 models, the paper assesses how AI engagement influences the themes and topics within fields. Notably, it finds that as AI ubiquity increases, AI-engaged research within fields becomes more semantically aligned with AI research in Computer Science, yet simultaneously diverges from non-AI-engaged work within the same field. This finding suggests a semantic tension between traditional research and emergent AI paradigms, implying that while AI knowledge is infiltrating various disciplines, it is not harmonizing effortlessly with existing scholarly work.

The implications of these findings are multifaceted. Practically, this paper highlights the widespread penetration of AI as an interdisciplinary tool, suggesting that stakeholders in academia and industry should foster integrative frameworks that accommodate such technological diffusion. Theoretically, it prompts reconsideration of established models of technological diffusion, as the observed 'oil and water' dynamic points towards a nuanced interplay between new and traditional research paradigms.

Future developments in AI research and application could entail further diversification of AI methodologies tailored to distinct disciplinary needs. There is potential for AI to catalyze interdisciplinary collaborations, driving innovation at the intersections of fields. As AI continues to reshape the landscapes it touches, careful attention must be paid to fostering integrative approaches that balance novel AI methodologies with the well-established practices of traditional fields.

In summary, the paper provides a detailed empirical account of AI's pervasive growth within scientific domains, underscored by rising engagement and ubiquity. The paper articulates the challenges inherent to this growth, particularly the integration of AI into diverse domains while managing semantic tensions with non-AI-engaged research—a task that stands as a fertile ground for further inquiry and strategy in the field of AI's academic and practical applications.

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