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AI & SOCIETY

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24-06-2022 | OPEN FORUM

Framing the effects of machine learning on science

Authors: Victo J. Silva, Maria Beatriz M. Bonacelli, Carlos A. Pacheco

Published in: AI & SOCIETY | Issue 2/2024

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Abstract

Studies investigating the relationship between artificial intelligence (AI) and science tend to adopt a partial view. There is no broad and holistic view that synthesizes the channels through which this interaction occurs. Our goal is to systematically map the influence of the latest AI techniques (machine learning, ML and its sub-category, deep learning, DL) on science. We draw on the work of Nathan Rosenberg to develop a taxonomy of the effects of technology on science. The proposed framework comprises four categories of technology effects on science: intellectual, economic, experimental and instrumental. The application of the framework in the relationship between ML/DL and science allowed the identification of multiple triggers activated by the new techniques in the scientific field. Visualizing these different channels of influence allows us to identify two pressing, emerging issues. The first is the concentration of experimental effects in a few companies, which indicates a reinforcement effect between more data on the phenomenon (experimental effects) and more capacity to commercialize the technique (economic effects). The second is the diffusion of new techniques lacking in explanation (intellectual effect) throughout the fabric of science (instrumental effects). The value of this article is twofold. First, it provides a simple framework to assess the relations between technology and science. Second, it provides this broad and holistic view of the influence of new AI techniques on science. More specifically, the article details the channels through which this relationship occurs, the nature of these channels and the loci in which the potential effects on science unfolds.

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Even though we agree with Flexner (1939) that curiosity is as much an essential driver of scientific inquiry as to the prospect of use.

Price probably wanted to allude to a technological innovation that escapes the dominant technological paradigm when he mentions unexpected technological innovation. He could not mobilize this concept because Giovanni Dosi (1984) would still spread it in the years to come.

“Base of observations” and “instrumentation” refer to the initial trigger that emerges in the technological sphere and that will influence science: new data from the body of empirical knowledge or new technological artifacts for instrumentation; on the other hand, “influencing the agenda” refers to the result, already in the scientific sphere, of changes that have taken place in the technological sphere. There is some confusion between cause and effect.

Also referred as an 'economy of research tools.' (Cockburn et al., 2018).

"Opening up the set of problems that can be feasibly addressed, and radically altering scientific and technical communities' conceptual approaches and framing of problems." (Cockburn et al., 2018).

According to Brooks (1994), “the more radical the invention, the more likely it is to stimulate wholly new areas of basic research or to rejuvenate older areas of research that were losing the interest of the most innovative scientists.”.

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(TITLE-ABS-KEY("xAI" OR "XAI" OR "explainable artificial intelligence" OR "explainable AI" OR "explainable machine learning" OR "explainable deep learning" OR "explainable algorithms" OR "interpretable artificial intelligence" OR "interpretable AI" OR "interpretable machine learning" OR "interpretable deep learning" OR "interpretable algorithms" OR "opaque artificial intelligence" OR "opaque AI" OR "opaque machine learning" OR "opaque deep learning" OR "opaque algorithms" OR "responsible artificial intelligence" OR "responsible AI" OR "responsible machine learning" OR "responsible deep learning" OR "responsible algorithms" OR "transparent artificial intelligence" OR "transparent AI" OR "transparent machine learning" OR "transparent deep learning" OR "transparent algorithms") AND ( LIMIT-TO ( DOCTYPE,"cp") OR LIMIT-TO ( DOCTYPE,"ar") OR LIMIT-TO ( DOCTYPE,"re"))).

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We understand AI science as Gazis (1979, p. 252) understands computer science/software science: “the search for knowledge, or the development of a methodology, that goes beyond satisfying the needs of a single application, but forms the basis for new applications."

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The development of NetWare (hardware for neural networks) was high on the agenda in the 1980s (van Raan & Tijssen, 1993). However, as already mentioned, all things related to neural networks were marginalized and would only return to the spotlight after AlexNet’s breakthrough in 2012.

Intel researchers have released Loihi, the company's fifth generation of chips inspired by neuromorphic technologies (Davies et al., 2018).

See Google’s Cloud TPU: https://cloud.google.com/tpu?hl=pt-br

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Title: Framing the effects of machine learning on science
Authors: Victo J. Silva
Maria Beatriz M. Bonacelli
Carlos A. Pacheco
Publication date: 24-06-2022
Publisher: Springer London
Published in: AI & SOCIETY / Issue 2/2024
Print ISSN: 0951-5666
Electronic ISSN: 1435-5655
DOI: https://doi.org/10.1007/s00146-022-01515-x

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