Quand l’intelligence artificielle théorisera les organisations

Philippe Baumard ()
Additional contact information
Philippe Baumard: ESD R3C - Équipe Sécurité & Défense - Renseignement, Criminologie, Crises, Cybermenaces - CNAM - Conservatoire National des Arts et Métiers [CNAM]

Post-Print from HAL

Abstract: This article explores the feasibility of machines inventing and theorizing organizations. Most machine learning models are automated statistical processes that barely achieve a formal induction. In that sense, most current learning models do not generate new theories, but, instead, recognize a pre-existing order of symbols, signs or data. Most human theories are embodied and incarnated: they spawn from an organic connection to the world, which theorists can hardly escape. This article is organized in three parts. First, we study the history of artificial intelligence, from its foundation in the 19th century to its recent evolution, to understand what an artificial intelligence would be able to do in terms of theorization... Which leads us, in a second step, to question the act of scientific production in order to identify what can be considered a human act, and what can be the subject of modelling and autonomous learning led by a machine. The objective here is to assess the feasibility of substituting man with machine to produce research. In a third and final part, we propose four modes of theoretical exploration that are already the work of machines, or that could see, in the future, a complete substitution of man by machine. We conclude this article by sharing several questions about the future of research in organizational theory, and its utility, human or machine, for organizations and society.

Keywords: AI; artificial intelligence; organization theory; sociology of knowledge; cognitive theory (search for similar items in EconPapers)
Date: 2019-11
New Economics Papers: this item is included in nep-big and nep-cmp
Note: View the original document on HAL open archive server: https://hal.science/hal-03218196
References: Add references at CitEc
Citations:

Published in Revue Française de Gestion, 2019, 45 (285), pp.135-159. ⟨10.3166/rfg.2020.00409⟩

Downloads: (external link)
https://hal.science/hal-03218196/document (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-03218196

DOI: 10.3166/rfg.2020.00409

Access Statistics for this paper

More papers in Post-Print from HAL
Bibliographic data for series maintained by CCSD ().