EconPapers    
Economics at your fingertips  
 

Relational Expertise: What Machines Can’t Know

Pauli Pakarinen and Ruthanne Huising ()
Additional contact information
Ruthanne Huising: EM - EMLyon Business School

Post-Print from HAL

Abstract: "Professions continue to be the primary means through which societies institutionalize expertise. Recent analyses and narratives predict that artificial intelligence (AI) will make meaningful inroads into non-routine reasoning about complex cases, threatening the authority of professions. These predictions, we argue, draw on substantialist understandings of expertise as an intellectual possession, a mental achievement, or a cognitive state performed – by humans or machines – to achieve effects. A synthesis of empirical studies shows that expertise is more accurately conceptualized as relationally constituted – generated, applied, and recognized – through interactions. Relational expertise creates challenges of opacity, translation, and accountability for the development and deployment of AI technologies in the context of professional work. A relational understanding of expertise disrupts notions that professions may be augmented with, subordinated to, or dismantled by AI technologies. Instead, AI technologies are embedded in the network of interactions through which the relational expertise of professions is constituted."

Keywords: artificial intelligence; expertise; professions; relationality; technology; work (search for similar items in EconPapers)
Date: 2025-07-01
References: Add references at CitEc
Citations:

Published in Journal of Management Studies, 2025, 62 (5), 2053-2082 p. ⟨10.1111/joms.12915⟩

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

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-05100468

DOI: 10.1111/joms.12915

Access Statistics for this paper

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

 
Page updated 2025-06-10
Handle: RePEc:hal:journl:hal-05100468