The Recursive Theory of Knowledge Augmentation: Integrating human intuition and knowledge in Artificial Intelligence to augment organizational knowledge

Antoine Harfouche (), Bernard Quinio, Mario Saba and Peter Bou Saba
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Antoine Harfouche: Paris Nanterre University
Bernard Quinio: Paris Nanterre University
Mario Saba: Business School Lausanne
Peter Bou Saba: Léonard de Vinci Pôle Universitaire

Information Systems Frontiers, 2023, vol. 25, issue 1, No 4, 55-70

Abstract: Abstract Artificial intelligence (AI) has increased the ability of organizations to accumulate tacit and explicit knowledge to inform management decision-making. Despite the hype and popularity of AI, there is a noticeable scarcity of research focusing on AI's potential role in enriching and augmenting organizational knowledge. This paper develops a recursive theory of knowledge augmentation in organizations (the KAM model) based on a synthesis of extant literature and a four-year revised canonical action research project. The project aimed to design and implement a human-centric AI (called Project) to solve the lack of integration of tacit and explicit knowledge in a scientific research center (SRC). To explore the patterns of knowledge augmentation in organizations, this study extends Nonaka's SECI (socialization, externalization, combination, and internalization) model by incorporating the human-in-the-loop Informed Artificial Intelligence (IAI) approach. The proposed design offers the possibility to integrate experts' intuition and domain knowledge in AI in an explainable way. The findings show that organizational knowledge can be augmented through a recursive process enabled by the design and implementation of human-in-the-loop IAI. The study has important implications for research and practice.

Keywords: Informed Artificial Intelligence; Human-Centric AI; Human-in-the-loop AI; Explainable AI; Dynamic Theory of Organizational Knowledge Creation; Canonical action research; Knowledge augmentation (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s10796-022-10352-8

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