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Artificial intelligence, machine learning, and big data: Improvements to the science of people at work and applications to practice.

Authors :
Woo, Sang Eun
Tay, Louis
Oswald, Frederick
Source :
Personnel Psychology; Dec2024, Vol. 77 Issue 4, p1387-1402, 16p
Publication Year :
2024

Abstract

Currently, in the organizational research community, artificial intelligence (AI), machine learning (ML), and big data techniques are being vigorously explored as a set of modern‐day approaches contributing to a multidisciplinary science of people at work. This paper discusses more specifically how these sophisticated technologies, methods, and data might together advance the science of people at work through various routes, including improving theory and knowledge, construct measurements, and predicting real‐world outcomes. Inspired by the four articles in the current special issue highlighting several of these aspects in essential ways, we also share other possibilities for future organizational research. In addition, we indicate many key practical, ethical, and institutional challenges with research involving AI/ML and big data (i.e., data accessibility, methodological skill gaps, data transparency, privacy, reproducibility, generalizability, and interpretability). Taken together, the opportunities and challenges that lie ahead in the areas of AI and ML promise to reshape organizational research and practice in many exciting and impactful ways. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
00315826
Volume :
77
Issue :
4
Database :
Complementary Index
Journal :
Personnel Psychology
Publication Type :
Academic Journal
Accession number :
180925957
Full Text :
https://doi.org/10.1111/peps.12643