Book chapter
Introducing and Maintaining Machine Learning Selection Systems
Case Studies in I-O Psychology, pp.412-427
The SIOP Organizational Frontiers Series, Oxford University Press
2025
DOI: 10.1093/9780197692288.003.0023
Abstract
Machine learning (ML) as a method of artificial intelligence may be the biggest innovation in personnel selection since the invention of employment tests, yet organizations may be hesitant to adopt ML due to lack of understanding and distrust. In this chapter, we describe one of the earliest (and ongoing) applications of ML published in industrial and organizational psychology journals (Campion et al., 2016) and its subsequent 10 years of use. We explain how the potential for innovation was recognized, introduced to management, demonstrated to be useful, defended to critics, updated, and maintained and applied for more than 10 years across 30 hiring waves, processing 77,000 candidates to make 3,000 hires in a single organization. We describe throughout this case study what we did and the lessons learned, which we hope will be useful to others considering similar innovations in industrial and organizational practice.
Details
- Title: Subtitle
- Introducing and Maintaining Machine Learning Selection Systems
- Creators
- Emily D. CampionMichael A. Campion
- Contributors
- Rick Jacobs (Editor) - Pennsylvania State UniversityDouglas H. Reynolds (Editor) - Development Dimensions International (DDI), 1225 Washington Pike Bridgeville, PA 15017, United States of America (the)
- Resource Type
- Book chapter
- Publication Details
- Case Studies in I-O Psychology, pp.412-427
- Series
- The SIOP Organizational Frontiers Series
- DOI
- 10.1093/9780197692288.003.0023
- Publisher
- Oxford University Press; New York, NY
- Language
- English
- Date published
- 2025
- Academic Unit
- Management and Entrepreneurship
- Record Identifier
- 9984962538002771
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