California Management Review· 2019Q1
Artificial Intelligence in Human Resources Management: Challenges and a Path Forward
- 1,358citations
- Q1SCImago
- 2019year
Short summary
A framework is proposed to address four key challenges hindering AI adoption in HR: complexity of HR phenomena, limited data, ethical/legal constraints, and adverse employee reactions.
AI-generated from the title and abstract; the full text is not read.
Key points
- Four major challenges impede AI in HR: complexity, small data, ethics/legality, and employee reactions.
- The proposed solutions are grounded in causal reasoning, randomization/experiments, and employee contribution.
- These principles aim for economically efficient and socially appropriate data-driven HR management.
AI-generated from the title and abstract; the full text is not read.
Abstract
There is a substantial gap between the promise and reality of artificial intelligence in human resource (HR) management. This article identifies four challenges in using data science techniques for HR tasks: complexity of HR phenomena, constraints imposed by small data sets, accountability questions associated with fairness and other ethical and legal constraints, and possible adverse employee reactions to management decisions via data-based algorithms. It then proposes practical responses to these challenges based on three overlapping principles—causal reasoning, randomization and experiments, and employee contribution—that would be both economically efficient and socially appropriate for using data science in the management of employees.
The authors' abstract, as published at the source. California Management Review, 2019 · DOI ↗
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