Ethical Dilemmas of Generative AI and Large Language Models in Human Resource Management

Brian Mabuyana (), David Mhlanga (), Mufaro Dzingirai () and Emmanuel Ndhlovu ()
Additional contact information
Brian Mabuyana: University of South Africa, Department of Marketing and Retail Management
David Mhlanga: Monash University
Mufaro Dzingirai: Namibia University of Science and Technology, Harold Pupkewitz Graduate School of Business
Emmanuel Ndhlovu: University of Johannesburg, School of Public Management, Governance and Public Policy, College of Business and Economics

A chapter in Strategic Human Resource Analytics and Intelligent Data-Driven Systems, 2026, pp 203-218 from Springer

Abstract: Abstract The rapid technological advancements witnessed in the early twenty-first century are reforming human resource management at a faster pace. However, there is scant evidence pertaining to the influence of generative Artificial Intelligence (AI) and Large Language Models (LLMs) on human resource management. This chapter seeks to bridge this gap. This chapter aims to examine how generative AI and LLMs influence talent management in a data-oriented context. A desktop approach and a conceptual approach were used as the data collection technique. The results revealed that making use of generative AI and LLMs alters the human resource management within a data-oriented setting. On the other side, the results showed that risks and ethical dilemmas affect the use of generative AI and LLMs within the data-oriented workplace. These results are useful for human resource practitioners, policymakers, and scholars.

Keywords: Generative Artificial Intelligence; People Analytics; Human Resource Management; Fourth Industrial Revolution; Talent Management; Large Language Models (search for similar items in EconPapers)
Date: 2026
References: Add references at CitEc
Citations:

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:spr:sprchp:978-981-92-2623-8_10

Ordering information: This item can be ordered from
http://www.springer.com/9789819226238

DOI: 10.1007/978-981-92-2623-8_10

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().