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A multi-stakeholder ethical framework for AI-augmented HRM

Author

Listed:
  • Verma Prikshat
  • Parth Patel
  • Arup Varma
  • Alessio Ishizaka

Abstract

Purpose - This narrative review presents a multi-stakeholder ethical framework for AI-augmented HRM, based on extant research in the domains of ethical HRM and ethical AI. More specifically, the authors identify critical ethical issues pertaining to AI-augmented HRM functions and suggest ethical principles to address these issues by identifying the relevant stakeholders based on the responsibility ethics approach. Design/methodology/approach - This paper follows a narrative review approach by first identifying various ethical/codes/issues/dilemmas discussed in HRM and AI. The authors next discuss ethical issues concerning AI-augmented HRM, drawing from recent literature. Finally, the authors propose ethical principles for AI-augmented HRM and stakeholders responsible for managing those issues. Findings - The paper summarises key findings of extant research in the ethical HRM and AI domain and provides a multi-stakeholder ethical framework for AI-augmented HRM functions. Originality/value - This research's value lies in conceptualising a multi-stakeholder ethical framework for AI-augmented HRM functions comprising 11 ethical principles. The research also identifies the class of stakeholders responsible for identified ethical principles. The research also presents future research directions based on the proposed model.

Suggested Citation

  • Verma Prikshat & Parth Patel & Arup Varma & Alessio Ishizaka, 2022. "A multi-stakeholder ethical framework for AI-augmented HRM," International Journal of Manpower, Emerald Group Publishing Limited, vol. 43(1), pages 226-250, January.
    • Handle: RePEc:eme:ijmpps:ijm-03-2021-0118
    DOI: 10.1108/IJM-03-2021-0118
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    Citations

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    Cited by:

    1. Prikshat, Verma & Islam, Mohammad & Patel, Parth & Malik, Ashish & Budhwar, Pawan & Gupta, Suraksha, 2023. "AI-Augmented HRM: Literature review and a proposed multilevel framework for future research," Technological Forecasting and Social Change, Elsevier, vol. 193(C).
    2. De Obesso Arias, María de las Mercedes & Pérez Rivero, Carlos Alberto & Carrero Márquez, Oliver, 2023. "Artificial intelligence to manage workplace bullying," Journal of Business Research, Elsevier, vol. 160(C).

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