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A multi-stakeholder ethical framework for AI-augmented HRM

Verma Prikshat (Cardiff Metropolitan University – Llandaff Campus, Cardiff, UK)
Parth Patel (Discipline of Management and Human Resources, Australian Institute of Business, Adelaide, Australia)
Arup Varma (Loyola University Chicago, Chicago, Illinois, USA)
Alessio Ishizaka (Information Systems, Supply Chain Management and Decision Support, NEOMA Business School, Mont-Saint-Aignan, France)

International Journal of Manpower

ISSN: 0143-7720

Article publication date: 20 January 2022

Issue publication date: 22 April 2022

2492

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.

Keywords

Citation

Prikshat, V., Patel, P., Varma, A. and Ishizaka, A. (2022), "A multi-stakeholder ethical framework for AI-augmented HRM", International Journal of Manpower, Vol. 43 No. 1, pp. 226-250. https://doi.org/10.1108/IJM-03-2021-0118

Publisher

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Emerald Publishing Limited

Copyright © 2021, Emerald Publishing Limited

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