Responsible artificial intelligence in human resources management: a review of the empirical literature

Bujold, Antoine; Roberge‐Maltais, Isabelle; Parent‐Rocheleau, Xavier; Boasen, Jared; Sénécal, Sylvain; Léger, Pierre‐Majorique

doi:10.1007/s43681-023-00325-1

Cited by 17 publications

(6 citation statements)

References 153 publications

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“…This work holds implications for scholars and practitioners alike. Scholars of many disciplines have already pointed towards the potential danger of algorithmic bias (e.g., Baker & Hawn, 2022;Bujold et al, 2023;Kordzadeh & Ghasemaghaei, 2022). Recent developments and advancements in generative AI have added to the scenarios in which algorithmic bias could lead to discrimination or reinforce existing stereotypes.…”

Section: Discussionmentioning

confidence: 99%

Algorithmic Bias in Image-Generating Artificial Intelligence: Prevalence and User Perceptions

Messingschlager,

Appel

2024

Preprint

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Image-generating AI is among the most popular generative AI applications, likely changing the visual mediated environments humans are exposed to on a mass scale. Prior work found that AI can be biased against women and minorities (algorithmic bias), whereas humans attribute rather high objectivity to AI. We focused on image-generating AI, analyzing the extent of algorithmic bias in AI-generated pictures, as well as human responses to bias in image-generating AI. Study 1 showed that AI-generated portraits of people in STEM professions were almost exclusively depicting male, white (and older) individuals. Study 2 (experimental, N = 495) showed that the responses to AI-generated pictures vary, depending on the portrayed group. Participants perceived pictures to be less biased if they were introduced as AI-generated, but only if pictures showed college students (vs. older people). If images showed older people, participants reported higher moral outrage if pictures were supposedly generated by AI (vs. human creators).

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Section: Discussionmentioning

confidence: 99%

Algorithmic Bias in Image-Generating Artificial Intelligence: Prevalence and User Perceptions

Messingschlager,

Appel

2024

Preprint

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“…In conjunction with previous investigations specifically addressing AI and HRM (e.g., Parry and Battista, 2019;Kaur et al, Budhwar et al, 2022;Garg et al, 2022;Gélinas et al, 2022;Palos-Sánchez et al, 2022;Vrontis et al, 2022;Pereira et al, 2023;Alsaif and Sabih Aksoy, 2023;Basu et al, 2023;Bujold et al, 2023;Chowdhury et al, 2023;Jatobá et al, 2023;Kaushal and Ghalawat, 2023;Malik et al, 2023;Pan and Froese, 2023;Prikshat et al, 2023), we provide several contributions to the academic field. First, to our knowledge, our scoping review is the most inclusive so far, as we expanded our data extraction across four disciplines (management, HRM/IR, psychology, and IS).…”

Section: Theoretical Implications and Agenda For Future Researchmentioning

confidence: 95%

The effects of artificial intelligence on human resource activities and the roles of the human resource triad: opportunities and challenges

Dima,

Gilbert,

Dextras-Gauthier

et al. 2024

Front. Psychol.

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IntroductionThis study analyzes the existing academic literature to identify the effects of artificial intelligence (AI) on human resource (HR) activities, highlighting both opportunities and associated challenges, and on the roles of employees, line managers, and HR professionals, collectively referred to as the HR triad.MethodsWe employed the scoping review method to capture and synthesize relevant academic literature in the AI–human resource management (HRM) field, examining 27 years of research (43 peer-reviewed articles are included).ResultsBased on the results, we propose an integrative framework that outlines the five primary effects of AI on HR activities: task automation, optimized HR data use, augmentation of human capabilities, work context redesign, and transformation of the social and relational aspects of work. We also detail the opportunities and challenges associated with each of these effects and the changes in the roles of the HR triad.DiscussionThis research contributes to the ongoing debate on AI-augmented HRM by discussing the theoretical contributions and managerial implications of our findings, along with avenues for future research. By considering the most recent studies on the topic, this scoping review sheds light on the effects of AI on the roles of the HR triad, enabling these key stakeholders to better prepare for this technological change. The findings can inform future academic research, organizations using or considering the application of AI in HRM, and policymakers. This is particularly timely, given the growing adoption of AI in HRM activities.

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“…These three phases, from technocratic to fully-embedded, are derived based on the evolution of AI technology adoption within the field of HRM. The first two phases are based on recent empirical literature on AI in HRM (e.g., Arslan et al, 2022 ; Bansal et al, 2023 ; Bujold et al, 2023 ). The last phase is our conceptual view, and it represents a logical progression of how AI is integrated into HRM practices and aligns with broader developments in technology adoption and societal goals (e.g., He et al, 2021 ).…”

Section: How Hrm Can Bring Humans and Machines Closer Together In The...mentioning

confidence: 99%

Revisiting the role of HR in the age of AI: bringing humans and machines closer together in the workplace

Fenwick,

Molnar,

Frangos

2024

Front. Artif. Intell.

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The functions of human resource management (HRM) have changed radically in the past 20 years due to market and technological forces, becoming more cross-functional and data-driven. In the age of AI, the role of HRM professionals in organizations continues to evolve. Artificial intelligence (AI) is transforming many HRM functions and practices throughout organizations creating system and process efficiencies, performing advanced data analysis, and contributing to the value creation process of the organization. A growing body of evidence highlights the benefits AI brings to the field of HRM. Despite the increased interest in AI-HRM scholarship, focus on human-AI interaction at work and AI-based technologies for HRM is limited and fragmented. Moreover, the lack of human considerations in HRM tech design and deployment can hamper AI digital transformation efforts. This paper provides a contemporary and forward-looking perspective to the strategic and human-centric role HRM plays within organizations as AI becomes more integrated in the workplace. Spanning three distinct phases of AI-HRM integration (technocratic, integrated, and fully-embedded), it examines the technical, human, and ethical challenges at each phase and provides suggestions on how to overcome them using a human-centric approach. Our paper highlights the importance of the evolving role of HRM in the AI-driven organization and provides a roadmap on how to bring humans and machines closer together in the workplace.

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Responsible artificial intelligence in human resources management: a review of the empirical literature

Cited by 17 publications

References 153 publications

Algorithmic Bias in Image-Generating Artificial Intelligence: Prevalence and User Perceptions

Algorithmic Bias in Image-Generating Artificial Intelligence: Prevalence and User Perceptions

The effects of artificial intelligence on human resource activities and the roles of the human resource triad: opportunities and challenges

Revisiting the role of HR in the age of AI: bringing humans and machines closer together in the workplace

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