Reference : Artificial Intelligence Applications in Human Resources Management: Implications for ...
Scientific congresses and symposiums : Unpublished conference/Abstract
Business & economic sciences : Human resources management
http://hdl.handle.net/2268/267037
Artificial Intelligence Applications in Human Resources Management: Implications for Skills
English
Franssen, Marine mailto [Université de Liège - ULiège > HEC Liège : UER > LENTIC >]
Rondeaux, Giseline mailto [Université de Liège - ULiège > HEC Liège : UER > LENTIC >]
5-Jul-2021
No
International
After Covid? Critical Conjunctures and Contingent Pathways of Contemporary Capitalism
Du 2 juillet 2021 au 5 juillet 2021
The Society for the Advancement of Socio-Economics (SASE)
[en] AI ; human resources management ; skills ; artificial intelligence ; HRM ; sociomateriality
[fr] IA ; gestion des ressources humaines ; compétences
[en] intelligence artificielle ; GRH ; sociomatérialité
[en] Context
Over the past years, the use of artificial intelligence-based tools has emerged in vast areas of HR professional practices, particularly in the field of recruitment (Jatobá et al., 2019; Patmore et al., 2017). As Levy (2018) noted, tools designers claim these AI-based applications enhance human resources policies. Notably, they often underline such technology provide rational decisions based on objective, unbiased and measurable sets of data, as opposed to practices based on a gut feeling. In the management literature, a growing field is increasingly paying attention to the link between artificial intelligence and skills. From a decontextualized point of view, many of these works highlight the importance of soft skills, said to be central because of their presumed irreplaceability by the machine (Frey & Osborne, 2013).
Objective of the paper
Our paper aims to find out whether such claims are effectively reflected in the HR-practices of a selected enterprise and to document the evolution of the HR function within that enterprise. Drawing on from a sociomaterial perspective (Leonardi, 2011, 2013), we consider the imbrication of HR applications based on AI and the evolution of professional practices and skills of HR officers using these applications.
Methodology
Findings are part of a study commissioned by a major European group in the telecom sector. HR practices related to artificial intelligence were investigated over the year 2019 through several interconnected research methods. Among those methods were semi-structured interviews with executives of the company, the distribution of a questionnaire to 7800 employees and managers, as well as a focus group discussion with ten HR professionals.
Results
The transversal analysis of our empirical approaches shows an evolution in terms of expected competencies profiles for HR professionals. In particular, they illustrate how AI-technologies and human activities combine into sociomaterial HR practices implying evolutions in terms of skills. We also illustrate how these technologies are not neutral but, on the contrary, enacted in action and producing new biases that need to be understood. In particular, we argue that the cruciality of soft skills needs to be rethought in favour of a deformation of occupational skills and the acquisition of a basic digital literacy.
Conclusion
Our results highlight the relevance of taking a step back from the trends in the literature as regards the rise of AI. They are generally expressed in terms of neutrality of the technology and through an emphasis on soft skills development to strengthen human capabilities. Our research show how the social and material aspects of HR practices and AI applications are imbricated with each other and produce effects on competencies profiles: through reconfigured occupational skills and basic numeric literacy is the HR officer able to see beyond data sorting algorithms and candidate strategies for example.
Laboratoire d'Etudes sur les Nouvelles formes de Travail, l'Innovation et le Changement - LENTIC
Researchers ; Professionals ; Students
http://hdl.handle.net/2268/267037

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