Abstract
AbstractAlgorithmic technologies are widely applied in organizational decision-making today, which can improve resource allocation and decision-making coordination to facilitate the accuracy and efficiency of the decision-making process within and across organizations. However, algorithmic controls also introduce and amplify organizational inequalities—workers who are female, people of color and the marginalized population, and workers with low skills, a low level of education, or who have low technology literacy can be disadvantaged and discriminated against due to the lack of transparency, explainability, objectivity, and accountability in these algorithms. Through a systematic literature review, this study comprehensively compares three different types of controls in organizations: technical controls, bureaucratic controls, and algorithmic controls, which led to our understanding of the advantages and disadvantages associated with algorithmic controls. The literature on the organizational inequality related to the employment of algorithmic controls is then discussed and summarized. Finally, we explore the potential of trustworthy algorithmic controls and participatory development of algorithms to mitigate organizational inequalities associated with algorithmic controls. Our findings raise the awareness related to the potential corporate inequalities associated with algorithmic controls in organizations and endorse the development of future generations of hiring and employment algorithms through trustworthy and participatory approaches.
Publisher
Springer Science and Business Media LLC
Reference129 articles.
1. Kellogg KC, Valentine MA, Christin A. Algorithms at work: the new contested terrain of control. Acad Manag Ann. 2020;14(1):366–410. https://doi.org/10.5465/annals.2018.0174.
2. Rodgers W, Murray JM, Stefanidis A, Degbey WY, Tarba SY. An artificial intelligence algorithmic approach to ethical decision-making in human resource management processes. Hum Resour Manag Rev. 2023;33(1): 100925. https://doi.org/10.1016/j.hrmr.2022.100925.
3. Liu M, Huang Y, Zhang D. Gamification’s impact on manufacturing: enhancing job motivation, satisfaction and operational performance with smartphone-based gamified job design. Hum Factors Ergon Manuf Serv Ind. 2018;28(1):38–51. https://doi.org/10.1002/hfm.20723.
4. Liu YE, Mandel T, Brunskill E, Popovic Z. Trading off scientific knowledge and user learning with multi-armed bandits. In EDM, London, United Kingdom; 2014. pp. 161–168.
5. European Commission. Proposal for regulation of the European parliament and of the council—Laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain Union legislative acts. 2021, April 21. https://digital-strategy.ec.europa.eu/en/library/proposal-regulation-laying-down-harmonised-rules-artificial-intelligence. Accessed 7 December 2023.