Reducing organizational inequalities associated with algorithmic controls

• Published in: Discover Artificial Intelligence 2024-12, Vol.4 (1), p.36-18, Article 36
• Main Authors: Li, Yueqi, Xiang, Biyun
• Format: Article
• Language: English
• Subjects: Algorithmic control; Artificial Intelligence; Computer Science; Decision making; Employees; Employment; Engineering; Organizational inequality; Participatory AI; Trustworthy AI
• ISSN: 2731-0809; 2731-0809
• DOI: 10.1007/s44163-024-00137-0

Algorithmic 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.