Justice Evaluations of Algorithmic Management: The Role of Prior Discrimination Experience

Abstract

Abstract Algorithmic management (AM) is increasingly used and offers several benefits to organizations, such as increased efficiency of processes and greater accuracy. At the same time, employees associate AM systems negatively with justice, which hinders the realization of expected benefits. To understand how justice evaluations of AM are shaped, this study investigated the impact of prior human and algorithmic discrimination experience on this relationship. Three online experiments (n1 = 82; n2 = 83; n3 = 216) demonstrated that AM use is negatively associated with justice evaluations. Study 1 shows that the negative effect of AM use on justice evaluations was weakened if participants had prior human discrimination experience. Study 2 does not show a moderating effect of prior algorithmic discrimination experience on the relationship between AM use and justice evaluations. Study 3 further demonstrates that algorithmic discrimination is perceived less negatively compared to human discrimination. Qualitative responses show that individuals associated algorithmic discrimination with biased data and opacity, whereas human discrimination was associated with favoritism and increased human bias. These findings suggest that justice evaluations of AM systems are shaped by individuals’ prior experiences. Understanding these patterns can help organizations design and implement AM systems that promote justice.

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