Not seeing the (moral) forest for the trees? How task complexity and employees' expertise affect moral disengagement with discriminatory data analytics recommendations

Data analytics provides versatile decision support to help employees tackle the rising complexity of today's business decisions. Notwithstanding the benefits of these systems, research has shown their potential for provoking discriminatory decisions. While technical causes have been studied, the human side has been mostly neglected, albeit employees mostly still need to decide to turn analytics recommendations into actions. Drawing upon theories of technology dominance and of moral disengagement, we investigate how task complexity and employees' expertise affect the approval of discriminatory data analytics recommendations. Through two online experiments, we confirm the important role of advantageous comparison, displacement of responsibility, and dehumanization, as the cognitive moral disengagement mechanisms that facilitate such approvals. While task complexity generally enhances these mechanisms, expertise retains a critical role in analytics-supported decision-making processes. Importantly, we find that task complexity's effects on users' dehumanization vary: more data subjects increase dehumanization, whereas richer information on subjects has the opposite effect. By identifying the cognitive mechanisms that facilitate approvals of discriminatory data analytics recommendations, this study contributes toward designing tools, methods, and practices that combat unethical consequences of using these systems.