The incorporation of algorithmic systems into organizations is reconfiguring decision-making processes and raising new ethical challenges related to transparency, impartiality, and accountability. This study maps the field of algorithmic ethics in organizational contexts through a co-citation–based bibliometric analysis of 1,437 Web of Science publications (search conducted on August 20, 2025). The analysis identifies 12 thematic clusters and reveals a robust intellectual structure, with high modularity (Q = 0.726) and a high weighted mean silhouette value (S = 0.894). The findings highlight the centrality of algorithmic management, responsible artificial intelligence, and explainability, as well as bridging works that connect technical, normative, and management-oriented perspectives. The study advances an integrative conceptual model and a future research agenda that point to the emergence of algorithmic ethics as an institutional logic of organizational governance. For managers, the results underscore the need to embed algorithmic ethics within organizational decision-making and control systems.

Why do organizations conduct job interviews? The traditional view of interviewing holds that interviews are conducted, despite their steep costs, to predict a candidate’s future performance and fit. This view faces a twofold threat: the behavioral and algorithmic threats. Specifically, an overwhelming body of behavioral research suggests that we are bad at predicting performance and fit; furthermore, algorithms are already better than us at making these predictions in various domains. If the traditional view captures the whole story, then interviews seem to be a costly, archaic human resources procedure sustained by managerial overconfidence. However, building on T. M. Scanlon’s work, we offer the value of choice theory of interviewing and argue that interviews can be vindicated once we recognize that they generate commonly overlooked kinds of noninstrumental value. On our view, interviews should thus not be entirely replaced by algorithms, however sophisticated algorithms ultimately become at predicting performance and fit.