Ethical Leadership in Algorithmic Management Systems: A Qualitative Exploration of Managerial Accountability
Keywords:
ethical leadership, algorithmic management, managerial accountability, artificial intelligence, qualitative research, organizational ethics, human oversight, TehranAbstract
This study aimed to explore how managers understand, experience, and enact ethical leadership and accountability in organizations that use algorithmic management systems for monitoring, evaluation, coordination, and decision support. This qualitative study was conducted using an interpretive thematic design. Data were collected through semi-structured interviews with 24 managers, supervisors, human resource professionals, operations managers, and digital transformation specialists working in Tehran-based organizations that had implemented algorithmic tools in employee evaluation, scheduling, customer-service monitoring, recruitment screening, productivity analytics, or performance dashboards. Participants were selected through purposive sampling, and recruitment continued until theoretical saturation was reached. Interviews lasted between 45 and 75 minutes and focused on managers’ experiences of algorithmic decision-making, ethical responsibility, transparency, fairness, employee voice, and human oversight. All interviews were transcribed verbatim and analyzed using thematic analysis with the support of NVivo software. Trustworthiness was enhanced through member checking, peer debriefing, audit trail documentation, and repeated comparison of codes and themes. Analysis generated five main categories: ethical sensemaking under algorithmic opacity, fragmented managerial accountability, fairness and dignity in data-driven evaluation, human oversight and employee contestability, and institutionalizing ethical governance. Participants described algorithmic management as increasing efficiency and consistency, but also as creating moral ambiguity when decisions were technically produced yet managerially enforced. Managers reported tension between trusting automated outputs and protecting employees from biased, decontextualized, or opaque evaluations. Ethical leadership emerged as a relational and procedural practice requiring explanation, listening, intervention, and willingness to accept responsibility rather than shifting blame to technology. The study shows that ethical leadership in algorithmic management depends on managers’ capacity to translate abstract AI ethics principles into everyday accountability practices. Algorithmic accountability is not achieved by technical design alone; it requires accountable leaders, transparent procedures, contestable decisions, and organizational governance mechanisms that preserve human judgment, fairness, and employee dignity.
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