Ethical and Emotional Intelligence in Algorithmic Management

Abstract

Views on algorithmic management of organisations have emerged as an important subject of contemporary organizational governance, as both artificial intelligence, machine learning, predictive analytics and automated scheduling, performance monitoring, recommendation systems and data-driven decision-support mechanisms increasingly take on functions traditionally considered to be done by human managers. Organizational opportunities arise from the use of algorithmic management, with regards to quicker decision-making, ongoing performance measurement, coordinating the workforce, and resource allocation based on data. Concurrently, algorithmic decision systems raise issues of fairness, transparency, accountability, privacy, autonomy, discrimination, psychological well-being and quality of human relationships in the workplace. These concerns are best addressed from the complementary conceptual frameworks of ethical intelligence and emotional intelligence. While emotional intelligence focuses on identifying, understanding and regulating emotions in managerial relationships, ethical intelligence puts into perspective the values of fairness and responsibility, dignity and transparency, and accountability.

This research proposes an integrated conceptual framework of the relationship between ethical intelligence, emotional intelligence and algorithmic management. The method used is secondary data synthesis with structured literature review on the peer reviewed literature that has been found and institutional evidence related to responsible artificial intelligence, algorithmic management, artificial intelligence in human resource management, and intelligent decision-support systems. Systematic reviews of 107 empirical studies with responsible AI in HRM, 167 peer-reviewed studies on algorithmic management at work, 93 studies on the ethics-based auditing of AI in the workplace, and 45 studies on intelligent decision-support systems and ethical decision-making have been published so far. Throughout, the OECD evidence on managerial perceptions of algorithmic management is added. The numerical analysis reveals that there is a lack of uniformity in ethical governance in the literature. Of 107 empirical HRM studies, 63 of these studies failed to explicitly use a responsible AI principle. OECD evidence showed that 28% of managers with algorithmic management noted that accountability was unclear, 27% noted issues of explainability and 27% noted inadequate protection of physical and mental health of workers. The findings suggest that technical performance is not enough to serve as a basis for governance when using algorithms to manage the trading process.

The analysis suggests that the inclusion of the concepts of ethical intelligence and emotional intelligence in algorithmic management should be done in conjunction with human oversight, explainability, procedural justice, privacy protection, affect-sensitive communication, employee participation and accountability mechanisms. The framework created thereby moves algorithmic management beyond the mere computational towards a socio-technical governance system. Researchers will learn how to integrate the normative and affective aspects of managerial authority into the study of algorithmic managerial authority and how to apply the concepts and theories developed in managerial studies to practical management scenarios.