8. Trust in Artificial Intelligence in Human Resources Development

Data-driven global companies are increasingly using data-driven learning academies with artificial intelligence (AI), data-driven skills development processes, or technology-enhanced learning (up-skilling, re-skilling, skill matching) for self-directed, personalized employee learning. There has been observed a shift in the relationship between employees and the manager, with whom training measures traditionally agreed, and the self-organized application of AI-supported learning systems. For example, the collection and use of data in an organizational context leaves employees vulnerable. They are dependent on trusting the managers with their competence and intentions, and equally trusting that the AI used will produce credible and meaningful data. Thus, this study was aimed at identifying the critical variables of trust in the relationship among an employee, the AI used, and the manager. The proposed research method consists of an integrative literature review. Mature trust literature is synthesized with new perspectives of AI literature on trust. The result is a new integrative concept of trust in AI based on the integrative model of organizational trust, extended by aspects of initial trust and FEAS (fairness, explainability, auditability, and safety) elements of trustworthy AI. A salient attribute of this concept is the employeeʼs perspective, which provides groundwork for further research, theoretical, and practical contributions and provides implications for trust-sensitive management of AI in organizations.