Causal Mapping of Ethical Components in Artificial Intelligence–Enhanced Human Resource Management
Subject Areas : اخلاق و تربیت اسلامی
Fatemeh Fereidun
1
,
abbasali rastegar
2
*
,
Mohsen Shafiei Nik Abadi
3
1 -
2 - semnan university
3 - Semnan Uni, faculty of economics, management and administrative sciences
Keywords: Artificial Intelligence, Human Resource Management, AI Ethics, Data stability, Organizational Justice.,
Abstract :
This study examines the ethical dimensions of deploying artificial intelligence in human resource management within Iranian organizations through a mixed-methods approach. In the qualitative phase, semi-structured interviews with HR and IT experts and thematic analysis using MAXQDA identified eight foundational ethical components: organizational justice and non-discrimination; transparency and explainability; privacy preservation; accountability and answerability; organizational trust; continuous feedback mechanisms and human oversight; awareness-raising and ongoing employee training; and active human participation in decision-making. In the quantitative phase, the fuzzy DEMATEL method in Excel was used to map causal relationships among sixteen ethical factors. Results demonstrate that six factors—system sustainability; assignment of responsibility for errors; awareness-raising about ethical challenges; transparency in selection criteria; algorithm explainability; and human involvement in key decisions—serve as primary causal drivers exerting the strongest influence on other components. Conversely, organizational justice and non-discrimination, employee training and communication, and organizational trust function as effect variables whose improvement depends on strengthening these drivers. The findings underscore that AI ethics policies must be developed within the Iranian cultural and social context and that technological innovation should be aligned with human values and perspectives. The proposed framework offers a structured roadmap for ethical AI governance in HRM, aimed at enhancing organizational trust, improving decision quality, and safeguarding employee well-being.
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