A body of research is emerging in management and human resource management literature on the implications of artificial intelligence for organizational justice and for diversity, equity, and inclusion (DEI). Recruitment, as one primary focus area of this research, shows simultaneous potential for bias reduction through standardization and new risks of negative impact through proxy variables embedded in training data, as well as workers' and applicants' fairness perceptions related to the transparency and explainability of the process (Soleimani et al. 2025). Beyond hiring, evidence from algorithmic management in monitoring and compensation highlights distributive concerns (e.g., pay, task allocation, and scheduling outcomes), procedural concerns (opacity, appealability, data governance), and relational concerns (dehumanization and trust in AI-mediated interactions) in the use of AI in the workplace (Doan and Diehl 2025; Keegan and Meijerink 2025; Zhang et al. 2025).

Despite fast-growing interest in the impact of AI on work and employment, significant gaps remain. Organizational justice can be a central concept for designing "responsible AI" in HR and for tying AI outcomes to employees' lived experiences across different groups — a link that can advance DEI and justice rather than merely comply with accuracy or efficiency benchmarks. The construct of organizational justice encompasses three key dimensions: (a) procedural (the fairness of the methods used to reach a decision, including concepts such as transparency and voice), (b) distributive (the fairness of the outcome itself and outcome parity across intersections such as gender × ethnicity × age), and (c) relational (the fairness of how people treat each other, including respectful communication, dignity, and opportunities for interaction with accountable humans). Most current research on AI in the workplace focuses mainly on procedural aspects, such as transparency and consistency in algorithmic decision-making, but neglects how the opacity and automation of AI may erode relational trust and supervisor–employee dynamics. Furthermore, the impact of AI on the outcomes of marginalized workers (i.e., distributive justice) remains underexplored, particularly where intersecting identities are involved.

A holistic justice framework that integrates all three dimensions is essential for developing ethical AI governance in HRM. The purpose of this study is to examine how the implementation of AI-driven human resource management (HRM) systems affects employees' perceptions of organizational justice across its three dimensions. The research questions are:

• How do AI design features in HR decision-making influence employees' and applicants' perceptions of justice?
• To what extent do these perceptions differ across social groups (e.g., gender, age cohorts, people with a migratory background)?

Methodology

A series of vignette-based survey experiments will be conducted in which professionals evaluate realistic HR decisions made with AI assistance. A survey experiment combines the internal validity of randomized experiments with the external realism and scalability of surveys. Participants will be randomly assigned to read short scenarios ("vignettes") that vary specific features of an AI-enabled decision process and will then report their perceptions. Participants will be recruited through high-quality online survey panels.