Frontiers in artificial intelligence · 2026
Governing algorithms, empowering people: how ethical AI oversight shapes trust, technostress, and employee autonomy in AI-enabled HRM.
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Patnaik Tamrisha, Gnankob Robert Ipiin, Nanda Nitya Sundar
Аннотация
INTRODUCTION: Artificial intelligence is increasingly embedded in human resource management, yet its implications for employee autonomy depend not only on technological capability but also on how algorithmic systems are governed. This study examines how algorithmic transparency, perceived algorithmic fairness, and human-in-the-loop oversight shape employee autonomy through trust in AI, psychological safety, and technostress.
METHODS: Drawing on Social Exchange Theory and Conservation of Resources Theory, the study tested an integrated model using survey data from 290 employees working in organisations that employ AI-enabled HRM systems in India. The hypothesised direct and mediated relationships were examined using partial least squares structural equation modelling.
RESULTS: Algorithmic transparency, perceived algorithmic fairness, and human-in-the-loop oversight significantly strengthened employee trust in AI. Algorithmic transparency also enhanced psychological safety, while human-in-the-loop oversight reduced technostress. Trust in AI and psychological safety positively influenced employee autonomy, whereas technostress negatively influenced autonomy. Trust mediated the relationships between transparency, fairness, human oversight, and employee autonomy. Psychological safety mediated the transparency-autonomy relationship, while technostress mediated the relationship between human oversight and autonomy.
DISCUSSION: The findings demonstrate that employee autonomy in AI-enabled workplaces is shaped through relational, psychological, and resource-preservation mechanisms. Responsible AI-enabled HRM therefore requires more than technical efficiency; transparent decision processes, fairness safeguards, meaningful human oversight, and psychologically safe opportunities to question algorithmic decisions are important for preserving employee agency.
METHODS: Drawing on Social Exchange Theory and Conservation of Resources Theory, the study tested an integrated model using survey data from 290 employees working in organisations that employ AI-enabled HRM systems in India. The hypothesised direct and mediated relationships were examined using partial least squares structural equation modelling.
RESULTS: Algorithmic transparency, perceived algorithmic fairness, and human-in-the-loop oversight significantly strengthened employee trust in AI. Algorithmic transparency also enhanced psychological safety, while human-in-the-loop oversight reduced technostress. Trust in AI and psychological safety positively influenced employee autonomy, whereas technostress negatively influenced autonomy. Trust mediated the relationships between transparency, fairness, human oversight, and employee autonomy. Psychological safety mediated the transparency-autonomy relationship, while technostress mediated the relationship between human oversight and autonomy.
DISCUSSION: The findings demonstrate that employee autonomy in AI-enabled workplaces is shaped through relational, psychological, and resource-preservation mechanisms. Responsible AI-enabled HRM therefore requires more than technical efficiency; transparent decision processes, fairness safeguards, meaningful human oversight, and psychologically safe opportunities to question algorithmic decisions are important for preserving employee agency.