A Systematic Review on AI and Employee Trust: Examining Transparency, Fairness and Human Oversight in HR Decision Making

Abstract

Adopting AI in HRM revolutionizes the hiring, evaluation and decision-making within companies. However if the procedural justice vision – transparency of algorithms and reduced human involvement in technology – is going to be realized, there are many questions that need to be answered. All these factors are crucial in influencing and building employee attitudes and confidence towards AI HR tools. Objective: This approach has been used consistently to examine the phenomenon of using AI in the HR decision making process, as well as the moderation-mediatory analysis, has been discussed based on the three enabling factors ‘transparency, fairness and human supervision'. A detailed job market analysis was also included – in the context of India. In terms of socio-legal and cultural aspects they could be perceived as unusual in terms of AI use in an Indian context. Methods: The search process has been done using Scopus and Web of Science (WoS) based on PRISMA 2020 with search period starting from year 2021-2026 and adding other search engines (JSTOR, SSRN, Google scholar) and peer-reviewed articles. The parameters with respect to which search has been done are literature pertaining to employee trust, transparency research literature, research literature where there is no role of any algorithmic involvement at all and then, the concepts of algorithmic fairness. Quality assessment was done using the CASP evaluation frameworks and Joanna Briggs Institute. topic synthesis and data extraction procedures were applied to analyze the data to get the results of the integration. Results: The total records of the database were 1883 – of which 105 studies were included – final selection. From this comes some of key synthesis implications: there is no clear correlation between algorithmic transparency and employee trust, procedural and/or distributive fairness can be considered as intermediate points in the continuum between algorithmic transparency and employee trust, there are other contextual factors for employee trust some of which are ‘human' and some having an impact on the relationship between the elements of AI and employee trust and finally, specifically, the boundary conditions in Indian context are a fragmented regulatory landscape, or digital illiteracy, regional/cultural orientations have impacts on the relationship between the elements of AI and employee trust. Conclusions: Trust in AI for HR decision making is not a relation or tech – but a socio tech challenge and demands more than just tech strategies. The study recommends the use of interdisciplinary thinking; in the AI system the attributes are interconnected and viewed from the perspective of the contextual (moderating) factors, organizational justice theories and its relationship with the following employee indicators: trust. Highlighted policy recommendations/policy implications with a particular emphasis on the implications of the policy for HR practitioners, for policy makers and developers of AI in the unique context of India.