Trust, Fairness, and Wellbeing in the Age of Algorithmic Management: A Cross-Industry Study of AI-Driven People Management in India

Abstract

Purpose — This paper examines the relationship between the growing use of artificial intelligence (AI) in human resource management (HRM) — spanning recruitment screening, performance evaluation, attrition prediction, and algorithmic monitoring — and employees' perceptions of organizational justice, trust, and wellbeing in India. It investigates whether rapid cross-industry adoption of AI-driven people-management tools is being matched by a corresponding growth in employee trust and perceived fairness, or whether an adoption-trust gap is emerging.
Design/methodology/approach — Methodology (Summarized):
The study uses secondary data from eight India-specific workplace surveys (2023–2025) and the Fairwork India Ratings 2023 report. It applies descriptive statistics, Pearson correlation, and a paired t-test to examine AI adoption, employee concerns, and employer–employee confidence gaps. The analysis is based on Organizational Justice, Trust in Automation, Algorithmic Control, UTAUT, Job Demands–Resources, and Privacy Calculus theories. A conceptual model with six hypotheses is proposed for future PLS-SEM validation.
Findings: AI adoption in India is rapidly increasing, with 72% of organizations using AI in HR, 43% of employees, and 92% of knowledge workers using AI at work. Despite high adoption, trust and fairness remain concerns. The EY (2025) survey found a 5.5% employer–employee confidence gap, highest in data privacy (8%). A weak correlation (r = 0.13) showed AI adoption does not directly increase employee concerns, highlighting the role of organizational factors. Additionally, Fairwork India (2023) found poor fairness standards across gig platforms, with no platform scoring on Fair Representation, reflecting limited worker participation in algorithmic management.
Originality/value — The paper contributes one of the first integrated, cross-industry (not IT-sector-specific) syntheses of India's AI-HRM adoption-trust gap, triangulating eight independent survey sources and a fair-work audit within a single organizational-justice-and-trust framework. It also proposes a testable conceptual model — with validated-scale recommendations (Colquitt, 2001; Edmondson, 1999) and a PLS-SEM analytical plan — to guide the primary-data research this evidence synthesis identifies as the necessary next step.