AI, Automation & Algorithmic HR: Bias, Trust, Agentic Systems, and the Transformation of Generalist Roles in the Indian Context
TL;DR
It is argued that while AI offers efficiency and scale, it simultaneously amplifies risks of discrimination, erodes perceived justice, demands new governance architectures, and necessitates a fundamental reorientation of HR competencies toward judgment, ethics, oversight, and human-centric capabilities.
Abstract
Artificial intelligence, automation, and algorithmic systems are reshaping human resource management functions—from recruitment and performance appraisal to operational workflows. This paper examines four interconnected dimensions of this transformation, with particular attention to the Indian institutional and organizational context: algorithmic bias in AI-based recruitment and screening tools and the resulting legal exposure under Indian equal-opportunity norms; employee perceptions of trust and fairness in AI-driven performance appraisal; adoption barriers and governance challenges of agentic AI in HR operations within Indian organizations; and the impact of generative AI on HR generalist roles and the consequent skill shifts required. Drawing on constitutional provisions, statutory frameworks (including the Digital Personal Data Protection Act, 2023), empirical studies, industry surveys, and emerging policy guidance, the paper argues that while AI offers efficiency and scale, it simultaneously amplifies risks of discrimination, erodes perceived justice, demands new governance architectures, and necessitates a fundamental reorientation of HR competencies toward judgment, ethics, oversight, and human-centric capabilities. Recommendations emphasize mandatory bias audits, human-in-the-loop designs, robust agentic governance, and deliberate reskilling strategies.