Who Loses to Automation? AI-Driven Labour Displacement and the Limits of Reskilling Policies in Platform-Based Informal Work in India

Abstract

Rapid advances in artificial intelligence (AI) and automation have led to serious concerns about labour displacement and job insecurity, yet their effects vary widely across different labour market contexts.

This paper examines how AI-driven automation shapes employment outcomes in platform-based informal work in India, with a focus on labour displacement and limits of reskilling policies.  Using a qualitative, secondary literature-based analysis which examines the distinction between job displacement and task displacement, the study examines how algorithmic management restructures work in platform-based employment. The paper argues that while AI primarily operates at the task level, the lack of job security, institutional protection and access to alternative labour tracks means that task-level displacement manifests as complete job displacement in the informal sector especially in platform-based informal work. Furthermore, it shows that prevailing reskilling policies are not suitable for this context as they assume access to stable employment, training opportunities and institutional support that the informal sector generally lacks. The findings highlight the importance of labour market structure in shaping the employment effects of automation and suggest that policy responses must move beyond reskilling to address informality and job quality in developing economies. On the basis of the mentioned findings, this paper proposes potential policy implications which focuses on the importance of a multifaceted approach to reskilling and job protection.