Poornima Mahesh

RIGHTS REMEDIES AND REDRESS FOR AI HARMS A REGULATORY DESIGN FOR FAIR WORKPLACES

Poornima Mahesh¹

This paper proposes a rights-based regulatory design to address harms caused by AI systems in the workplace, centering remedies and redress mechanisms that protect workers' autonomy, privacy, and livelihoods. centring remedies and redress mechanisms that protect worker autonomy, privacy, and livelihood. The Objectives are threefold: (1) map the main categories of AI-related workplace harms (surveillance, automated decision-making in hiring, task allocation and performance evaluation, and opaque disciplinary algorithms); (2) assess existing legal and institutional remedies across jurisdictions with emphasis on gaps in applicability; and (3) propose a workable regulatory architecture that integrates statutory rights, accessible remedies, and organizational accountability measures. to map the principal categories of AI-related workplace harms (surveillance, automated decision-making in hiring, task allocation and performance evaluation, and opaque disciplinary algorithms); (2) to evaluate existing legal and institutional remedies across jurisdictions with emphasis on enforceability gaps; and (3) to propose a practicable regulatory architecture that integrates statutory rights, accessible redress pathways, and organizational accountability measures. The Methodology combines doctrinal legal analysis, comparative policy review (India, EU and select Global South contexts) and qualitative case synthesis of recent litigation and regulatory actions involving platform and corporate employers. EU, and selected Global South contexts), and qualitative case synthesis of recent litigation and regulatory actions involving platform and enterprise employers. The analysis highlights recurring obstacles – burden of proof, opacity of evidence, regulatory fragmentation and power asymmetries between workers and employers – and examines tools such as algorithmic impact assessments, mandatory explainability standards, collective bargaining supplements and specialized ombudsman or court processes. obstacles—burdens of proof, evidentiary opacity, regulatory fragmentation, and power asymmetries between workers and employers—and examines tools such as algorithmic impact assessments, mandated explainability standards, collective bargaining complements, and specialized ombuds or tribunal processes. Expected contributions include a modular regulatory model that links the articulation of rights to procedural remedies, the design of principles for enforceable remedies (including presumptions of transparency and evolving burdens of proof), and policy recommendations tailored to jurisdictions with limited enforcement capacity. articulation to procedural remedies, design principles for enforceable redress (including presumptions of transparency and shifting evidentiary burdens), and policy recommendations tailored for jurisdictions with limited enforcement capacity. The paper is aimed at policymakers, labor regulators and academics who are looking for concrete frameworks to ensure that the use of AI at work respects fundamental rights and provides rapid and effective remedies. targets policymakers, labour regulators, and scholars seeking actionable frameworks to ensure AI use at work respects fundamental rights and provides timely, effective remedies.