Algorithmic Persuasion and Populist Fiscal Politics: AI-Enabled Voter Targeting, Freebie-Based Political Marketing, and Their Long-Term Fiscal Consequences in India

Abstract

This paper examines the convergence of two consequential developments in Indian democratic practice: the deployment of artificial intelligence (AI) tools for voter profiling and persuasion, and the intensification of populist ‘freebie’-based fiscal politics at the state level. Drawing on the ideational and political-strategic traditions in populism studies (Mudde, 2004; Weyland, 2001), the paper argues that AI-enabled microtargeting has lowered the transaction costs of populist fiscal mobilisation by allowing parties to identify, message, and reward narrow demographic segments- most visibly women voters through unconditional cash-transfer schemes- with a precision that was unavailable to earlier generations of patronage politics. Using Reserve Bank of India state-finance data, budget documents from Madhya Pradesh, Karnataka, West Bengal, Maharashtra, and Tamil Nadu, Supreme Court litigation records, and industry and journalistic accounts of AI-enabled campaigning in the 2024 general election and the 2025 Bihar and Delhi state elections, the paper develops a composite account of how algorithmic persuasion and cash-transfer populism interact to reshape sub-national fiscal trajectories. The analysis finds that while aggregate consolidated state finances have remained within Fiscal Responsibility and Budget Management (FRBM) thresholds, an identifiable subset of high-debt states exhibits a widening gap between revenue-account subsidy commitments and capital expenditure, with debt-to-Gross State Domestic Product (GSDP) ratios persistently above the 35 per cent caution threshold flagged by the RBI. The paper concludes with an assessment of regulatory responses- including the Election Commission of India’s AI-labelling advisories and the pending Supreme Court reference in Ashwini Kumar Upadhyay Vs Union of India- and offers policy recommendations for reconciling redistributive welfare politics with fiscal sustainability in an algorithmically mediated electoral environment.