Surveillance Pricing and Consumer Autonomy: Regulating AI-Driven Algorithmic Price Discrimination in Digital Marketplaces across the United States, the European Union, and India

Abstract

The proliferation of artificial intelligence in digital marketplaces has enabled a commercial practice that regulatory authorities now term surveillance pricing. Online retailers and digital platforms deploy AI-driven algorithms that analyse consumer data including browsing history, geographic location, purchase patterns, and demographic profiles to generate individualised prices for identical goods and services. Preliminary findings from a federal market study in the United States released in January 2025 confirmed the widespread extraction of granular personal data for price personalisation. Legislative responses have since proliferated across multiple jurisdictions. New York enacted the first state-level algorithmic pricing disclosure law, effective November 2025. Maryland signed legislation restricting AI-enabled pricing in food retail in April 2026. The first comprehensive state-level AI governance statute targeting high-risk systems takes effect in Colorado in June 2026. More than forty state-level bills across twenty-four states were introduced in the first quarter of 2026 alone. The European Union addresses algorithmic pricing through overlapping instruments, principally its AI Act, data protection regulation, and digital services legislation. India has responded through consumer protection statutes, dark patterns guidelines, data protection legislation, and implementing rules that impose algorithmic due diligence obligations on significant data fiduciaries. Doctrinal and comparative legal research methods structure the analysis across these three jurisdictions. Findings reveal a regulatory spectrum from transparency-focused disclosure mandates in the United States to comprehensive risk-based governance in the European Union to an evolving sectoral approach in India. Consumer pricing transparency emerges as a component of algorithmic fairness. A model regulatory framework combining mandatory disclosure, algorithmic auditing, and sector-specific prohibitions on discriminatory pricing practices is proposed.