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Structural oppression and AI: A systematic review of data policy frameworks in India

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  • Biju, P.R.
  • Gayathri, O.

Abstract

This paper argues that emerging AI governance frameworks in India must make sense of the structural inequalities in Indian society, as the prevailing standards set by globally recognized models are inadequate to address discrimination reinvented by algorithm systems deployed in various social situations. AI systems are mathematically grounded in universally applicable principles such as linear algebra, calculus, and probability theory. At the same time, their context-specific ethical, institutional, and cultural forces also influence their design and inventions. When algorithmic systems are integrated into public service delivery, exclusionary consequences encountered by marginalized groups cannot be attributed solely to algorithmic bias. Adopting a systematic review of popular global AI policy frameworks and a comparative assessment of Indian use-cases, particularly in welfare and governance domains, this study proposes that structural failures across various levels from data entry, and verification, to infrastructure design, and implementation, compound the risks attributed to automation. Findings of the paper call for a re-conceptualization of AI not as an isolated technical entity but as part of a dynamic network of interacting systems. The study advances the demand for policy interventions that recognize these interdependencies and promote fairness, transparency, and justice in the design and governance of AI in India.

Suggested Citation

  • Biju, P.R. & Gayathri, O., 2026. "Structural oppression and AI: A systematic review of data policy frameworks in India," Technological Forecasting and Social Change, Elsevier, vol. 223(C).
    • Handle: RePEc:eee:tefoso:v:223:y:2026:i:c:s0040162525004469
    DOI: 10.1016/j.techfore.2025.124415
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    References listed on IDEAS

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