AI-driven surveillance in India: Reconciling privacy, national security and legal oversight
Bibliographic record
Abstract
Artificial intelligence (AI) is having a significant impact on how the surveillance apparatus in India operates. Along with the numerous possibilities, the indoctrination of AI in surveillance mechanisms poses serious privacy concerns. The conflict between state surveillance and the fundamental right of privacy is apparent even at the conceptual level. On the one hand, the rise of advanced surveillance mechanisms has been an abetting factor in this conflict, while on the other hand, many theorists have been at work to find a harmonisation between them. Throughout Indian history, surveillance apparatus has helped thwart threats to national security and maintain the nation’s integrity. The apparent disadvantage of surveillance can be its intrusion into citizens’ right to privacy, which poses several legal challenges. This paper explores how incorporating AI in surveillance mechanisms enhances India’s surveillance apparatus and influences the conflict between national security and privacy rights. The paper examines how revolutionary AI technologies such as predictive policing, facial recognition (FRT) and AI-enhanced monitoring systems aggravate the apparent conflict between national security interests and the fundamental right to privacy, as adjudged in the Puttaswamy judgment. The paper critically analyses the existing legal architecture, which consists of the Telecommunications Act and the IT Act, and highlights its shortcomings. Further, the paper traverses how legal frameworks of other jurisdictions such as the European Union (EU) AI Act, the Canadian AI and Data Act (AIDA) and the US regulatory guidelines could guide India in determining a well-rounded regulatory approach. Additionally, the paper proposes adopting a context-based or risk-based approach to AI regulation and the practical challenges therewith in an attempt to harmonise the state security imperative with citizens’ privacy rights without obstructing technological advancement. The comparative analysis of different regulatory guidelines and legislations and the potential regulations would provide practical insights for the legislature, law enforcement and other stakeholders. The paper ultimately argues that there is an exigence for a comprehensive regulatory framework to conciliate national security and privacy rights in the AI-powered digital landscape. This article is also included in The Business and Management Collection which can be accessed at https://hstalks.com/business/.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".