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Introduction

Technological interventions such as biometric authentication, digital tracking and app-based monitoring are often projected as solutions to inefficiency and leakage in welfare delivery. Yet, their surveillance-oriented design may unintentionally deepen exclusion by imposing rigid verification, infrastructure dependence and disproportionate scrutiny on vulnerable beneficiaries.

Body

Efficacy of surveillance-based applications in India’s welfare architecture

• Leakage Control: Biometric authentication curbs duplicate and ghost beneficiaries, strengthening welfare targeting and reducing corruption in delivery chains.
  Eg: Aadhaar authentication helped identify duplicate ration cards in several states, reducing subsidy leakage.
• Faster Transfers: Surveillance-linked Aadhaar-based systems enable direct benefit transfer without intermediaries, improving the timeliness of payments.
  Eg: Aadhaar-enabled DBT expedited payments under PM-Kisan by authenticating beneficiaries swiftly.
• Digital Audit Trails: App-based monitoring creates searchable audit logs that enhance accountability of frontline workers and implementing agencies.
  Eg: e-Shram and PM-Jan Arogya Yojana dashboards facilitate real-time administrative monitoring.
• Scalability & Integration: Unified digital IDs enable integration across schemes, reaching millions without needing separate verification systems.
  Eg: Large-scale Aadhaar-linked PDS integration allowing portability under One Nation One Ration Card.
• Reduced Administrative Burden: Automation reduces paperwork, freeing officials from manual verification and repetitive processing tasks.

Challenges in surveillance-based welfare delivery

• Authentication Failures: Biometric mismatches due to age, disability or network issues exclude genuine beneficiaries from essential services.
• Digital Divide: Poor connectivity and low digital literacy hamper access to app-based welfare verification systems.
  Eg: Connectivity failures causing MGNREGA attendance apps to malfunction in rural areas.
• Privacy Vulnerabilities: Centralised data collection expands risks of surveillance, profiling and potential misuse of personal information.
• Exclusion of Migrants: Rigid address-linked systems marginalise migrants who cannot regularly update IDs or access technology.
• Over-Monitoring Labour: Apps demanding real-time attendance or geotagged evidence can criminalise workers instead of improving welfare.

Solutions to address these challenges

• Multi-Mode Verification: Offer OTP-based, offline, and human-supervised authentication to prevent exclusion due to biometric or connectivity failures.
• Strengthen Data Protection: Implement strict purpose-limitation, decentralised storage, and explicit consent norms to avoid surveillance creep.
  Eg: Robust enforcement of the Digital Personal Data Protection Act, 2023.
• Improve Connectivity: Expand broadband, community digital centres and mobile connectivity to minimise digital divide-based exclusions.
  Eg: BharatNet expansion as core to rural welfare digitisation.
• Inclusive Design: Human-centred design must integrate needs of elderly, migrants, persons with disabilities and remote populations.
• Strengthen Grievance Systems: Establish accessible, fast grievance redressal for wrongful exclusions and technology failures.
  

Conclusion

Surveillance-based welfare applications can strengthen transparency and efficiency, yet their rigid, data-driven design risks amplifying exclusion. Ensuring flexible authentication, stronger privacy protections, inclusive design and responsive grievance systems can transform digital welfare from a high-risk mechanism of control into an instrument of equitable state support.

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