Mentor’s Comment

Surveillance-driven governance is expanding rapidly across India’s welfare programmes. Mobile apps promising “real-time monitoring” and “perfect accountability” are being deployed at scale, often without adequate evidence, capacity, or safeguards. This article critically evaluates the growing reliance on tech fixes in welfare delivery. For UPSC aspirants, it offers an analytical understanding of digital governance, state capacity, accountability frameworks, and ethical concerns, key themes across GS-2 and GS-4.

Introduction

Digital tools entered India’s welfare architecture as instruments to modernise attendance, prevent leakages, and strengthen accountability. Over time, however, their use expanded without evaluating field conditions such as connectivity, device access, literacy, and administrative capacity. Surveillance apps have produced limited gains, created new exclusion risks, and shifted the burden of accountability onto frontline workers instead of programme designers and administrators.

Why in the news

Welfare programmes across India are increasingly mandating surveillance apps, ranging from biometric attendance to compulsory photo uploads, to improve accountability. But a series of recent failures, especially in schemes like the National Mobile Monitoring System (NMMS) and the Poshan Tracker, has exposed deep flaws. For the first time, governments are publicly acknowledging that these apps are producing unreliable data, penalising genuine beneficiaries, and overburdening frontline workers.

How did biometric attendance become a dominant tool in welfare programmes?

1. Biometric punctuality enforcement: Introduced to ensure staff attendance; absenteeism led governments to mandate digital attendance, even threatening punitive action. Example: Block in Uttarakhand where nurses faced punishments for late biometric attendance.
2. Competing administrative tasks: Conscientious officials stayed back late to complete computerised work, leading to poor next-day biometric compliance.
3. Impact on health workers: In Rajasthan, RCT evidence showed biometric attendance increased absenteeism, not punctuality.
4. MGNREGA experience: Wage expenditure tied to digital attendance meant workers paid for tasks they did not perform if supervisors manipulated records.

Why did the National Mobile Monitoring System (NMMS) generate controversy?

1. Mandatory photo uploads: Required two geotagged photos daily; failure resulted in wages withheld.
2. Unrealistic conditions: Poor connectivity in remote areas made uploads impossible.
3. Limited deterrence of fraud: The app could not confirm whether workers were present all day; supervisors were still able to manipulate attendance.
4. Excessive burden on workers: Workers anxious about upload deadlines; many were forced to return to worksites simply to capture photos.

How did the Poshan Tracker create disruptions in nutrition schemes?

1. Mandatory recognition technology: Ministry required Face Recognition Technology (FRT) for THR pack distribution to children and mothers.
2. Connectivity problems: Anganwadi worker in Haryana, crowd waiting; app warning: “those who want to eat will continue”, meaning refusal impossible.
3. Risk of exclusion: Adivasi worker unable to upload photos; THR packs denied to her centre’s beneficiaries.
4. Extra documentation: Ministry insisted FRT photos must match recorded photographs, adding further layers of control.

How did ration distribution apps worsen inclusion errors for vulnerable households?

1. App-based authentication: Some States required biometric or photograph-based verification for the full ration quota.
2. Penalties for errors: In Jharkhand, uploaded photo mismatch led to partial ration denial.
3. Burden on elderly/disabled beneficiaries: Those unable to stand for photographs or travel to ration shops lost access entirely.

Do tech fixes improve accountability in welfare implementation?

1. Accountability diversion: Apps target frontline workers (anganwadi workers, nurses, teachers) instead of programme designers who control budgets and logistics.
2. Narrow definition of accountability: Focus limited to procedural compliance rather than service quality.
3. Over-reliance on automation: Governments assume apps can “prove” honesty or dishonesty; instead, structural gaps remain untouched.
4. Manipulation persists: Despite apps, fraud, delays, and ghost entries continue, because the administrative ecosystem, not workers, drives corruption patterns.

Limited effect of tech surveillance

1. User rejection: Nurses in several states stopped using apps mandated by NHM due to technical and workload issues.
2. False confidence in data: Administrators felt the ANA tool provided proof of malnutrition despite underlying measurement problems.
3. Infrastructure mismatch: Apps needed smartphones, servers, data connectivity, conditions often absent in rural welfare ecosystems.
4. Shifting blame: When NMMS and Poshan Tracker failed, ministries blamed “misuse” instead of app design flaws.

Accountability Without Capacity: A Flawed Approach

1. Fragmented accountability: Failures frequently attributed to workers; rarely to poor programme design.
2. Blame-shifting: Ministries argued NMMS failures were due to workers manipulating apps.
3. Overproduction of technology: Industries push surveillance apps and governments adopt them without field-testing.
4. Cost to welfare: Data obsession overshadows quality of service delivery, including nutrition, health outreach, and ration reliability.

Conclusion

Surveillance apps in welfare promise transparency but frequently deliver exclusion, burden frontline workers, and create a false sense of accountability. The article shows that technological solutions, when applied without understanding field realities, act like “snake oil”, seductive yet ineffective. Real accountability requires strengthening administrative capacity, improving worker conditions, and focusing on welfare outcomes rather than digital compliance rituals.

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