AI face checks added to rural jobs scheme attendance under VB-G RAM G

The brief in 5 cards

1. Note1 / 5

   WHY IN NEWS?

   • On 23 October 2026, Union Rural Development Minister Shivraj Singh Chouhan announced an upgraded National Mobile Monitoring System (NMMS).
   • It adds AI-based checks to curb fraudulent attendance under VB-G RAM G.
   • VB-G RAM G, the Viksit Bharat–Guarantee for Rozgar and Ajeevika Mission (Gramin), took effect on 1 July 2026, replacing MGNREGA.
   • It guarantees 125 days of work a year to rural households willing to do unskilled manual labour.
2. Note2 / 5

   WHAT THE UPGRADE DOES

   • Face verification: Matches the worker's face to their record.
   • Liveness detection: Tells a live person from a photograph or dummy image.
   • Photo de-duplication: Flags the same photo used across different muster rolls.
   • Anti-tampering: Detects cloned apps and doctored records.
   • Payment hold: Suspicious wage lists and fund transfer orders are held for checks.
   • Offline option: Allows attendance in poor-connectivity areas, with exceptions for the elderly and persons with disabilities.
3. Note3 / 5

   KEY CONCEPTS

   1. What is the NMMS?

   It is a mobile app that records worksite attendance. Since January 2023, it has required two time-stamped, geo-tagged photographs of workers each day.

   2. What is a muster roll?

   It is the official attendance register at a worksite. Wages are paid on its basis, so fake entries mean stolen wages.

   3. What is a "ghost worker"?

   It is a name on the rolls for someone who does not work, or does not exist. Wages are drawn in their name.

   4. What is a social audit?

   It is a public review by the Gram Sabha of records, payments and completed works. It lets villagers check the scheme themselves.

4. Note4 / 5

   THE CONCERNS

   This card sets out documented positions. It does not adjudicate between them.

   • Reported irregularities: Opposition leaders and investigations have alleged attendance manipulation in several States.
   • Digital exclusion: Weak networks and few devices can shut out workers in remote areas.
   • Wrongful rejections: Poor lighting, ageing faces and system errors may reject genuine workers.
   • Privacy: Facial data needs secure storage and limited use.
   • Delayed wages: Held payments can hurt honest workers if checks take long.
   • Limits: Face checks prove who was present, not whether work was done well.
5. Note5 / 5

   WAY FORWARD

   These are suggested measures, not approved policy.

   • Keep humans in the loop: Treat AI flags as prompts for checks, with a quick appeal route.
   • Publish error rates and audit the algorithms independently.
   • Strengthen social audits so Gram Sabhas verify rolls and assets.
   • Pay on time: Set deadlines to clear flagged cases and release genuine wages.
   • Link records: Combine attendance with electronic measurement books and geo-tagged assets.

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