What TECH could do right

1. Smarter Land Mapping: AI can scan satellite images to identify land boundaries and encroachments, helping tribal people prove their land claims.

In Odisha, AI digitized land records using satellite mapping. It did not help initially because a lot of tribal land went unaccounted for, but when local workers reached out, tribal communities verified data themselves.

2. Faster Legal Help: AI tools can sort through old legal documents to speed up Forest Rights Act (FRA) claims, especially in places with big backlogs.

In Maharashtra, satellite-assisted forest mapping sped up some land claim processes. But AI-generated data was not enough and ground checks and oral testimony were still critical. Still, it did speed up the process, and with the backlog Indian courts have, was still impactful.

3. Digitized Land Records: Programs like the Digital India Land Records Modernisation Programme (DILRMP) aim to put every land record online, making them more transparent and harder to manipulate.

WHERE TECH could go wrong

1. AI systems rely on lots of data including satellite images, land records, and even biometric info. But tribal communities often don’t give consent for how that data is collected or used.

2. If an AI system is trained on flawed or incomplete land records, it can misclassify community lands as “vacant” or “public”, opening them up to mining or tourism.

3. Drones and satellite monitoring may track tribal forest use without transparency or accountability which would lead to criminalization of traditional practices.

4. AI doesn’t understand sacred groves, oral history, or shared ancestral lands unless someone tells it, and these things are extremely important when discussing the relationship between India’s tribal population and their land When tech overrides traditional systems, identity is at risk.

Recommendations

Making TECH Work for Indigenous Communities

  • Ensure free, prior, and informed consent (FPIC) before using AI tools in tribal areas or collecting data.

  • Involve tribal communities in the design, testing, and implementation of AI systems that affect them.

  • Recognize and protect data sovereignty. Tribal communities should at least know, if not control how their data is collected, stored, and shared.

  • Incorporate oral histories and traditional knowledge into land and cultural mapping tools and not just government records.

  • Audit algorithms regularly for cultural bias, especially those used in land classification, policing, or resource allocation.

  • Bridge the digital divide through tech training and infrastructure investment in tribal and Adivasi regions.

  • Create Indigenous-led oversight bodies to monitor AI development and flag risks early.

  • Align AI initiatives with existing rights laws like the Forest Rights Act (FRA) and PESA in India.