India’s Digital Census Reveals Caste, Digital Divide, and Colonial Legacy in 1.4B Data Points

Adivasi and Dalit communities have historically rejected state enumeration, viewing it as a colonial imposition that disrupts their oral traditions, land tenure systems, and communal identities. The digital census’s caste enumeration echoes pre-independence 'caste schedules' used to justify discrimination, while indigenous knowledge systems like the Gond tradition of *gondvana* (oral genealogies) are rendered invisible. Indigenous data sovereignty movements, such as those led by the *Adivasi Gothra Mahasabha*, argue for alternative data frameworks that center community consent and decolonial epistemologies.

Census-taking in India dates to 1872 under British rule, when caste was first systematically recorded to justify colonial divide-and-rule policies. Post-independence, the 1951 census excluded caste to avoid reinforcing hierarchies, but the current inclusion reflects a resurgence of identity politics tied to neoliberal governance. The digital turn mirrors earlier technocratic solutions (e.g., the 1991 Mandal Commission’s OBC quotas) that instrumentalized caste data without addressing structural inequities. Historical parallels show how enumeration often serves state control rather than social justice.

In Rwanda, post-genocide censuses avoided ethnic categorization to prevent reification of divisions, opting instead for geographic and socioeconomic data. Brazil’s IBGE has experimented with 'color' categories that allow self-identification, but critics argue this still essentializes race. In India, the digital census’s caste enumeration risks repeating global patterns where identity data is collected for administrative convenience but rarely for the benefit of the identified groups. Cross-culturally, the tension between data utility and identity erasure highlights the need for participatory design.

Digital census methods introduce sampling biases, as internet penetration in India is only 52% (with stark rural-urban divides) and caste data collection lacks standardized definitions across states. The inclusion of caste data raises ethical concerns about privacy and potential misuse, as seen in historical cases where caste lists fueled violence. Methodologically, the census’s digital-first approach prioritizes speed over accuracy, risking misclassification of marginalized groups. Scientific literature on data justice (e.g., D’Ignazio & Klein’s *Data Feminism*) underscores the need for transparent, accountable data systems.

For many Adivasi communities, the census disrupts sacred landscapes and oral traditions, as enumeration is seen as a violation of *dharmshala* (cosmic order). Artists like Gulammohammed Sheikh have critiqued census categories as tools of state assimilation, while poets like Namdeo Dhasal used caste data to expose systemic oppression. Spiritual traditions like Buddhism and Jainism reject caste hierarchies, yet the census’s digital framework risks erasing these epistemologies. The act of counting itself is spiritually fraught in traditions that value qualitative over quantitative knowledge.

If unchecked, the digital census could entrench a surveillance state where caste data is monetized by private firms or weaponized by political actors, as seen in Myanmar’s Rohingya crisis. Scenario modeling suggests that without safeguards, caste enumeration could deepen social fragmentation, particularly in states like Uttar Pradesh where caste-based violence is rising. Conversely, a participatory census design—centered on community data trusts—could model a decolonial alternative to state enumeration. Future systems must prioritize data sovereignty and interoperability with indigenous knowledge frameworks.

Dalit and Adivasi activists warn that caste enumeration will be used to justify quotas without addressing root causes of discrimination, such as land dispossession and police violence. Transgender communities are excluded from binary gender categories, risking further marginalization in policy. Rural women, who face intersecting caste and gender oppressions, are least likely to be counted accurately due to digital access gaps. The census’s top-down design silences those it purports to represent, reproducing the very exclusions it claims to measure.