India’s AI ‘Third Way’: Bridging Structural Divides Through People-Centric, Planetary-Conscious Governance

India’s indigenous Adivasi communities and Dalit activists have long resisted data extraction, framing digital rights as part of land and bodily sovereignty. Traditional knowledge systems, such as Ayurveda’s emphasis on holistic, non-reductive data (e.g., prakriti-based health records), offer alternatives to the atomized, profit-driven datasets fueling AI. However, these perspectives are systematically excluded from policy circles dominated by techno-elites who view data as a resource to be commodified, not a sacred or communal trust.

India’s AI ‘third way’ echoes colonial-era patterns where the state and foreign capital co-opted local innovation for global markets, from the East India Company’s textile looms to IBM’s early dominance in computing. The 1970s ‘License Raj’ and 1991 liberalization created a bifurcated tech sector—state-backed champions like ISRO alongside Silicon Valley’s outsourcing hubs—laying the groundwork for today’s AI infrastructure. Historical precedents like the Green Revolution show how technocratic solutions imposed from above often deepen inequality, a risk echoed in AI’s top-down deployment.

In Latin America, movements like Brazil’s *Marco Civil da Internet* emphasize digital rights as human rights, contrasting with India’s market-friendly approach. African scholars argue that AI governance must prioritize communal well-being over GDP growth, a principle reflected in Rwanda’s AI-driven healthcare but absent in India’s techno-nationalist framing. The ‘third way’ risks becoming a neocolonial bridge, where Global South nations mediate between Western tech giants and authoritarian AI models, rather than asserting autonomous pathways.

Peer-reviewed studies show that India’s AI ecosystem is heavily reliant on imported hardware (e.g., NVIDIA chips) and foreign-trained talent, undermining claims of ‘sovereignty.’ Research on algorithmic bias in India reveals systemic discrimination against marginalised castes and Muslims in predictive policing and welfare algorithms. The ‘people-centric’ narrative lacks empirical grounding in how AI actually impacts labor, environment, or democracy, with most policy documents citing industry-friendly think tanks rather than independent audits.

Indian classical arts, such as Kathakali’s emphasis on embodied knowledge, challenge the disembodied, data-driven AI models being promoted. Spiritual traditions like Advaita Vedanta’s non-dualism critique the reductionist logic of AI, while Sufi poetry’s focus on interconnectedness offers a metaphor for decentralized, human-centered tech. However, these perspectives are sidelined in favor of a techno-utopian aesthetic that equates progress with Silicon Valley’s aesthetic of sleek interfaces and ‘disruptive’ innovation.

Scenario modeling suggests India’s ‘third way’ could either accelerate extractive AI growth or, if reoriented, become a model for equitable, low-impact AI development. A 2023 report by the *AI for Good* initiative warns that without structural reforms, India’s AI expansion will exacerbate water scarcity, e-waste, and digital divides by 2030. Alternative futures include community-owned AI cooperatives or ‘AI commons’ that prioritize ecological and social well-being over GDP metrics, but these require deliberate policy shifts away from current trajectories.

Dalit and Adivasi activists have documented how AI systems in India replicate caste and tribal discrimination, from facial recognition errors to algorithmic welfare exclusion. Women in gig economies, such as delivery workers subject to opaque AI-driven performance metrics, face heightened precarity under the ‘people-centric’ narrative. Migrant laborers in tech hubs like Bengaluru are displaced by AI-driven urban development, yet their voices are absent from policy debates that frame AI as a unifying force.