This study looks at why many AI projects in rural India have not met expectations, even though there has been a lot of investment and promise. It examines the social and technical challenges, infrastructure gaps, and ethical issues that make it hard to use AI successfully in areas such as agriculture, healthcare, education, and local government. Key challenges identified include inadequate digital infrastructure, limited data representativeness of local populations, linguistic and cultural barriers, and low levels of digital literacy among rural users. Additionally, the opacity of AI decision processes has led to mistrust, inappropriate recommendations, and unintended negative consequences for underserved communities. It highlights a significant gap between how AI is designed and the realities of rural communities. The study argues that without inclusive design practices, localized data systems, and policies that tackle economic inequalities, AI initiatives may worsen existing disparities instead of improving them. Finally, the paper offers guidelines for responsible and context-aware AI use in rural India. It stresses the importance of working together with local stakeholders, managing data ethically, and investing in infrastructure. The findings highlight the urgent need for AI systems that are not only technically strong but also socially inclusive and fair, reflecting the experiences of rural populations.
A lack of localized and high-quality datasets, algorithmic bias, AI designs that do not consider context, usability challenges in resource-limited environments, gaps in public policy and governance, regulatory hurdles, ethical concerns surrounding AI, information and communication technology for development.
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