Rethinking Indic AI from a Lens of Cultural Heritage Preservation

2026-07-07

AI Summary

This study addresses the risk that widespread AI adoption across the Indian subcontinent may accelerate linguistic and cultural homogenization, thereby endangering low-resource languages and the cultural practices and worldviews they embody. The paper systematically examines the unique challenges posed by Indian languages—including their morphological complexity, script diversity, grammatical variation, and rich dialectal landscapes—and traces the historical trajectory of Indic NLP research. It critically evaluates current foundation models, highlighting their limitations in handling resource scarcity and inadequate cultural representation. In response, the work proposes a novel “culturally aware” paradigm grounded in hermeneutic reasoning, advocating for AI systems capable of understanding and generating language that is contextually appropriate to local cultural norms. Integrating technical analysis, model assessment, and language resource development, this research offers a theoretical framework and actionable pathways toward building linguistically equitable and culturally sensitive next-generation Indic foundation models.

Abstract

As Artificial Intelligence (AI) makes inroads into different parts of the Indian subcontinent, there is significant interest in studying how AI impacts the linguistic and cultural foundations of this civilization. AI is seen as a ''double-edged sword'' where on the one hand, it can enable access and inclusion for a large population, on the other, it can homogenize worldviews and exclude underrepresented languages and worldviews. In this paper, we try to characterize this problem by addressing the extensive characteristic nature of Indian linguistics and the way they closely connect to cultural practices and worldview. We then perform a longitudinal survey of how Natural Language Processing (NLP) techniques have evolved in this space, tracing the historical development of Indic NLP, covering key milestones, methodological shifts, and resource creation efforts. In addition, the paper also examines the structural and sociolinguistic characteristics of Indian languages, such as rich morphology, complex scripts and grammar rules, diglossia, and large dialectal variation, and explains how these create unique challenges for building AI foundation models. We then discuss the growing role of Indic foundation models and analyze how these models address these long-standing resource and representation gaps. Finally, we propose a research direction called 'Culture Sensing', which re-imagines AI based on hermeneutic reasoning. Culture Sensing aims to address open problems such as ensuring equitable performance across low-resource languages and producing outputs that are culturally meaningful. By bringing together past work, current techniques, and emerging trends, this paper outlines research directions that can guide the next phase of Indic NLP and contribute to the development of more robust and inclusive Indic foundation models.

Problem

• Indic AI
• Cultural Heritage Preservation
• Low-resource Languages
• Linguistic Diversity
• Culturally Meaningful AI

Innovation

• Culture Sensing
• Indic NLP
• hermeneutic reasoning
• foundation models
• low-resource languages