Generative AI systems are trained on cultural content without consent from or consideration of heritage art communities who created it. In this paper, we examine how Generative AI intersects with traditional art through a case study of West Bengal’s Chitrakar painter-singer-storyteller community. Based on interviews and surveys with 10 Chitrakar artists, we find that significant AI literacy gaps exist within this heritage community, yet artists demonstrate informed resistance to AI integration based on awareness of ethical concerns and cultural appropriation. Our analysis shows that current AI models fail to authentically capture the cultural symbolism and technical depth of Chitrakar art. We contend that generic AI solutions are inadequate and hence propose community-centered interventions which ensure the continued vitality of heritage art.

Little research examines how ethnographers perceive Responsible AI. This paper investigates Indian ethnographers’ knowledge, critiques, and envisioned roles through a qualitative study with 20 participants. Findings reveal knowledge heterogeneity, with most having indirect engagement through seminars while few demonstrated direct expertise through formal training. Drawing on field experiences, participants critique dominant Responsible AI frameworks as contextually misaligned with India’s social realities, failing to address caste, class, and regional hierarchies. Through concrete examples, they demonstrate how helpfulness and harmlessness logics operate without power analysis or cultural grounding, such as welfare metrics missing household dynamics and benchmarks excluding marginalized languages. Participants advocate situated approaches co-created with affected communities, proposing methodological innovations including ethnographic metadata in model cards, field-conditioned evaluation, and interpretive roles in reinforcement learning for human feedback workflows.