Abstract
Contemporary generative AI tools exhibit systematic biases rooted in their Anglo-American development context and training data, prioritizing Global North perspectives in design and implementation. Drawing on jugaad—an Indian approach to resourceful problem-solving—this paper critically evaluates how AI solutions disconnected from local contexts perpetuate technological Through a mixed-methods study involving 52 participants across workshops, systematic AI testing with over 300 images, and expert discussions, the authors compare human and AI responses to identical problem-solving scenarios. Our findings reveal stark differences: 100% of human solutions considered environmental and cultural contexts while 90% of AI solutions ignored these factors. Human participants demonstrated contextual awareness and resourceful innovation characteristic of jugaad principles, while AI tools generated standardized responses disconnected from local practices. With the hypothesis that generative AI enables mono cultural technological colonialism by imposing Global North solutions onto diverse cultural contexts, this research contributes to responsible AI development by identifying three critical patterns: AI's perpetuation of power hierarchies, homogenization of problem-solving approaches, and creation of cultural-technological divides that lead to "Global South thought alienation." This study provides design considerations for human-centered AI development, with the provocation that AI can and should center users lived experience to culturally responsive solutions. This paper demonstrates how current AI systems systematically exclude cultural and native innovation practices, offering specific recommendations for integrating diverse knowledge systems into AI development. This paper addresses the critical question: How might responsible AI development move beyond monolithic approaches to embrace regional cultures, practices, and behaviors in human-centered design?
Keywords
Cultural bias in AI; Jugaad innovation; Human-Centered AI; Technological colonialism
DOI
https://doi.org/10.21606/iasdr.2025.537
Citation
Rathore, N.S.,and Thakkar, S.(2025) From Jugaad to Generative AI: A Framework for Culturally Responsive Human-Centered AI, in Chang, C.-Y., and Hsu, Y. (eds.), IASDR 2025: Design Next, 02-05 December, Taiwan. https://doi.org/10.21606/iasdr.2025.537
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
Conference Track
Track 4 - Human-Centered AI
From Jugaad to Generative AI: A Framework for Culturally Responsive Human-Centered AI
Contemporary generative AI tools exhibit systematic biases rooted in their Anglo-American development context and training data, prioritizing Global North perspectives in design and implementation. Drawing on jugaad—an Indian approach to resourceful problem-solving—this paper critically evaluates how AI solutions disconnected from local contexts perpetuate technological Through a mixed-methods study involving 52 participants across workshops, systematic AI testing with over 300 images, and expert discussions, the authors compare human and AI responses to identical problem-solving scenarios. Our findings reveal stark differences: 100% of human solutions considered environmental and cultural contexts while 90% of AI solutions ignored these factors. Human participants demonstrated contextual awareness and resourceful innovation characteristic of jugaad principles, while AI tools generated standardized responses disconnected from local practices. With the hypothesis that generative AI enables mono cultural technological colonialism by imposing Global North solutions onto diverse cultural contexts, this research contributes to responsible AI development by identifying three critical patterns: AI's perpetuation of power hierarchies, homogenization of problem-solving approaches, and creation of cultural-technological divides that lead to "Global South thought alienation." This study provides design considerations for human-centered AI development, with the provocation that AI can and should center users lived experience to culturally responsive solutions. This paper demonstrates how current AI systems systematically exclude cultural and native innovation practices, offering specific recommendations for integrating diverse knowledge systems into AI development. This paper addresses the critical question: How might responsible AI development move beyond monolithic approaches to embrace regional cultures, practices, and behaviors in human-centered design?