Dr. Whose access, whose agenda? Artificial intelligence/machine learning expansion in unequal societies: the case of India
Abstract This paper presents an original case study using policy analysis and practitioner-informed experiential insights to analyze the impact of AI/ML technologies to enhance the lives of resource-poor communities of India and create positive social impact. Against the backdrop of a global agenda, significantly shaped by Gates Foundation’s network diplomacy (Puyvallée et al., 2025) to deploy large language models (LLMs) and the significant national revenue imperatives, the author critically examines specific cross-sectoral examples and the influence of various stakeholders on science diplomacy goals in India. To this end, the paper investigates the current regulatory and legal frameworks and approaches in India enabling technological innovation while simultaneously generating tensions with fundamental democratic values. The article concludes by assessing the implications of expanding access to AI/ML technologies in a society marked by structural inequalities and institutional failures at the regional level. We explore pathways where effective science diplomacy can play a critical role in driving the growth of AI/ML balancing it with principles of a democratic society.
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Kalyanakrishnan, S., Panicker, R. A., Natarajan, S., & Rao, S. (2018, December). Opportunities and challenges for artificial intelligence in India. In Proceedings of the 2018 AAAI/ACM conference on AI, Ethics, and Society (pp. 164-170).
Kapania, S., Siy, O., Clapper, G., Meena S. P., & Sambasivan, N. (2022). “Because AI is 100% right and safe”: User attitudes and sources of AI authority in India. In Proceedings of the CHI Conference on Human Factors in Computing Systems (CHI ’22) (pp. 1–18). ACM. https://doi.org/10.1145/3491102.3517533
Lourenço, V., Mann, P., Guimarães, A., Paes, A., & de Oliveira, D. (2018). Towards safer (smart) cities: Discovering urban crime patterns using logic-based relational machine learning. In 2018 International Joint Conference on Neural Networks (IJCNN) (pp. 1–8). IEEE. https://doi.org/10.1109/IJCNN.2018.8489374
Marda, V. (2018). Artificial intelligence policy in India: a framework for engaging the limits of data-driven decision-making. Phil. Trans. R. Soc. A, 376, 20180087. https://doi.org/10.1098/rsta.2018.0087
Ministry of Electronics and Information Technology. (2025). MeitY launches AIKosha and GPU compute portal under IndiaAI Mission to enable AI innovation ecosystem [Press release]. https://moneycontrol.com/technology/meity-to-launch-indiaai-datasets-platform-gpu-access-portal-today-article-12957715.html
Mohanty, P., & Sahu, G. (2024). Regulating AI in India: Between innovation and control. Economic & Political Weekly, 59(12).
Radhakrishnan, R. (2021). Experiments with social good: Feminist critiques of artificial intelligence in healthcare in India. Catalyst: Feminism, Theory, Technoscience, 7(2).
Reddy, B., Pai, N., Panicker, R., Sahu, S. S., & Krishna, S. (2023). A pathway to AI governance (Discussion Document No. 2023‑14). The Takshashila Institution.
Sahoo, N. R., Kulkarni, P. P., Asad, N., Ahmad, A., Goyal, T., Garimella, A., & Bhattacharyya, P. (2024). IndiBias: A Benchmark Dataset to Measure Social Biases in Language Models for Indian Context. arXiv preprint: https://doi.org/10.48550/arXiv.2403.20147
Stoker, G., Smith, G., & Maloney, W. (2004). Building social capital in city politics: scope and limitations at interorganisational level. Polit. Stud. 52, 508–530. https://doi.org/10.1111/j.1467-9248.2004.00493.x
Aneja, U., Gupta, A., Jain, A., & John, S. (2024). From Code to Consequence: Interrogating Gender Biases in LLMs within the Indian Context. Digital Futures Lab Publications/Reports.
Artificial Intelligence Task Force. (2019). Report of the Artificial Intelligence Task Force. Government of India.
Bhalla, N., Brooks, L., & Leach, T. (2024). Ensuring a ‘Responsible’ AI future in India: RRI as an approach for identifying the ethical challenges from an Indian perspective. AI and Ethics, 4(4), 1409-1422.
Bhattacharya, P., Ghosh, S., Bose, S., Ghanghor, N., Pandey, A., Bhandari, M., & Kunchukuttan, A. (2021). IndicCorp: A multilingual corpus for Indian languages. AI4Bharat. https://github.com/AI4Bharat/indicnlp_corpus
Bill & Melinda Gates Foundation (2023, December 11). Artificial intelligence: Our principles for development and use. https://www.gatesfoundation.org/ideas/articles/artificial-intelligence-ai-development-principles
Breen, E., & Kumar, R. (2023). Private foundations and their global health grant-making patterns: A rapid analysis of the Rockefeller Foundation, Wellcome Trust, and Bill and Melinda Gates Foundation. Global Policy Forum Europe e.V. https://www.globalpolicy.org/en/data-working-paper-private-foundations-and-their-global-health-grant-making-patterns
Cath, C. (2018). Governing artificial intelligence: ethical, legal and technical opportunities and challenges. Phil. Trans. R. Soc. A., 376: 20180080. https://doi.org/10.1098/rsta.2018.0080
Choudhary, P., & Sarthy, P. (2022). Transforming Cities for Sustainability: Role of Standards on Smart City. 2nd International Conference on Power Electronics and IoT Applications in Renewable Energy and Its Control, PARC 2022. https://doi.org/10.1109/PARC52418.2022.9726674
de Bengy Puyvallée, A., Storeng, K. T., & Rushton, S., (2025, April 24). The Gates Foundation's network diplomacy in European donor countries. Global Health, 21(1): 22. PMID: 40275294. PMCID: PMC12020045. https://doi.org/10.1186/s12992-025-01112-9
Fedoroff, N. V. (2009, January 9). Science diplomacy in the 21st century. Cell, 136(1): 9-11. PMID: 19135879. https://doi.org/10.1016/j.cell.2008.12.030
Joshi, P., Santy, S., Budhiraja, A., Bali, K., & Choudhury, M. (2020). The state and fate of linguistic diversity and inclusion in the NLP world. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (pp. 6282–6293). Association for Computational Linguistics.
Kalyanakrishnan, S., Panicker, R. A., Natarajan, S., & Rao, S. (2018, December). Opportunities and challenges for artificial intelligence in India. In Proceedings of the 2018 AAAI/ACM conference on AI, Ethics, and Society (pp. 164-170).
Kapania, S., Siy, O., Clapper, G., Meena S. P., & Sambasivan, N. (2022). “Because AI is 100% right and safe”: User attitudes and sources of AI authority in India. In Proceedings of the CHI Conference on Human Factors in Computing Systems (CHI ’22) (pp. 1–18). ACM. https://doi.org/10.1145/3491102.3517533
Lourenço, V., Mann, P., Guimarães, A., Paes, A., & de Oliveira, D. (2018). Towards safer (smart) cities: Discovering urban crime patterns using logic-based relational machine learning. In 2018 International Joint Conference on Neural Networks (IJCNN) (pp. 1–8). IEEE. https://doi.org/10.1109/IJCNN.2018.8489374
Marda, V. (2018). Artificial intelligence policy in India: a framework for engaging the limits of data-driven decision-making. Phil. Trans. R. Soc. A, 376, 20180087. https://doi.org/10.1098/rsta.2018.0087
Ministry of Electronics and Information Technology. (2025). MeitY launches AIKosha and GPU compute portal under IndiaAI Mission to enable AI innovation ecosystem [Press release]. https://moneycontrol.com/technology/meity-to-launch-indiaai-datasets-platform-gpu-access-portal-today-article-12957715.html
Mohanty, P., & Sahu, G. (2024). Regulating AI in India: Between innovation and control. Economic & Political Weekly, 59(12).
Radhakrishnan, R. (2021). Experiments with social good: Feminist critiques of artificial intelligence in healthcare in India. Catalyst: Feminism, Theory, Technoscience, 7(2).
Reddy, B., Pai, N., Panicker, R., Sahu, S. S., & Krishna, S. (2023). A pathway to AI governance (Discussion Document No. 2023‑14). The Takshashila Institution.
Sahoo, N. R., Kulkarni, P. P., Asad, N., Ahmad, A., Goyal, T., Garimella, A., & Bhattacharyya, P. (2024). IndiBias: A Benchmark Dataset to Measure Social Biases in Language Models for Indian Context. arXiv preprint: https://doi.org/10.48550/arXiv.2403.20147
Stoker, G., Smith, G., & Maloney, W. (2004). Building social capital in city politics: scope and limitations at interorganisational level. Polit. Stud. 52, 508–530. https://doi.org/10.1111/j.1467-9248.2004.00493.x
Gandhi Kanakia, J. (2025). Dr. Whose access, whose agenda? Artificial intelligence/machine learning expansion in unequal societies: the case of India. Artefactos. Philosophical Studies on Science and Technology, 14, e32666. https://doi.org/10.14201/art2025.32666
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