Safety Considerations During Rapid Implementation of A.I. in India
Article Sidebar
Main Article Content
Abstract
India has made A.I. adoption and native A.I. innovation a priority. However, rapid adoption and growth without a safety framework can lead to harm. Although standards to measure safety and reduce biasin A.I. have been researched in the West, these recommendations need to be adapted to address India’s digital landscape. This paper aims to contribute to the growing body of knowledge on the practical implementation of A.I. safety measures across a diverse digital landscape. In the paper, we examine various areas where A.I. can cause harm. First, we examine biases that enter code and algorithms. Second, applying Young’s five faces of oppression, we identify different forms of oppression in A.I. Third, we examine the Indian digital landscape to understand why its diversity requires a tailored safety framework and unique guardrails to mitigate bias. The paper concludes by recommending pathways that will enable Indian innovators to create Responsible Artificial Intelligence and applications that will adhere to safety standards while reducing instances of exploitation.
Downloads
Article Details
Section
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
How to Cite
References
Borthakur, S., Agarwal, S., Kaushik, P., Pareek, A., Sarna, A., Satyanarayana, J., Sharma, H., Vohra, S., Jindal, G., Johri, D., Kela, S., Rajdev, P & Chakraborthy, D. (2025, April 29). Future farming in India: a playbook for scaling artificial intelligence in agriculture. weforum.org. https://reports.weforum.org/docs/WEF_Future_Farming_in_India_A_Playbook_for_Scaling_Artificial_Intelligence_in_Agriculture_2025.pdf
Chauhan, C. P. S. (2008). Education and caste in India. Asia Pacific Journal of Education, 28(3), 217–234.
DOI: 10.1080/02188790802267332. Works remain significant; see the declaration
Costanza-Chock, S. (2018, July 16). Design justice, A.I., and escape from the matrix of domination · Journal of Design and Science. Retrieved April 29, 2021, from https://jods.mitpress.mit.edu/pub/costanza-chock/release/4
De Carvalho, L. L., Buchbinder, F., Da Hora, D. A., Ferreira, V. B., & De Carvalho, D. M. (2026a). Social participation as an ethical criterion for AI governance in Public Administration. AI and Ethics, 6(2). DOI: https://doi.org/10.1007/s43681-026-01010-9
Dixit, P. (2019, October 10). India is creating a National facial recognition system, and critics are afraid of what will happen next. Retrieved April 25, 2021, from https://www.buzzfeednews.com/article/pranavdixit/india-is-creating-a-national-facial-recognition-system-and
Dwivedi, J. (2024, September 9). Upload, undress, blackmail: AI “nudify” apps emerge as new weapons of extortion. India Today. https://www.indiatoday.in/fact-check/story/upload-undress-blackmail-ai-nudify-apps-emerge-as-new-weapons-of-extortion-2595198-2024-09-06
Finn, E. (2017). What Algorithms Want. DOI: 10.7551/mitpress/9780262035927.001.0001
Friedman, B., & Nissenbaum, H. (1996). Bias in computer systems. ACM Transactions on Information Systems, 14(3), 330–347. DOI: 10.1145/230538.230561. Works remain significant; see the declaration
Gender gap – mobile for development. (2020). Retrieved April 25, 2021, from https://www.gsma.com/r/gender-gap/
Gulankar, A. (2020, September 14). Over 62% of dropouts in education happen at the school level. Retrieved April 30, 2021, from https://thefederal.com/news/over-62-of-dropouts-in-education-happens-at-school-level/
Hamilton, I. (2018, October 10). Amazon built an AI tool to hire people but had to shut it down because it was discriminating against women. Retrieved April 25, 2021, from https://www.businessinsider.com/amazon-built-ai-to-hire-people-discriminated-against-women-2018-10
Kantayya, S. (Director). (2021, April 5). Coded Bias [Video file]. Retrieved April 26, 2021, from https://www.netflix.com/watch/81328723?trackId=13752289&tctx=0%2C0%2Cc4b1ae5ff1f48b2153fe3241ec13cf7456fc7358%3Ac4b369a00f87a737e50584065deb2cbf6a99d901%2Cc4b1ae5ff1f48b2153fe3241ec13cf7456fc7358%3Ac4b369a00f87a737e50584065deb2cbf6a99d901%2C%2C
Kapania, S., Siy, O., Clapper, G., Meena SP, S. P., & Sambasivan, N. (2022). "Because AI is 100% right and safe": User attitudes and sources of AI authority in India. In CHI Conference on Human Factors in Computing Systems (CHI '22). ACM. DOI: 10.1145/3491102.3517533
Ministry of Electronics & IT. (2025, March 06). India’s AI revolution, a road map to Viksit Bharat [Press release]. https://www.pib.gov.in/PressReleasePage.aspx?PRID=2108810#:~:text=AI%20Innovation%3A%20India%20has%20emerged,in%20AI%20innovation%20and%20adoption.
Ministry of Finance, Government of India. (2025). Economic Survey 2024-25. https://www.indiabudget.gov.in/economicsurvey/doc/echapter.pdf
Mozur, P. (2019, April 14). One month, 500,000 FACE SCANS: How China is using A.I. to profile a minority. Retrieved April 28, 2021, from https://www.nytimes.com/2019/04/14/technology/china-surveillance-artificial-intelligence-racial-profiling.html
Müller, H., & Freytag, J.-C. (2005). Problems, methods, and challenges in comprehensive data cleansing. In Proceedings of the Data Cleaning Workshop at the 31st International Conference on Very Large Data Bases (VLDB). Technical University of Berlin. https://www.researchgate.net/publication/228929938_Problems_methods_and_challenges_in_comprehensive_data_cleansing, works remain significant, see the declaration
Nestor Maslej, Loredana Fattorini, Raymond Perrault, Yolanda Gil, Vanessa Parli, Njenga Kariuki, Emily Capstick, Anka Reuel, Erik Brynjolfsson, John Etchemendy, Katrina Ligett, Terah Lyons, James Manyika, Juan Carlos Niebles, Yoav Shoham, Russell Wald, Tobi Walsh, Armin Hamrah, Lapo Santarlasci, Julia Betts Lotufo, Alexandra Rome, Andrew Shi, Sukrut Oak. “The AI Index 2025 Annual Report,” AI Index Steering Committee, Institute for Human-Centered AI, Stanford University, Stanford, CA, April 2025. https://hai.stanford.edu/assets/files/hai_ai_index_report_2025.pdf
Noble, S. U. (2018). Algorithms of oppression. New York University Press. DOI: 10.1086/703431
Office of the Registrar General and Census Commissioner. (2018). Census of India 2011: Language and mother tongue data tables. Ministry of Home Affairs, Government of India. https://censusindia.gov.in/nada/index.php/catalog/42458
O'Neil, C. (2016). Weapons of math destruction: How big data increases inequality and threatens democracy. Crown Publishing Group. https://www.researchgate.net/publication/314165204_Cathy_O'Neil_Weapons_of_Math_Destruction_How_Big_Data_Increases_Inequality_and_Threatens_Democracy_New_York_Crown_Publishers_2016_272p_Hardcover_26_ISBN_978-0553418811
Rani, U., & Dhir, R. K. (2024). "Chapter 4: AI-enabled business model and human-in-the-loop (deceptive AI): implications for labour". In Handbook of Artificial Intelligence at Work. Cheltenham, UK: Edward Elgar Publishing. Retrieved Nov 3, 2025, from the DOI: https://doi.org/10.4337/9781800889972.00011
Roy J.K. A systematic review of artificial intelligence, machine learning, and environment–social–governance in marketplace lending. Discov Artif Intell (2026). DOI: 10.1007/s44163-026-01399-6
Sambasivan, N., Arnesen, E., Hutchinson, B., Doshi, T., & Prabhakaran, V. (2021, March). Re-imagining algorithmic fairness in India and beyond. In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (pp. 315–328). Association for Computing Machinery. DOI: 10.1145/3442188.3445896
Sambhav, K, Tapasya, Joshi, J. (2024, January 25). In India, an algorithm declares them dead; they have to prove they’re alive. Al Jazeera. https://www.aljazeera.com/economy/2024/1/25/in-india-an-algorithm-declares-them-dead-they-have-to-prove-theyre
Sathe, G. (2019, September 02). Little brother Is Watching: India is overrun by Cheap Snooping Systems. Retrieved April 25, 2021, from https://www.huffpost.com/archive/in/entry/face-recognition-india-ncrb-police-digiyatra_in_5d68fa22e4b02bc6bb37fa6e
Sheth, N. (2025, August 25). The silence around India's AI crisis is deafening. The Deccan Herald. https://www.deccanherald.com/opinion/the-silence-around-indias-ai-crisis-is-deafening-3695702
Singh, A., & Sujay, B. M. (2024a, April 7). Tangled web: senior citizens navigate a complex digital world. Deccan Herald. Retrieved November 12, 2025, from
Smith, G., & Rustagi, I. (2021). When good algorithms Go Sexist: Why and how to ADVANCE AI gender Equity (SSIR). Retrieved April 25, 2021, from
Starke, C., Baleis, J., Keller, B., & Marcinkowski, F. (2022). Fairness perceptions of algorithmic decision-making: A systematic review of the empirical literature. Big Data & Society, 9(2).
DOI: 10.1177/20539517221115189
Sundblad, W. (2018, October 18). Data is the foundation for artificial intelligence and machine learning. Retrieved March 08, 2021, from
Udechukwu, L. M. (2026). Culturally contextual datasheets: A framework for embedding cultural reflexivity in Global AI governance. AI and Ethics, 6(2). DOI: 10.1007/s43681-026-01069-4
West, D. (2019, October 25). What is artificial intelligence? Retrieved April 30, 2021, from https://www.brookings.edu/research/what-is-artificial-intelligence/
Williams, R. (1977). Marxism and literature. Oxford University Press. https://global.oup.com/academic/product/marxism-and-literature-9780198760610?cc=in&lang=en&, works remain significant; see the declaration
Young, I. M. (1990). Five faces of oppression. In Justice and the politics of difference (pp. 40-65). Princeton, NJ: Princeton Univ. Pr. https://www.researchgate.net/publication/275985054_Justice_and_the_Politics_of_Difference_By_Iris_Marion_Young_Princeton_Princeton_University_Press_1990_286p_4500_cloth_1095_paper. Works remain significant; see the declaration
Zhong, T., Yang, Z., Liu, Z., Zhang, R., Liu, Y., Sun, H., Pan, Y., Li, Y., Zhou, Y., Jiang, H., Chen, J., & Liu, T. (2024, December 9). Opportunities and Challenges of Large Language Models for Low-Resource Languages in Humanities Research. Opportunities and challenges of large language models for low-resource languages in humanities research. https://arxiv.org/html/2412.04497v2#S2