A Value-Driven Framework for the Design of Ethically Aligned Artificial Intelligence Systems
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AI systems are operating with increasing autonomy and capability in complex domains in the real world becoming more and more ubiquitous in ways that we do not understand yet. These systems will continue to become more complex and pervasive as they become more intelligent and newer applications are enabled. Increased autonomy in such systems means that they have the potential take actions that can be detrimental to humans within their increased sphere of interaction unless they are designed to be otherwise. Thus systems with AI capabilities urgently require better alignment with human values. This is called the AI Value alignment problem (AI VAP). In this paper, a framework for creating such an AI system is proposed based on the traditional Indian conceptualization of intelligence and values. Human values have been the foundation of Indian scriptures, including the Bhagwad Gita, Vedas, and the Upanishads. Values act as guidelines for our behavior. They have a significant influence on our personalities, attitudes, and perceptions. The traditional Indian philosophy offers a holistic perspective on intelligence and values by considering various aspects of human cognition and consciousness. The proposed framework for creating Value-aligned AI system based on these perspectives provides conceptualization of the different modules required to actually implement Value aligned AI systems. This makes the proposed framework different from those that provide high level ideas without delineating implementation aspects. A rudimentary case of AI value aligned system is also provided to illustrate ideas presented in the paper.
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Allen, C., Smit, I., Wallach, W. (2005). Artificial morality: Top-down, bottom-up, and hybrid approaches. Ethics and information technology, 7(3),149–155.
Amodei, D., Olah, C., Steinhardt, J., Christiano, P., Schulman, J., Mané, D. (2016). Concrete problems in AI safety. arXiv preprint arXiv:1606.06565.
Asilomar AI Principles. (2017). Future of Life.
Benjamin, R. (2023). Race after technology. Social theory re-wired (pp. 405–415). Routledge.
Bulla, L., De Giorgis, S., Mongiovì, M., Gangemi, A. (2025). Large language models meet moral values: A comprehensive assessment of moral abilities. Computers in Human Behavior Reports, 17,100609.
Christian, B. (2020). The alignment problem: Machine learning and human values. W W Norton and Company.
Christiano, P. (2017, Mar). Prosaic AI alignment. AI Alignment. Retrieved fromhttps://ai-alignment.com/prosaic-ai-control-b959644d79c2
Dignum, V. (2018). Ethics in artificial intelligence: introduction to the special issue. Springer.
Eubanks, V.(2018). Automating inequality: How high-tech tools profile, police, and punish the poor. St. Martin’s Press.
Flanagan, M., Howe, D.C., Nissenbaum, H. (2008). Embodying values in technology: Theory and practice. Information technology and moral philosophy, 322,24.
Floridi, L., & Sanders, J.W. (2004).Onthemoralityofartificialagents. Minds and machines, 14(3), 349–379.
Gabriel, I. (2020). Artificial intelligence, values, and alignment. Minds and machines, 30(3), 411–437.
Geitgey, A. (2019, Sep). Faking the News with Natural Language Processing and GPT-2. Medium. Retrieved from https://medium.com/@ageitgey/deepfaking-the-news-with-nlp-andtransformer-models-5e057ebd697d
Gill, A.S., & Germann, S. (2021). Conceptual and normative approaches to AI governance for a global digital ecosystem supportive of the unsustainable development goals (sdgs). AI and Ethics, 1–9.
Gloor, P., Fronzetti Colladon, A., Grippa, F.(2022). Measuring ethical behavior with AI and natural language processing to assess business success. Scientific Reports, 12(1),10228.
Hibbard, B. (2012). Model-based utility functions. Journal of Artificial General Intelligence, 3(1),1–24.
Ibid (1938). Prashnaupanishad4.8.,25.
Irving, G., Christiano, P., Amodei, D. (2018). Ai safety via debate. arXiv preprint arXiv:1805.00899.
Kearns, M., & Roth, A. (2019). The ethical algorithm: The science of socially aware algorithm design. Oxford University Press.
Kim, W., & Lee, K. (2020). Building ethical ai from news articles. 2020 IEEE/itu international conference on artificial intelligence for good (ai4g) (pp.210–217).
Klenk, M. (2021). How do technological artefacts embody moral values? Philosophy & Technology, 34(3),525–544.
Kodiyan, A.A. (2019). An overview of ethical issues in using AI systems in hiring with a case study of amazon’s AI based hiring tool. Research gate Preprint.
Kroes, P., & Verbeek, P.-P. (2014). The moral status of technical artefacts (Vol.17). Springer.
Latour, B. (1993). We have never been modern (new york: Harvester wheatsheaf). Page Intentionally Left Blank.
Latour, B., et al. (1992). Where are the missing masses? The sociology of a few mundane artifacts. Shaping technology/building society: Studies in sociotechnical change, 1,225–258.
Lee, K.-F. (2018). Ai superpowers: China, silicon valley, and the new world order. HoughtonMifflin.
Leike, J., Krueger, D., Everitt, T., Martic, M., Maini, V., Legg, S.(2018).Scalable agent alignment via reward modeling: a research direction. arXiv preprint arXiv:1811.07871.
Maedche, A., Legner, C., Benlian, A., Berger, B., Gimpel, H., Hess, T. Söllner,M. (2019). AI-based digital assistants. Business & Information Systems Engineering, 61(4),535–544.
McKinlay, J., De Vos, M., Hoffmann, J.A., Theodorou, A. (2025). Understanding the process of human-ai value alignment. arXiv preprint arXiv:2509.13854.
Miller,B.(2021).Is technology value-neutral? Science, Technology, & Human Values, 46(1),53–80.
Mullen, J. (2015, Jul). Google rushes to fix software that tagged photo with racial slur CNN business. Cable News Network. Retrieved from https://edition.cnn.com/2015/07/02/tech/google-imagerecognition-gorillas-tag/index.html
Noble, S.U. (2018). Algorithms of oppression. Algorithms of oppression. New York university press.
Ogunlere, S., & Adebayo, A. (2015,01). Ethical issues incomputing sciences.
O’neil, C. (2017). Weapons of math destruction: How big data increases inequality and threatens democracy. Crown.
Pana, S.C., Schweppeb, J., Teoa, A.Z., Indrajayaa, A., Wenzeld, N. (2024). Using AI-generated prequestions to improve memory and text comprehension.
Paranjpe, A.C. (2013). The concept of dharma: Classical meaning, common misconceptions and implications for psychology. Psychology and Developing Societies, 25(1),1–20.
Picard, R. (1997). Affective computing cambridge. MA: MIT Press [Google Scholar],19.
Pitt, J.C. (2014). “guns don’t kill, people kill”; values in and/or around technologies. The moral status of technical artefacts (pp. 89–101). Springer.
Poel,I.v.d.,&Kroes,P. (2014). Can technology embody values? The moral status of technical artefacts (pp.103–124). Springer.
Poole, D.I., Goebel, R.G., Mackworth, A.K. (1998). Computational intelligence (Vol.1). Oxford University Press Oxford.
Poole, D.L., & Mackworth, A.K. (2010). Artificial intelligence: foundations of computational agents. Cambridge University Press.
Russell,S. (2019). Human compatible: Artificial intelligence and the problem of control. Penguin.
Russell, S.J. (2010). Artificial intelligence a modern approach. Pearson Education, Inc.
Schmager, S., Pappas, I.O., Vassilakopoulou, P. (2025). Understanding human-centred ai: a review of its defining elements and a research agenda. Behaviour & Information Technology, 44(15), 3771–3810.
Schwartz, S.H. (1992). Universals in the content and structure of values: Theoretical advances and empirical tests in 20 countries. Advances in experimental social psychology (Vol.25, pp.1–65). Elsevier.
Sharpe, M., Bowen, M., Lambiotte, R. (2025). Quantifying digital habits. EPJ Data Science, 14(1),72.
Shen, H., Knearem, T., Ghosh, R., Yang, Y.-J., Mitra, T., Huang, Y. (2024).Value compass: A framework of fundamental values for human-AI alignment. arXiv preprint arXiv:2409.09586.
Sullins,J.P. (2006). When is a robot a moral agent. Machine ethics, 6(2006), 23–30.
Terra, J. (2023, May). Agents in ai: Exploring intelligent agents and its types, functions & composition. Simplilearn. Retrieved from https://www.simplilearn.com/what-is-intelligent-agent-inai-types-function-article
Verbeek,P.-P. (2011). Moralizing technology. University of Chicago press.
Wiener, N. (1960). Some moral and technical consequences of automation: As machines learn they may develop unforeseen strategies at rates that baffle their programmers. Science, 131(3410), 1355–1358.
Winner,L. (2017). Do artifacts have politics? Routledge.
Wolf, M.J., Miller, K.W., Grodzinsky, F.S. (2017). Why we should have seen that coming: commentsonmicrosoft’ stay “experiment,” and wider implications. The ORBIT Journal, 1(2),1–12.
Zadeh,L.A. (1988). Fuzzylogic. Computer, 21(4),83–93.
Zednik, C. (2021). Solving the black box problem: a normative framework for explainable artificial intelligence. Philosophy & Technology, 34(2), 265–288.
Ziems, C., Yu, J., Wang, Y.-C., Halevy, A., Yang, D. (2022). The moral integrity corpus: A benchmark for ethical dialogue systems. Proceedings of the 60th annual meeting of the association for computational linguistics (volume 1: Long papers) (pp.3755–3773).
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