DE-ANTHROPOCENTRISM AS A CONCEPTUAL ALTERNATIVE TO ANTHROPOMORPHIZATION IN THE EPISTEMOLOGY OF ARTIFICIAL INTELLIGENCE
DOI:
https://doi.org/10.5840/eps202663343Keywords:
epistemic concepts, artificial intelligence, knowledge, semantics, normativity, agency, anthropomorphismizationSupporting Agencies:
Исследование подготовлено при поддержке Российского научного фонда, проект № 26-18-00851 «Цифровые трансформации семантики понятия науки в социаль- но-сетевую эпоху: лингво-аналитические, теоретико-эволюционные и социально-коммуникативные исследования».Abstract
The paper explores whether epistemic concepts – such as belief, knowledge, justification, possession of concepts, and doubt – can be justifiably ascribed to AI models. Various positions on this issue are considered, ranging from skepticism to radical optimism. The position defended in this paper, however, leans more towards the optimistic side of the spectrum. The main claim is that justified ascription of epistemic concepts to AI models is determined not by the system's intrinsic properties, but by its ability to function as a normative epistemic agent as well as its capability to interact properly with other epistemic agents. This optimistic view aims to counter criticisms of unjustified anthropomorphism in our understanding of the design and functioning of AI systems. This is accomplished by advocating for the thesis of de-anthropocentrism. This thesis argues that there is nothing in human nature or in the nature of epistemic concepts that justifies the belief that only humans, due to their unique nature, are capable of possessing 'real' knowledge, understanding, reasoning, and possession of concepts. Remarkably, justifying the de-anthropocentrism thesis does not require a reconsideration of human nature or a transformation of views on AI. Instead, it merely requires a certain stance regarding the semantics of epistemic concepts. Thus, the justification for the de-anthropocentrism thesis primarily pertains to the realm of epistemology and the philosophy of language, rather than the study of cognitive science, or specialized research into artificial intelligence.
References
Бажанов В.А. Феномен числа с позиций психологизма: можно ли утверждать, что числа «сочиняются»? // Эпистемология и философия науки. 2025. Т. 62. № 1. С. 134–147.
Вострикова Е.В. Знание и контекст. Размышления о книге Майкла Бломе-Тилльманна «Семантика атрибуции знания» // Эпистемология и философия науки. 2025. Т. 62. № 1. С. 215–233.
Alvarado, R. “AI as an Epistemic Technology”, Science and Engineering Ethics, 2023, vol. 29, pp. 1–18.
Anscombe, E. Intention. Cambridge, MA: Harvard University Press, 2000, 94 p.
Arsenault, L.M. “Humanizing: On the Anthropomorphization of AI as a Tactic of Cultural Integration”, AI & Society, 2026, vol. 41, pp. 3789–3799.
Bazhanov, V.A. “Fenomen chisla s pozitsii psikhologizma: mozhno li utverzhdat’, chto chisla ‘sochinyayutsya’?” [The Phenomenon of Number From The Lens of Psychologism: Can We Claim that Numbers Are Being “Made Up”?], Epistemology & Philosophy of Science, 2025, vol. 62, no. 1, pp. 134–147. (In Russian)
Blackburn, S. Ruling Passion: A Theory of Practical Reasoning. Oxford: Clarendon Press, 1998.
Bratman, M. Structures of Agency: Essays. Oxford: Oxford University Press, 2007.
Budnik, C. “Can We Trust Artificial Intelligence?”, Philosophy & Technology, 2025, vol. 38, pp. 1–23.
Buijsman, S. “Conceptual Disconnect: The Prospect of Concept-Based AI Explanations”, in: Pantsar, M., Stjernfelt, F., Gramelsberger, G., Olteanu A. (eds.) Philosophy of Artificial Intelligence: Optimistic and Pessimistic Views. Cham: Springer, 2025, pp. 1–16.
Cappelen, H., Dever, J. Making AI Intelligible: Philosophical Foundations. Oxford: Oxford University Press, 2021, 175 pp.
Cappelen, H., Dever, J. Going Whole Hog: A Philosophical Defense of AI Cognition, 2025. DOI: 10.48550/arXiv.2504.13988.
Cappelen, H., Dever, J. “A Hyper-Externalist Manifesto for LLMs”, in: Cappelen, H., Dever, J. (eds.) Communicating with AI: Philosophical Perspectives. Oxford: Oxford University Press, 2025, pp. 1–21.
Cash, T., Oppenheimer, D., Christie, S., Devgan, M. “Quantifying Uncert-AI-nty: Testing the Accuracy of LLMs’ Confidence Judgments”, Memory & Cognition, 2025, vol. 54, pp. 375–400.
Cochrane, T. The Emotional Mind: A Control Theory of Affective States. Cambridge: Cambridge University Press, 2018.
Dennett, D. Kinds of Minds: Toward an Understanding of Consciousness. New York: Basic Books, 1997.
Hardy-Vallee, B., Dubreuil, B. “Folk Epistemology as Normative Social Cognition”, Review of Philosophy and Psychology, 2010, vol. 1, pp. 483–498.
Humphreys, P. Extending Ourselves: Computational Science, Empiricism, and Scientific Method. New York: Oxford University Press, 2004.
Ma, W., Valton, V. “Toward an Ethics of AI Belief”, Philosophy & Technology, 2024, vol. 37, pp. 1–28.
Milliere, R., Buckner, C. “A Philosophical Introduction to Language Models – Part II: The Way Forward”, 2024, DOI: 10.48550/arXiv. 2405.03207.
Shanahan, M. “Talking about Large Language Models”, Communications of the ACM, 2024, vol. 67, no. 2, pp. 68–79.
Peacocke, C. Being Known. Oxford: Oxford University Press, 1999.
Price, H. “Defending Desire-as-Belief”, Mind, 1989, vol. 98, pp. 119–127.
Scorici, G., Schultz, M.D., Seele, P. “Anthropomorphization and Beyond: Conceptualizing Humanwashing of AI-enabled Machines”, AI & Society, 2024, vol. 39, pp. 789–795.
Sellars, W. Empiricism And The Philosophy Of Mind. Cambridge, MA: Harvard University Press, 1997.
Shin, D. Automating Epistemology: How AI Reconfigures Truth, Authority, and Verification”, AI & Society, 2025, vol. 41, pp. 1553–1559.
Simbolon, L., Manugeren, M., Barus, E. “Does AI Know Things? An Epistemological Perspective on Artificial Intelligence”, Journal of English Language and Education, 2025, vol. 10, pp. 1022–1028.
Simion, M., Kelp, C. “Trustworthy Artificial Intelligence”, Asian Journal of Philosophy, 2023, vol. 2, pp. 1–12.
Sosa, E. “Epistemic Agency”, The Journal of Philosophy, 2013, vol. 110, pp. 585–605.
Steyvers, M., Tejeda, H., Kumar, A., Belem, C., Karny, S., Hu, X., Smyth, P. “What Large Language Models Know and What People Think They Know”, Nature Machine Intelligence, 2025, vol. 7, pp. 221–231.
Vostrikova, E. “Znanie i kontekst. Razmyshleniya o knige Maikla Blome-Till’manna “Semantika atributsii znaniya” [Knowledge and Context. Reflections on Michael Blome-Tillmann’s “Semantics of Knowledge Attribution”], Epistemology & Philosophy of Science, 2025, vol. 62, no. 1, pp. 215–233. (In Russian)
Wang, C., Liu, X, Awadallah, A. “Cost-Effective Hyperparameter Optimization for Large Language Model Generation Inference”, Proceedings of Machine Learning Research, 2023, vol. 224, pp. 1–17.
