How is "AI testimony" possible? A "Non-Anthropocentric" Attempt to Defend "Technological Testimony"


Published: Jun 7, 2026
Keywords:
Social epistemology Technological epistemology technological testimony testimony Artificial intelligence
Weng Hong, Yue
Abstract

In contemporary society, where artificial intelligence technology is highly developed, Generative AI and its generated content permeate daily practices, shaking the anthropocentric concept of "testimony." Therefore, whether "AI testimony," generated and provided by AI, can be accepted has become an unavoidable frontier issue in contemporary philosophical research. Canadian scholar Freiman, after analyzing related debates, argues that "AI testimony" lies in the middle ground between humans and technological objects, between everyday language and mechanical scales, thus proposing the concept of "quasi-testimony" to describe it. This paper, while supporting the viewpoint of "AI testimony," adopts a more concise "deceptive argument" to demonstrate that Freiman's "quasi-testimony" is unnecessary. Simultaneously, this paper attempts to delve into the anthropocentric perspective and address the potential problems arising from accepting "AI testimony," striving to supplement the discussion of "AI testimony" with possible ideas and necessary normative frameworks.

Article Details
  • Section
  • Articles
Downloads
Download data is not yet available.
References
Antoniadis, C. (2025). Knowing: The Zone of Interest from a philosophical point of view. Dia-noesis: A Journal of Philosophy, 17(1), 351–374. https://doi.org/10.12681/dia.41718
Clark, A. (2025). Extending minds with generative AI. Nature Communications, 16, Article 4627.
Clark, A., & Chalmers, D. J. (1998). The extended mind. Analysis, 58(1), 7–19.
Cornelio, C., & Dash, S. (2023). Combining data and theory for derivable scientific discovery with AI-Descartes. Nature Communications, 14, Article 3.
Crawford, K. (2021). Atlas of AI: Power, politics, and the planetary costs of artificial intelligence. Yale University Press.
Fricker, E. (2015). How to make invidious distinctions amongst reliable testifiers. Episteme, 12(2), 173–201.
Freiman, O. (2024). AI-testimony, conversational AIs and our anthropocentric theory of testimony. Social Epistemology, 38(4), 6–10.
Freiman, O., & Miller, B. (2020). Can artificial entities assert? In S. Goldberg (Ed.), The Oxford handbook of assertion (pp. 414–430). Oxford University Press.
Goldberg, S. (2015). Epistemic extendedness, testimony, and the epistemology of instrument-based belief. Philosophical Explorations, 18(2), 179–196.
Graham, P. (1997). What is testimony? The Philosophical Quarterly, 47(187), 227–232.
Graham, P. (2022). Testimony is not disjunctive. Asian Journal of Philosophy, 1(1), 1–18.
Humphreys, P. (2004). Extending ourselves: Computational science, empiricism, and scientific method. Oxford University Press.
IBM. (2023). What are AI hallucinations? https://www.ibm.com/topics/ai-hallucinations
Jones, K. (1996). Trust as an affective attitude. Ethics, 107(1), 4–25.
Katz, P. S. (2014). Expert robot: Using artificial intelligence to assist judges in admitting scientific expert testimony. Albany Law Journal of Science & Technology, 24, 1–32.
Kourtoglou, O., Vavouras, E., & Sariannidis, N. (2024). The Stoic paradigm of ethics as a philosophical tool for objectifying the concepts of organizational ethics, corporate social responsibility, and corporate governance. Conatus - Journal of Philosophy, 9(2), 119–143. https://doi.org/10.12681/cjp.37932
Kusch, M. (2002). Testimony in communitarian epistemology. Studies in History and Philosophy of Science, 33(2), 335–354.
Lackey, J. (2006). The nature of testimony. Pacific Philosophical Quarterly, 87(2), 177–197.
Li, Y. (2024). Reduce AI illusion based on data science technology and prompt engineering. Applied and Computational Engineering, 97(1), 152–156.
Morris, M. R., & Brubaker, J. R. (2025). Generative ghosts: Anticipating benefits and risks of AI afterlives. In Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems (CHI ’25), Article 536 (pp. 1–14). Association for Computing Machinery.
Nyrup, R. (2023). Trustworthy AI: A plea for modest anthropocentrism. Asian Journal of Philosophy, 2. https://doi.org/10.1007/s44204-023-00096-w
Record, I., & Miller, B. (2013). Justified belief in a digital age: On the epistemic implications of secret technologies. Episteme, 10(2), 119–136.
Russell, S., & Norvig, P. (2021). Artificial intelligence: A modern approach. Pearson.
Spitale, G., & Germani, F. (2025). The making of digital ghosts: Designing ethical AI afterlives.
The Alan Turing Institute. (2026). The Turing AI scientist grand challenge. https://www.turing.ac.uk/research/research-projects/turing-ai-scientist-grand-challenge
Vavouras, E., Koliopoulou, M., & Manolis, K. (2024). From participatory leadership to digital transformation under the interpretation of political philosophy: Types of leadership in education and school administration. Dia-noesis: A Journal of Philosophy, 15, 153–170. https://doi.org/10.12681/dia.38171