Abhinav Saxena
Library

Reading list: AI testimony

Everything my paper Assertion Without a Speaker engages: the epistemology of testimony from Burge to Lackey, the cases for and against AI testimony, and the machine-learning research on what language models represent and how they fail. Links come from open metadata (Crossref for DOIs, Unpaywall for open access); 23 of the 52 have a free copy.

  1. Alvarado, R. (2023). AI as an epistemic technology. Science and Engineering Ethics 29(5): 32.doi:10.1007/s11948-023-00451-3
  2. Arora, C. (2024). Proxy assertions and agency: The case of machine-assertions. Philosophy and Technology 37(1): 1–19.doi:10.1007/s13347-024-00703-5Open access
  3. Azaria, A. and Mitchell, T. (2023). The internal state of an LLM knows when it’s lying. In Findings of the Association for Computational Linguistics: EMNLP 2023, 967–976.doi:10.18653/v1/2023.findings-emnlp.68Open access
  4. Bender, E. M., Gebru, T., McMillan-Major, A. and Shmitchell, S. (2021). On the dangers of stochastic parrots: Can language models be too big? In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, 610–623.doi:10.1145/3442188.3445922Open access
  5. Burge, T. (1993). Content preservation. The Philosophical Review 102(4): 457–488.doi:10.2307/2185680
  6. Burge, T. (1998). Computer proof, apriori knowledge, and other minds: The sixth Philosophical Perspectives lecture. Philosophical Perspectives 12: 1–37.doi:10.1111/0029-4624.32.s12.1
  7. Burns, C., Ye, H., Klein, D. and Steinhardt, J. (2023). Discovering latent knowledge in language models without supervision. In Proceedings of the Eleventh International Conference on Learning Representations.
  8. Butlin, P. and Viebahn, E. (2025). AI assertion. Ergo: An Open Access Journal of Philosophy 12(0).doi:10.3998/ergo.7960Open access
  9. Creel, K. A. (2020). Transparency in complex computational systems. Philosophy of Science 87(4): 568–589.doi:10.1086/709729Open access
  10. Duede, E. (2022). Instruments, agents, and artificial intelligence: Novel epistemic categories of reliability. Synthese 200(6): 491.doi:10.1007/s11229-022-03975-6
  11. Duede, E. and Davey, K. (2025). Apriori knowledge in an era of computational opacity: The role of AI in mathematical discovery. Philosophy of Science, published online.
  12. Durán, J. M. and Formanek, N. (2018). Grounds for trust: Essential epistemic opacity and computational reliabilism. Minds and Machines 28(4): 645–666.doi:10.1007/s11023-018-9481-6Open access
  13. Faria, D. (2025). Can conversational AIs testify? Challenging the conservative view. Logos and Episteme 16(3): 249–261.doi:10.5840/logos-episteme202516320Open access
  14. Faulkner, P. (2011). Knowledge on Trust. Oxford: Oxford University Press.doi:10.1093/acprof:oso/9780199589784.001.0001
  15. Freiman, O. (2024a). Analysis of beliefs acquired from a conversational AI: Instruments-based beliefs, testimony-based beliefs, and technology-based beliefs. Episteme 21(3): 1031–1047.doi:10.1017/epi.2023.12Open access
  16. Freiman, O. (2024b). AI-testimony, conversational AIs and our anthropocentric theory of testimony. Social Epistemology 38(4): 476–490.doi:10.1080/02691728.2024.2316622Open access
  17. Freiman, O. and Miller, B. (2020). Can artificial entities assert? In S. C. Goldberg (ed.), The Oxford Handbook of Assertion, pp. 415–436. Oxford: Oxford University Press.doi:10.1093/oxfordhb/9780190675233.013.36Open access
  18. Fricker, E. (2006). Second-hand knowledge. Philosophy and Phenomenological Research 73(3): 592–618.doi:10.1111/j.1933-1592.2006.tb00550.x
  19. Fricker, E. (2026). On the metaphysical and epistemic contrasts between human and AI testimony. Inquiry 69(6): 2859–2884.doi:10.1080/0020174x.2025.2553297Open access
  20. Goldberg, S. C. (2010). Relying on Others: An Essay in Epistemology. Oxford: Oxford University Press.doi:10.1093/acprof:oso/9780199593248.001.0001
  21. Goldberg, S. C. (2012). Epistemic extendedness, testimony, and the epistemology of instrument-based belief. Philosophical Explorations 15(2): 181–197.doi:10.1080/13869795.2012.670719
  22. Graham, P. J. (2016). Testimonial knowledge: A unified account. Philosophical Issues 26: 172–186.doi:10.1111/phis.12082Open access
  23. Herrmann, D. A. and Levinstein, B. A. (2025). Standards for belief representations in LLMs. Minds and Machines 35(1): 1–25.
  24. Humphreys, P. (2009). The philosophical novelty of computer simulation methods. Synthese 169(3): 615–626.doi:10.1007/s11229-008-9435-2
  25. Kelp, C. (2018). Assertion: A function first account. Noûs 52(2): 411–442. (First published online 2016.)doi:10.1111/nous.12153Open access
  26. Lackey, J. (1999). Testimonial knowledge and transmission. The Philosophical Quarterly 49(197): 471–490.doi:10.1111/1467-9213.00154
  27. Lackey, J. (2008). Learning from Words: Testimony as a Source of Knowledge. Oxford: Oxford University Press.doi:10.1093/acprof:oso/9780199219162.001.0001
  28. Lambert, N., Morrison, J., Pyatkin, V., Huang, S., Ivison, H., Brahman, F., et al. (2024). Tülu 3: Pushing frontiers in open language model post-training. arXiv:2411.15124.arxiv.org/abs/2411.15124Open access
  29. Lanham, T., Chen, A., Radhakrishnan, A., Steiner, B., Denison, C., Hernandez, D., et al. (2023). Measuring faithfulness in chain-of-thought reasoning. arXiv:2307.13702.arxiv.org/abs/2307.13702Open access
  30. Lederman, H. and Mahowald, K. (2024). Are language models more like libraries or like librarians? Bibliotechnism, the novel reference problem, and the attitudes of LLMs. Transactions of the Association for Computational Linguistics 12: 1087–1103.doi:10.1162/tacl_a_00690Open access
  31. Levinstein, B. A. and Herrmann, D. A. (2025). Still no lie detector for language models: Probing empirical and conceptual roadblocks. Philosophical Studies 182(7): 1539–1565.doi:10.1007/s11098-023-02094-3Open access
  32. Lewis, P., Perez, E., Piktus, A., Petroni, F., Karpukhin, V., Goyal, N., et al. (2020). Retrieval-augmented generation for knowledge-intensive NLP tasks. Advances in Neural Information Processing Systems 33: 9459–9474.
  33. Li, K., Patel, O., Viégas, F., Pfister, H. and Wattenberg, M. (2023). Inference-time intervention: Eliciting truthful answers from a language model. Advances in Neural Information Processing Systems 36: 41451–41530.doi:10.52202/075280-1797
  34. Lin, S., Hilton, J. and Evans, O. (2022). TruthfulQA: Measuring how models mimic human falsehoods. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 3214–3252.doi:10.18653/v1/2022.acl-long.229Open access
  35. Longpre, S., Perisetla, K., Chen, A., Ramesh, N., DuBois, C. and Singh, S. (2021). Entity-based knowledge conflicts in question answering. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, 7052–7063.doi:10.18653/v1/2021.emnlp-main.565Open access
  36. Mallory, F. (2023). Fictionalism about chatbots. Ergo 10(38): 1051–1075.doi:10.3998/ergo.4668Open access
  37. Marks, S. and Tegmark, M. (2024). The geometry of truth: Emergent linear structure in large language model representations of true/false datasets. In Proceedings of the First Conference on Language Modeling.
  38. Nissenbaum, H. (1996). Accountability in a computerized society. Science and Engineering Ethics 2(1): 25–42.doi:10.1007/bf02639315
  39. Nozick, R. (1981). Philosophical Explanations. Cambridge, MA: Harvard University Press.
  40. Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., et al. (2022). Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35: 27730–27744.doi:10.52202/068431-2011
  41. Pritchard, D. (2005). Epistemic Luck. Oxford: Oxford University Press.doi:10.1093/019928038x.001.0001
  42. Shanahan, M. (2024). Talking about large language models. Communications of the ACM 67(2): 68–79.doi:10.1145/3624724Open access
  43. Sharma, M., Tong, M., Korbak, T., Duvenaud, D., Askell, A., Bowman, S. R., et al. (2024). Towards understanding sycophancy in language models. In Proceedings of the Twelfth International Conference on Learning Representations.
  44. Simion, M. and Kelp, C. (2023). Trustworthy artificial intelligence. Asian Journal of Philosophy 2(1): 8.doi:10.1007/s44204-023-00063-5Open access
  45. Sosa, E. (2007). A Virtue Epistemology: Apt Belief and Reflective Knowledge, Volume I. Oxford: Oxford University Press.
  46. Sullivan, E. (2022). Understanding from machine learning models. British Journal for the Philosophy of Science 73(1): 109–133.doi:10.1093/bjps/axz035
  47. Turpin, M., Michael, J., Perez, E. and Bowman, S. R. (2023). Language models don’t always say what they think: Unfaithful explanations in chain-of-thought prompting. Advances in Neural Information Processing Systems 36: 74952–74965.doi:10.52202/075280-3275
  48. Williams, I. and Bayne, T. (2024). Chatting with bots: AI, speech acts, and the edge of assertion. Inquiry, published online.doi:10.1080/0020174x.2024.2434874Open access
  49. Williamson, T. (2000). Knowledge and Its Limits. Oxford: Oxford University Press.
  50. Williamson, T. (2006). Conceptual truth. Aristotelian Society Supplementary Volume 80(1): 1–41.doi:10.1111/j.1467-8349.2006.00136.x
  51. Wright, S. (2018). Knowledge Transmission. London: Routledge.doi:10.4324/9781315111384
  52. Xie, J., Zhang, K., Chen, J., Lou, R. and Su, Y. (2024). Adaptive chameleon or stubborn sloth: Revealing the behavior of large language models in knowledge conflicts. In Proceedings of the Twelfth International Conference on Learning Representations.