B8111 - Digital Epistemology (1) (LM)

Learning outcomes

By the end of the course, students will have a solid understanding of key concepts in analytic epistemology, including knowledge, justification, truth, reasons, and understanding. Applying these concepts within the context of digital technologies—particularly artificial intelligence—students will develop the ability to critically analyze phenomena with significant social and cultural implications. These include issues such as algorithmic bias, epistemic injustice in digital media, and the ethical and environmental challenges posed by AI. Moreover, students will be equipped to propose informed strategies to address these pressing issues.

Course contents

Can generative AI be a reliable source of knowledge? Should we trust the answers produced by large language models? When can AI genuinely support inquiry and understanding, and when does reliance on it undermine our capacity to judge for ourselves?

This course introduces students to central concepts and debates in analytic epistemology and uses them to investigate the rapidly evolving epistemic challenges posed by digital technologies, with particular attention to generative AI and large language models.

Students will explore concepts such as knowledge, truth, justification, testimony, trust, expertise, enquiry, epistemic norms, bias, and epistemic injustice, and will learn how to apply them to concrete cases involving AI-generated content.

The course will examine the different roles that AI systems may play in our intellectual lives: as instruments, sources of information, assistants, or apparent partners in inquiry. It will consider when reliance on their outputs may be rational and productive, as well as their principal epistemic limitations, including hallucination, unreliability, opacity, bias, unfairness, and the possible absence of genuine understanding.

Particular attention will be devoted to the role of human users. Students will be encouraged to move beyond the passive consumption of AI-generated information and to develop the skills required for responsible epistemic agency: questioning outputs, checking sources, detecting bias, evaluating evidence, and exercising independent and reflective judgment.

No previous training in epistemology is required. The course is intended for students from diverse disciplinary backgrounds who are interested in understanding not only what AI systems can do, but also when (and on what grounds) we should believe them.

Note: this is a preliminary description. The complete and detailed syllabus, including the final selection of topics, readings, and activities will be presented during the first lecture.

Readings/Bibliography

The following is a general list of references relevant to the topics covered in the course. The definitive list of required readings will be provided at the beginning of the course, together with a detailed syllabus.

(SOME) CLASSIC PAPERS

Clifford, W. K. (1877). The ethics of belief. Contemporary Review, 29, 289–309.

Conee, E., & Feldman, R. (1985). Evidentialism. Philosophical Studies, 48(1), 15–34. https://doi.org/10.1007/BF00372404

Fricker, E. (1995). Telling and trusting: Reductionism and anti-reductionism in the epistemology of testimony. Mind, 104(414), 393–411. https://doi.org/10.1093/mind/104.414.393

Gettier, E. L. (1963). Is justified true belief knowledge? Analysis, 23(6), 121–123. https://doi.org/10.1093/analys/23.6.121

Goldman, A. I. (1979). What is justified belief? In G. S. Pappas (Ed.), Justification and knowledge (pp. 1–23). D. Reidel. https://doi.org/10.1007/978-94-009-9493-5_1

Goldman, A. I. (2001). Experts: Which ones should you trust? Philosophy and Phenomenological Research, 63(1), 85–110. https://doi.org/10.1111/j.1933-1592.2001.tb00093.x

Hardwig, J. (1985). Epistemic dependence. The Journal of Philosophy, 82(7), 335–349. https://doi.org/10.2307/2026523

Lackey, J. (2011). Testimony: Acquiring knowledge from others. In A. I. Goldman & D. Whitcomb (Eds.), Social epistemology: Essential readings (pp. 71–91). Oxford University Press.

Lipton, P. (1998). The epistemology of testimony. Studies in History and Philosophy of Science Part A, 29(1), 1–31. https://doi.org/10.1016/S0039-3681(97)00022-8

EPISTEMOLOGY AND AI

Alvarado, R. (2023). AI as an epistemic technology. Science and Engineering Ethics, 29(5), Article 32. https://doi.org/10.1007/s11948-023-00451-3

Beisbart, C., & Räz, T. (2022). Philosophy of science at sea: Clarifying the interpretability of machine learning. Philosophy Compass, 17(6), Article e12830. https://doi.org/10.1111/phc3.12830

Boyd, K. (2022). Trusting scientific experts in an online world. Synthese, 200(1), Article 14. https://doi.org/10.1007/s11229-022-03592-3

Budnik, C. (2025). Can we trust artificial intelligence? Philosophy & Technology, 38, Article 10. https://doi.org/10.1007/s13347-024-00820-1

Burge, T. (1998). Computer proof, a priori knowledge, and other minds: The sixth Philosophical Perspectives lecture. Noûs, 32(S12), 1–37. https://doi.org/10.1111/0029-4624.32.s12.1

Burrell, J. (2016). How the machine “thinks”: Understanding opacity in machine learning algorithms. Big Data & Society, 3(1), Article 2053951715622512. https://doi.org/10.1177/2053951715622512

Creel, K. A. (2020). Transparency in complex computational systems. Philosophy of Science, 87(4), 568–589. https://doi.org/10.1086/709729

Duede, E. (2023). Deep learning opacity in scientific discovery. Philosophy of Science, 90, 1089–1099. https://doi.org/10.1017/psa.2023.8

Duede, E., & Davey, K. (2025). A priori knowledge in an era of computational opacity: The role of artificial intelligence in mathematical discovery. Philosophy of Science, 92, 1394–1404. https://doi.org/10.1017/psa.2025.10160

Ferrario, A., Facchini, A., & Termine, A. (2024). Experts or authorities? The strange case of the presumed epistemic superiority of artificial intelligence systems. Minds and Machines, 34(3), Article 30. https://doi.org/10.1007/s11023-024-09681-1

Fleisher, W. (2022). Understanding, idealization, and explainable AI. Episteme, 19, 534–560. https://doi.org/10.1017/epi.2022.39

Freiman, O. (2024a). AI-testimony, conversational AIs and our anthropocentric theory of testimony. Social Epistemology, 38(4), 476–490. https://doi.org/10.1080/02691728.2024.2316622

Freiman, O. (2024b). Analysis of beliefs acquired from a conversational AI: Instruments-based beliefs, testimony-based beliefs, and technology-based beliefs. Episteme, 21, 1031–1047. https://doi.org/10.1017/epi.2023.12

Fricker, E. (2002). Trusting others in the sciences: A priori or empirical warrant? Studies in History and Philosophy of Science Part A, 33(2), 373–383. https://doi.org/10.1016/S0039-3681(02)00006-7

Goldberg, S. C. (2012). Epistemic extendedness, testimony, and the epistemology of instrument-based belief. Philosophical Explorations, 15(2), 181–197. https://doi.org/10.1080/13869795.2012.670719

Goldberg, S. C. (2020). Epistemically engineered environments. Synthese, 197(7), 2783–2802. https://doi.org/10.1007/s11229-017-1413-0

Hauswald, R. (2025). Artificial epistemic authorities. Social Epistemology, 39(6), 716–725. https://doi.org/10.1080/02691728.2025.2449602

Heersmink, R., de Rooij, B., Clavel Vázquez, M. J., & Colombo, M. (2024). A phenomenology and epistemology of large language models: Transparency, trust, and trustworthiness. Ethics and Information Technology, 26, Article 41. https://doi.org/10.1007/s10676-024-09777-3

Magnus, P. D. (2025). On trusting chatbots. Episteme, 22, 906–916. https://doi.org/10.1017/epi.2024.29

Miller, B., & Record, I. (2013). Justified belief in a digital age: On the epistemic implications of secret Internet technologies. Episteme, 10(2), 117–134. https://doi.org/10.1017/epi.2013.11

Miragoli, M. (2025). Conformism, ignorance, and injustice: AI as a tool of epistemic oppression. Episteme, 22, 522–540. https://doi.org/10.1017/epi.2024.11

Symons, J., & Alvarado, R. (2022). Epistemic injustice and data science technologies. Synthese, 200, Article 87. https://doi.org/10.1007/s11229-022-03631-z

ANTHOLOGIES, COLLECTED VOLUMES, AND TEXTBOOKS

Audi, R. (2011). Epistemology: A contemporary introduction to the theory of knowledge (3rd ed.). Routledge.

Boghossian, P., & Williamson, T. (2020). Debating the a priori. Oxford University Press. https://doi.org/10.1093/oso/9780198851707.001.0001

Fricker, M. (2007). Epistemic injustice: Power and the ethics of knowing. Oxford University Press. https://doi.org/10.1093/acprof:oso/9780198237907.001.0001

Fumerton, R. (2022). Foundationalism. Cambridge University Press. https://doi.org/10.1017/9781009028868

Goldman, A. I., & McGrath, M. (2014). Epistemology: A contemporary introduction. Oxford University Press.

Goldman, A. I., & Whitcomb, D. (Eds.). (2011). Social epistemology: Essential readings. Oxford University Press.

Kelp, C. (2023). The nature and normativity of defeat. Cambridge University Press. https://doi.org/10.1017/9781009161022

Nagel, J. (2014). Knowledge: A very short introduction. Oxford University Press. https://doi.org/10.1093/actrade/9780199661268.001.0001

Pritchard, D. (2023). What is this thing called knowledge? (5th ed.). Routledge.

Pritchard, D., & Neta, R. (Eds.). (2008). Arguing about knowledge. Routledge.

Sosa, E., Kim, J., Fantl, J., & McGrath, M. (Eds.). (2008). Epistemology: An anthology (2nd ed.). Wiley-Blackwell.

Warren, J. (2022). The a priori without magic. Cambridge University Press. https://doi.org/10.1017/9781009030472


ENCYCLOPAEDIA ENTRIES

Glanzberg, M. (2025). Truth. In E. N. Zalta & U. Nodelman (Eds.), The Stanford encyclopedia of philosophy (Fall 2025 ed.). https://plato.stanford.edu/archives/fall2025/entries/truth/

Grimm, S. (2025). Understanding. In E. N. Zalta & U. Nodelman (Eds.), The Stanford encyclopedia of philosophy (Winter 2025 ed.). https://plato.stanford.edu/archives/win2025/entries/understanding/

Ichikawa, J. J., & Steup, M. (2026). The analysis of knowledge. In E. N. Zalta & U. Nodelman (Eds.), The Stanford encyclopedia of philosophy (Summer 2026 ed.). https://plato.stanford.edu/archives/sum2026/entries/knowledge-analysis/

Leonard, N. (2023). Epistemological problems of testimony. In E. N. Zalta & U. Nodelman (Eds.), The Stanford encyclopedia of philosophy (Spring 2023 ed.). https://plato.stanford.edu/archives/spr2023/entries/testimony-episprob/

Littlejohn, C. (2025). Internalist vs. externalist conceptions of epistemic justification. In E. N. Zalta & U. Nodelman (Eds.), The Stanford encyclopedia of philosophy (Winter 2025 ed.). https://plato.stanford.edu/archives/win2025/entries/justep-intext/

McLeod, C. (2023). Trust. In E. N. Zalta & U. Nodelman (Eds.), The Stanford encyclopedia of philosophy (Fall 2023 ed.). https://plato.stanford.edu/archives/fall2023/entries/trust/

Schwitzgebel, E. (2024). Belief. In E. N. Zalta & U. Nodelman (Eds.), The Stanford encyclopedia of philosophy (Spring 2024 ed.). https://plato.stanford.edu/archives/spr2024/entries/belief/

Teaching methods

Lectures will be supported by handouts, slides, and supplementary materials, including videos and podcasts. Group work, guided activities, and structured discussion will promote peer learning and the critical examination of key topics. Large language models (LLMs) will be used selectively both to help design in-class activities and, during those activities, as interactive tutoring tools supporting argument reconstruction, conceptual clarification, and critical evaluation.

Assessment methods

Exam format

1. A written essay (between 3000 and 4000 words) in which the student critically discusses one of the topics dealt with during the course (weight: 60% of the total mark).

2. An oral exam aimed at discussing the essay as well as the student's knowledge and understanding of some basic concepts dealt with during the course (weight: 40% of the total mark).

Evaluation criteria

Concerning the essay, the basic criteria for the evaluation are: (i) whether and to what extent the essay shows an adequate knowledge and understanding of the main topics and arguments dealt with in the essay; (ii) whether the essay is adequately structured (as indicated by the guidelines made available during the course); (iii) clarity of exposition and argumentative rigor. Further criteria which, if present, may increase the evaluation are: (iv) some originality in either the content or the argumentative structure; (v) ability to critically assess in an autonomous manner the contents and arguments dealt with in the essay; (vi) ability to connect profitably the topic dealt with in the essay with some of the other topics discussed during the course; (vii) whether the student is able to autonomously perform bibliographical and thematic searches on the topic of the essay.

Concerning the oral part of the exam, the criteria for the evaluation are: (i) the extent to which the student knows and understands in a critical manner the topic of the essay, also in relation to the broader context of the course; (ii) the extent to which the student knows and understands the main topics discussed during the course—other than those discussed in the essay (this will weigh more for those students who didn’t attend the course).

Assessment Grid
30 (cum laude) — Excellent overall performance which demonstrates a solid knowledge as well as a deep and critical understanding of the topics dealt with during the course

30 — Very good overall performance which demonstrates solid knowledge and a very good understanding of the topics dealt with during the course

29-27 — Good overall performance which demonstrates a good knowledge and understanding of the topics dealt with during the course

26-24 — Fair overall performance which demonstrates adequate knowledge and understanding, but with detectable lacunae, of the topics dealt with during the course

23-20 – Sufficient overall performance which demonstrates barely adequate knowledge and understanding, with important lacunae, of the topics dealt with during the course

19-18 — Barely sufficient overall performance which demonstrates a rather superficial knowledge and understanding of the topics dealt with during the course

17 or less – Insufficient overall performance which demonstrates significant failures of understanding as well as absence of knowledge of significant parts of the topics dealt with during the course. Exam failed.


Students with disabilities and Specific Learning Disorders (SLD)
Students with disabilities or Specific Learning Disorders are entitled to special adjustments according to their condition, subject to assessment by the University Service for Students with Disabilities and SLD. Please do not contact teachers or Department staff, but make an appointment with the Service. The Service will then determine what adjustments are specifically appropriate, and get in touch with the teacher. For more information, please visit the page of the University of Bologna DSA service [https://site.unibo.it/studenti-con-disabilita-e-dsa/en/for-students] .


Gen AI Statement
Generative AI may be used for brainstorming, outlining, testing objections, formative feedback, and linguistic revision. Students remain responsible for accuracy, originality, sources, and argumentative quality. Any use must be disclosed through a brief statement identifying the tool, stages of use, representative prompts, and the way outputs were checked or revised.

Teaching tools

Handouts, slides, the Virtuale platform, Wooclap, and selected videos and podcasts by experts on the core topics of the course. Large language models (LLMs) will also be used as teaching tools to support guided activities, conceptual clarification, argument analysis, and interactive tutoring.

Office hours

See the website of Filippo Ferrari