Artificial intelligence is relevant to so many philosophical subfields, and its increased presence in our lives makes it a ripe topic to cover in philosophy courses.
So I thought it would be useful to solicit your suggestions about good readings, focusing on works it might be reasonable to assign undergraduates.
The readings need not be academic works. It may be that some pieces published in popular media are especially pedagogically valuable. Nor must the pieces be written by philosophers to be appropriate for philosophy courses.
In sharing your suggestion, please provide the title and author, and let us know what kind of course you think the reading would be appropriate for (e.g., “contemporary moral problems,” or “for a unit on mind in an intro course”). If you have a link to the piece handy, please include it. Thanks!
(And yes, I acknowledge there is an irony in soliciting readings about a technology that tempts students to not do the reading.)
In Chinese, I recommend a book called A Brief History of Artificial Intelligence (Rengong Zhineng Jianshi), which was written by Nick. I have read it and found it very clear. It can help you learn some basic concepts of AI and the history of AI. It also has a discussion about AI and Philosophy. If you can read Chinese, you should read this book.
Also for background on machine learning and LLMs, I’d like to plug a wonderful book by Andrew Glassner. Deep Learning: a Visual Approach. Takes you deep.
for other Mandarin speakers, I remember when Anil Seth won the 2025 Berggruen Prize for The Mythology of AI Consciousness (which is a great read for a Theories of Consciousness course), there were two Chinese-language prizes awarded. English and Mandarin versions are available. I’ll admit I haven’t read them myself, but they might be worth looking into
The syllabus is now 3 years out of date, but I last taught AI ethics in 2023 and you can find the syllabus with all of its readings here: https://danielweltman.com/teaching.html
In the third year Ethics of AI course I teach I spend the first lecture exploring the nature of AI, since without having some grasp on how AI works and what it is, engaging with normative and applied ethics for it is fruitless.
Milliere and Buckner have a two part paper (A Philosophical Introduction to Language Models) and I use the second part for a 3rd year or senior course, but it’s quite long and some of the writing is a bit disappointing for a philosophy paper (their polysemantic, overloaded use of the term ‘subspace’, for example.) Part II investigates both the multiple realisability thesis and the epistemic opacity dilemma (although it does not use those specific terms). Apart from the terminological issue, it’s very good and the field they take on is very complex and conceptually dense.
The authors refer to the metaphysics of mechanism (Woodward) to investigate the interventionist and interpretation strategies being deployed of late to try to penetrate the black box of LLMs and transformers. Overall they suggest that Putnam’s multiple realisability thesis has been shown to be true, or very close.
A less technical alternative which is nicely balanced in terms of philosophical coverage is VINCENT C. MÜLLER’s “Philosophy of AI: A Structured Overview” in A Companion to Applied Philosophy of AI, First Edition. Edited by Martin Hähnel and Regina Müller.
© 2025 John Wiley & Sons, Inc. Published 2025 by John Wiley & Sons, Inc.
The field is moving so fast that I have to update lectures fortnightly to monthly. In the last introductory lecture I made sure to present Anthropic’s J-Space video when discussing multiple realisability and global workspace theory.
( https://www.youtube.com/watch?v=rKV5JcALQoQ )
This semester, in an intro course where we sample a bunch of different debates, I assigned a reading on the ethics of AI which I knew I could vouch for — because I wrote it:
“Needle in a needle stack: How AI causes semiotic inflation, which causes experiential devaluation”
https://philpapers.org/archive/CHANIA-6.pdf
The two standard form arguments that are compared in the middle ensured that the stance was not a moving target (and as a bonus helped students get acquainted with arguments generally).
good thread from BSky comments from David Marx: https://bsky.app/profile/digthatdata.bsky.social/post/3mrpjrteoyc2o
“Why We’re Not Using AI in This Course, Despite Its Obvious Benefits”
By Patrick Lin (me), 2025
This long-read is basically an open letter to my students. It explains my AI ban, capturing just about all the major issues around LLM use; so I start my tech-ethics classes with this as the very first reading.
The goal is to persuade and get students’ buy-in (so that I don’t need to be an AI cop and rely only on deterrence and penalties), so this reading can work in any class. For tech ethics courses, it also provides a springboard for discussion about LLM ethics.
This Substack article has made the rounds in academia, e.g., here and here.
This is truly excellent. Thank you for taking the time to do this.
🙂
These readings were for an upper-level seminar in ethics and social epistemology, but they’re not difficult and could be used for ethics or aesthetics courses at any level:
Intellectual Property
Trystan Goetze – “AI Art is Theft”
Richard Chappell – “There’s No Moral Objection to AI Art”
Art
Erik Hoel – “AI-art Isn’t Art”
Raphael Milliere – “AI Art is Challenging the Boundaries of Creation”
Boomer Trujillo, Jr. – “AI Art is Art”
Environmental Costs
James O’Donnell & Casey Crownhart – “We Did the Math on AI’s Energy Use”
Andy Masley – “Using ChatGPT is Not Bad for the Environment”
Long-Term Benefits
Marc Andreessen – “Why AI Will Save the World” [good as foil]
Dario Amodei – “Machines of Loving Grace”
Existential Risk
Eliezer Yudkowsky & Nate Soares – If Anyone Builds It, Everyone Dies, Intro to Ch. 6
Employment
Harvey Lederman – “ChatGPT and the Meaning of Life”
Benjamin Todd – “How Not to Lose Your Job to AI”
Our new coursebook, INTRODUCING PHILOSOPHY OF MIND, TODAY, is written/edited to be teachable to undergrads, and features a unit on AI (including a chapter by Osman Attah and Cameron Buckner on LLMs, a chapter by Ben Baker and Catherine Stinson on AGI, a chapter by Luis Favela and Mazviita Chirimuuta on whether brains are computers, and a chapter by Şerife Tekin and Joe Gough on therapy chatbots).
https://www.routledge.com/Introducing-Philosophy-of-Mind-Today/Curry-Daoust/p/book/9781032775333
There will be a cup element on artifical minds very soon!
If non-academic pieces are acceptable, then there is one I’ve been using in my teaching for a while:
The “public debate” about AI is confusing for the general public and for policymakers because it is a three-sided debate by Adam David Long. https://www.lesswrong.com/posts/BTcEzXYoDrWzkLLrQ/the-public-debate-about-ai-is-confusing-for-the-general
I have my own follow up which gets more into the ethical differences and stakes, though it is somewhat more polemical: https://naiveskepticblog.wordpress.com/2026/05/23/three-perspectives-on-ai-accelerationism-pragmatism-doomerism/
Two of my recent essays on AI in education:
https://www.frontporchrepublic.com/2026/05/is-there-room-for-enmity-in-the-a-i-classroom/
https://www.frontporchrepublic.com/2025/12/large-language-models-and-the-new-scholasticism/
Browsing my bookmarks here (had a lot more but my computato recently passed and had to get a new one)
Agency
Consciousness
Religion
Industry
I took a course on the philosophy of AI, and my favorite pieces were the following two, which situate the debate about whether LLMs can think within the broader context of traditional philosophical questions:
https://philpapers.org/rec/CHADTR
https://philpapers.org/rec/SHADTR-2
On Opacity
Cappelen & Dever, Making AI Intelligible: Philosophical Foundations, pp. 3–27.
Vaassen, Bram (2022). AI, Opacity, and Personal Autonomy. Philosophy and Technology 35 (4):1-20.
Cappelen & Dever explain in very simple terms a central sense in which deep neural networks are opaque. Vaassen discusses one way in which using such networks for decision-making is problematic.
On Bias
Johnson, Gabbrielle M. (2020). Algorithmic bias: on the implicit biases of social technology. Synthese 198 (10):9941-9961.
O’Neil, C. (2017). Weapons of math destruction. Penguin Books. Chapter 5: “Civilian Casualties.”
Johnson uses simple machine-learning examples to illustrate how biased data lead to biased algorithms. O’Neil’s book discusses such algorithms in broader social contexts. I find chapter 5 particularly good for student discussion.
Here are some options that could be used for a variety of classes…I teach them all every semester in an “Ethics, Data, and Technology” class that is available for all majors. (The full syllabus for that class can be found here: http://cameronbuckner.net/professional/ethicsdatatech.htm
I have authored/coauthored two Philosophy Compass articles intended as general explainers as to what is new about two of the most important architectures behind the recent boom, deep convolutional neural networks and transformers (large language models). These are more from a philosophy of science/philosophy of mind side.
Buckner, C. (2019). Deep learning: A philosophical introduction. Philosophy compass, 14(10), e12625.
https://compass.onlinelibrary.wiley.com/doi/pdf/10.1111/phc3.12625
Millière, R., & Buckner, C. (2026). The Philosophy of Language Models. Philosophy Compass, 21(3), e70095.
https://compass.onlinelibrary.wiley.com/doi/pdfdirect/10.1111/phc3.70095
For a general introduction to the topic of algorithmic bias, this is also an excellent Philosophy Compass article by Fazelpour and Danks that I think is great as the standard reading on the topic:
Fazelpour, S., & Danks, D. (2021). Algorithmic bias: Senses, sources, solutions. Philosophy Compass, 16(8), e12760.
https://compass.onlinelibrary.wiley.com/doi/full/10.1111/phc3.12760
Iason Gabriel has I think the standard reading on alignment:
Gabriel, I. (2020). Artificial intelligence, values, and alignment: I. gabriel. Minds and machines, 30(3), 411-437.
https://link.springer.com/content/pdf/10.1007/s11023-020-09539-2.pdf
I think Kate Vredenburgh has the standard reading on right to explanation:
Vredenburgh, K. (2022). The right to explanation. Journal of Political Philosophy, 30(2), 209-229.
https://onlinelibrary.wiley.com/doi/pdf/10.1111/jopp.12262
There are lots of other good suggestions on other publicly posted syllabi, for example peruse:
https://www.conspicuouscognition.com/p/philosophy-of-artificial-intelligence
https://drive.google.com/file/d/1wSEtInmXdcQ_i6cdon5IF3WG4R0U8C0p/view
https://philosophy.ucla.edu/wp-content/uploads/2026/03/PHILOS-171-Syllabus-Talma-Paul.pdf
Also, the Curry & Daoust book that Devin already mentioned should be good and has an interesting commentary format.
Assuming the undergrad has no background in computing (and possibly none in philosophy either). The below three articles provide a foundational overview of the field’s philosophical history and are still relevant to the field as of 2026:
Artificial Intelligence: A Modern Approach 4th Edition – Russel & Norvig
Chapter 1 (Introduction)
Chapter 27 (Philosophy, Ethics and Safety of AI)
This is still the standard text book virtually every student of AI and computer science is reading.
Computing Machinery & Intelligence – Alan Turing
The entire AI project is this debate, over and over again since 1950.
Plato’s Theaetetus
Introducing students to epistemology and getting them debating what they think knowledge actually is, compared with the first two readings, seems like a great way to introduce them to the Philosophy of AI.
I think it’s more fun if students start by being immersed in the assumptions found in the first two texts, and then experience their own realizations as to what those assumptions might be after reading the third text.
Again, this assumes undergraduates which have no background in either computing or philosophy.
I’m pairing some works of fiction with philosophical readings for some units of my Ethics of AI class this semester. The hope is that students get practice figuring out what philosophical questions to ask about AI when they engage with the fiction before doing the philosophical readings that raise those questions explicitly. We’ll see how it goes! Here are some examples:
Privacy/Surveillance:
Ken Liu “The Perfect Match” (fiction)
Veliz The Ethics of Privacy and Surveillance Chs. 7 and 8
Vallor “Surveillance and The Examined Life” (Ch. 9 of Technology and the Virtues)
Philip K. Dick “The Minority Report” (fiction)
Susser “Predictive Policing and the Ethics of Preemption”
AI Relationships:
Black Mirror episode “Be Right Back” (fiction)
Campbell, Liu, and Nyholm “Can Chatbots Preserve Our Relationship with the Dead?”
Kind “Love in the Time of AI”
Great seeing such helpful resources. Not sure if it would be useful to you and/or your students, but there are a few folks out here, myself included, with academic backgrounds in philosophy who work in industry roles that are relevant to AI and AI ethics. Feel free to reach out if it might be useful to have a guest presentation or speaker share their perspectives with your class.
I have a piece analysing LLM functioning through Peircean pragmatism and semiotics that could offer a fresh perspective in a philosophy of language or mind course. Some of my undergraduates have found and enjoyed it: “Peirce and Generative AI” https://philpapers.org/rec/LEGPAG
Also, this classic paper by Pierre Steiner still has much to teach, IMO:
https://philpapers.org/rec/STECPA-5
https://newrepublic.com/article/213004/everybody-weirded-ai-except-people-foist-us
https://slate.com/human-interest/2024/02/literacy-crisis-reading-comprehension-college.html
https://joanwestenberg.medium.com/the-death-of-critical-thinking-will-kill-us-long-before-ai-781fdd23cc7c
https://www.theguardian.com/technology/2025/jun/03/creatives-academics-rejecting-ai-at-home-work
https://www.steelsnowflake.org/post/jacques-ellul-and-the-digital-age
https://ellul.org/wp-content/uploads/2015/08/Elluls-1962-Article1.pdf
https://www.markstoll.net/HIST4323/2011/Ellul;_The_Technological_Society_excerpt.pdf
https://1000wordphilosophy.com/wp-content/uploads/2025/04/Heidegger-on-Technology-.pdf
https://philosophynow.org/issues/125/Heidegger_and_Faulkner_Against_Modern_Technology
https://www2.hawaii.edu/~freeman/courses/phil394/The%20Question%20Concerning%20Technology.pdf
https://topdocumentaryfilms.com/humans-gods-technology/ Video
https://newrepublic.com/article/177197/year-ai-came-culture
https://lareviewofbooks.org/article/ai-nightmares-rise-of-the-dead-souls/
https://aeon.co/essays/tech-vexed-how-digital-life-threatens-our-capacity-for-awe
https://www.vox.com/the-highlight/23779413/silicon-valleys-ai-religion-transhumanism-longtermism-ea
https://hilariusbookbinder.substack.com/p/the-average-college-student-today
https://www.newyorker.com/culture/the-weekend-essay/will-the-humanities-survive-artificial-intelligence
https://thepointmag.com/examined-life/a-matter-of-words/
https://www.theatlantic.com/culture/archive/2025/06/artificial-intelligence-illiteracy/683021/
https://theendsdontjustifythemeans.substack.com/p/why-the-age-of-ai-is-the-age-of-philosophy
https://jacobin.com/2025/06/ban-smartphones-tech-society
https://www.theatlantic.com/family/archive/2025/06/smartphone-never-owned/683267/
https://www.tandfonline.com/doi/epdf/10.1080/00048402.2025.2504070?needAccess=true
https://theconversation.com/can-ai-think-and-should-it-what-it-means-to-think-from-plato-to-chatgpt-256648
https://www.newyorker.com/culture/infinite-scroll/gentle-parenting-my-smartphone-addiction
Two older articles are still very much worth reading:
(i) Ned Block, “Psychologism and Behaviorism,” PDF version https://www.nedblock.us/papers/Psychologism.pdf, Philosophical Review v90 n1 (January 1981) 5-43.
With vivid examples to illustrate his points, Block argues that not even a perfect simulator of intelligence need be intelligent. (While a natural description of the ‘Aunt Bubbles machine’ is as a generator of a giant lookup table, one can also think of it as a giant sieve for sentences that selects only for shapes – syntax – and never for meanings – semantics.) It took a while to figure out how to implement good though still imperfect simulators of intelligence. See 3 Blue 1 Brown videos for a reasonably accurate and accessible explanation of LLMs: https://www.3blue1brown.com/lessons/mini-llm/
(ii) Joseph Weizenbaum, “ELIZA – A Computer Program for the Study of Natural Language Communication between Man and Machine,” Communications of the ACM v9 n1 (January 1966) 36-45.
-offers a still necessary reminder that even when people know that something is merely a poor simulator of intelligence, they are very likely nonetheless to find it nearly impossible to resist thinking of it and treating it as intelligent. See also:
Sarah Ciston et al, “The First Chatbot’s Multiple Personalities: Uncovering ELIZA’s source code reveals hidden lessons,” IEEE Spectrum, July 16, 2026 https://spectrum.ieee.org/eliza-chatbot-source-code
(iii) Another kind of useful corrective to much of the marketing hype surrounding ‘AI’ is the entertaining video:
John Mauriello, “Silicon Valley’s Billion Dollar Design Scams,” https://www.youtube.com/watch?v=hDvAQf1cnr8
For a lucid (and often wry) analysis of one pertinent example, see:
Patrick Boyle, “We Need to Talk About Leopold [Aschbrenner]” https://www.youtube.com/watch?v=rE75WvOtcu8
Boyle gives a convincing answer to, “How does a 24 year-old with no trading experience borrow billions to blow up his own [$45B hedge] fund?” A small, initial part of the explanation: “… in California, if you write a science fiction manifesto about how the world is going to change, someone will hand you a $25 million check over coffee.”
(iv) It might also be of interest to students to learn that some who are well-informed regard LLMs as “dead end” technology that cannot yield (toddler) human (or even feline) intelligence. See:
Yann LeCun, “Mathematical Obstacles on the Way to Human-Level AI.” Josiah Willard Gibbs Lecture (3/21/2025), Joint Mathematics Meeting of 2025. https://www.youtube.com/watch?v=ETZfkkv6V7Y
Geometry of Machine Learning Special Lecture 9/16/2025 Speaker: Yann LeCun, “Self-Supervised Learning, JEPA, World Models, and the future of AI” https://www.youtube.com/watch?v=yUmDRxV0krg
(Slide decks for all of LeCun’s talks can be found by searching on “LeCun lecture slides” which should yield the relevant folder within his personal site, http://yann.lecun.com/.)
The two talks by LeCun above would not however be intelligible to the vast majority of undergraduate STEM majors. (The intended audience was research mathematicians.) For the most accessible account that I have yet found, see:
Welch Labs on Joint Embedding Predictive Architecture (JEPA), Parts I & II, with LeCun participating:
Part I: https://www.youtube.com/watch?v=kYkIdXwW2AE
Part II: https://www.youtube.com/watch?v=v_jDvpEGTIg.
(v) While somewhat dated and avowedly naive about moral theory,
Michael Kearns and Aaron Roth, The Ethical Algorithm: The Science of Socially Aware Algorithm Design (Oxford UP, 2019) https://global.oup.com/academic/product/the-ethical-algorithm-9780190948207?cc=us&lang=en&#
offers clear explanations of some complex material that should inform discussions of privacy, security and surveillance. Kearns and Roth’s discussion of the differential approach to privacy – adopted by the US Census Bureau for the 2020 census – can usefully be supplemented by:
Jane Bambauer, Krishnamurty Muralidhar and Rathindra Sarathy, “Fool’s Gold: An Illustrated Critique of Differential Privacy,” Vanderbilt Journal of Entertainment & Technology Law v16 (Summer 2014) 701-755.
-with withering criticism of Bambauer et al in:
Frank McSherry, “Differential Privacy for Dummies,” GITHUB (Jan. 4, 2017)
https://github.com/frankmcsherry/blog/blob/master/posts/2016-02-03.md
Well-informed concerns:
Josep Domingo-Ferrer, David Sánchez, and Alberto Blanco-Justicia, “Viewpoint: The Limits of Differential Privacy (and Its Misuse in Data Release and Machine Learning) Differential privacy is not a silver bullet for all privacy problems.” Communications of the ACM v64 n 7 (JULY 2021) 33-35 https://dl.acm.org/doi/10.1145/3433638.