Over the next several decades, advances in artificial intelligence are likely to transform our societies, economies, cultures, and understanding of what it means to be human. Not enough people have begun to grapple with this fact, and with the wide range of philosophical, ethical, and political issues it raises. If you’re interested in learning (or learning more) about this topic, I hope that this introductory syllabus is helpful.

It’s the syllabus for my undergraduate course, “The Philosophy of Artificial Intelligence”, at the University of Sussex. I’ve previously co-taught the course (with Robyn Waller). However, this year I’m solo-teaching it and have redesigned the entire syllabus to better align with my interests and the topics I believe are most important to cover.

The course moves from foundational questions about AI's nature to practical and political concerns about its impact on our collective future.

• It begins by examining what thought, intelligence, and consciousness mean in the context of machines (Weeks 1-3).

• It then confronts questions about whether AI poses an unprecedented existential risk or is simply another technology in human history (Weeks 4-5).

• The middle section examines the impact of AI on our society and institutions, including democracy (Week 6), the information environment (Week 7), and the economy (Week 8).

• It then concludes by exploring human-human and human-AI relationships in a world transformed by AI (Week 9) before reflecting on our AI future and the role of human agency in that future (Week 10).

For a ten-week course aimed at students with no technical background, I had to be highly selective in choosing the topics, readings, and other materials. If you think I’ve missed anything important, please let me know in the comments.

AI Philosophy Course Reading List

Week 1 — AI: History, Core Ideas, and the Turing Test

Essential

• Turing, A. M. (1950). Computing machinery and intelligence. Mind, 59(236), 433–460.

• Boden, M. A. (2016). Chapter 1: What is Artificial Intelligence? In AI: Its nature and future. Oxford University Press.

Recommended

• 3Blue1Brown. (2017). But what is a neural network? [Video]. YouTube.

• 3Blue1Brown. (2017). Gradient descent, how neural networks learn [Video]. YouTube.

• 3Blue1Brown. Large Language Models explained briefly. [Video]. YouTube.

• Russell, S. J., & Norvig, P. (2021). Introduction (Ch. 1). In Artificial intelligence: A modern approach (4th ed.). Pearson.

• Masley, A. (2025). All the ways I want the AI debate to be