Philosophy of Computer Science: Online Resources

Further Readings for Chapter 20:

Computer Science: A Personal View

Note 1: Many of these items are online; links are given where they are known. Other items may also be online; an internet search should help you find them.

Note 2: In general, works are listed in chronological order. (This makes it easier to follow the historical development of ideas.)

§20.1: Introduction

• On Sellar's characterization of philosophy as "understand[ing] how things in the broadest possible sense of the term hang together in the broadest possible sense of the term":
  • "Here are some of the different kinds of 'things in the broadest possible sense of the term': thoughts, colors, smiles, songs, haircuts, opportunities, dollars, games, meanings, dangers, holes, numbers … as well as atoms, molecules, photons, quarks, cells, gravity. What are songs made of? Are colors real? What are opportunities made of? Don't ask a scientist — or don't expect a cogent and problem-free answer. One impatient answer is that nothing exists except atoms (or subatomic particles). Then what is the chemical composition of a song, a poem, an opportunity? 'Those aren't real,' comes eanswer. Really? Attemps to reduce all the familiar things of life to the proper entities of science are all procrustean, but then what is the relationship between the various candidates for thinghood? How do they all hang together? Some people find these questions so annoyingly confusing that they block their ears and walk away. Fine. Philosophy isn't for everybody. But those who want to satisfy their curiosity will find that formal, systmatic attempts to line up all the issues and knock them off one by one are doomed. Here is where philsophy is an art, an exercise in imagination-streching, in dislodging the blockades set up by a host of semi-understood ideas."
    —Daniel C. Dennett (2023), I've Been Thinking (New York: W.W. Norton), pp. 358–359.

  • "To put it in Sellarsian terms: to understand is to see how things hang together."
    —Dellsén et al. (2024), §4.4
    

• In addition to the possible 6th question about the social and political implications of computing discussed below in my comments on §20.3.5, there is a possible 0th question:
  What is computation?

  In the book, I considered this as part of Question 1A, but I now think that it is better considered as preliminary to all the other questions. It is also ambiguous between a theoretical reading and a physical reading: Is computation a mathematical concept focused on, say, Turing Machines and other such abstract, formal systems? Or is computation something done by computers?

  Even that last question has two readings, depending on whether 'computer' is taken to include humans or to refer only to computing (calculating) machines (or beyond, to, say, DNA "computers", etc.). After all, the first mathematical theory of computation, due to Turing, began with physical side of things: It was a mathematical model of what humans did when they computed (calculated), as well as of what the mathematical or theoretical limitations of computing were (as opposed to physical limitations). (This is why I consider it to be an early, if not the first, example of AI.)

  Brian Cantwell Smith argues in his book Computational Reflections (Cambridge, MA: MIT Press, 2026) that computation is fundamentally a physical process that affects the real world and is part of that world.

• For semi-technical discussions of computational complexity and P=NP, see:
  • Austin, A. K. (1983). An elementary approach to NP-completeness. American Mathematical Monthly, 90:398–399.

  • Cook, S.A. (1983). An overview of computational complexity. Communications of the ACM, 26(6):400–408.

  • Mycielski, J. (1983). The meaning of the conjecture P ≠ NP for mathematical logic. American Mathematical Monthly, 90:129–130.

  • Loui, M.C. (1996). Computational complexity theory. ACM Computing Surveys, 28(1):47–49.

  • Fortnow, L. (2009). The status of the P versus NP problem. Communications of the ACM, 52(9):78–86.

  • Aaronson, S. (2013). Why philosophers should care about computational complexity. In Copeland, B.J., Posy, C.J., and Shagrir, O., editors, Computability: Turing, Gödel, Church, and Beyond, pages 261–327. MIT Press, Cambridge, MA

  • Fortnow, L. (2022). Fifty years of P vs. NP and the possibility of the impossible. Communications of the ACM, 65(1):76–85.

• For non-technical discussions, see:
  • Fortnow, L. (2013). The Golden Ticket: P, NP, and the Search for the Impossible. Princeton University Press, Princeton, NJ.

  • Walsh, T. (2014, November-December). Candy Crush's puzzling mathematics. American Scientist, 102(6):430–433.

• And for a wonderful fictional treatment (written by a computer scientist), see:
  • Ginsberg, Matt (2017), Factor Man (Eugene, OR: Zowie Press)

• On a possible relationship between the P vs. NP problem and large language models such as ChatGPT, see:
  • Lipton, R.J. (2023), "Things We Did Not Know How to Compute", Gödel's Lost Letter and P=NP (31 December)

§20.3.3: Practical Computability

• Davis, M.D. (1982). Why Gödel didn't have Church's thesis. Information and Control, 54:3–24, esp. p. 23.

• Vardi, M.Y. (2014). Boolean satisfiability: Theory and engineering. Communications of the ACM, 57(3):5.

• Fortnow, L. (2021, 20 August). Trusting scientists, Computational Complexity (blog)

§20.3.4: Physical Computability

• Piccinini 2015

• Ritchie & Piccinini 2018

• Shagrir 2022

• An additional aspect of ethical computability  —  or perhaps even a 6th question that CS is (or should be!) concerned with  —  is this:
  How does  —  or should  —  computation (including, but not limited to, the use of algorithmic methods, the ubiquity of physical computers, apps (programs, social media, etc.)) affect people and society?

  For a discussion of this, see Hermans 2026.
  

• For a good overview of many issues in what I call ethical computability, see the interview with Sven Nyholm, author of This Is Technology Ethics: An Introduction (Wiley-Blackwell).
  

• The online MS degree in AI at the University of Texas at Austin includes a course in "ethical AI/robotics", and "Each course will also include formal ethics training", according to:
  • Singer, Natasha (2023, 27 January), "University of Texas Will Offer Large-Scale Online Master's Degree in A.I.", New York Times

• A good survey of some recent work on ethical computation is:
  • Savage, Neil (2023), "How Robots Can Learn to Follow a Moral Code", Nature (26 October).

• A lot of examples of ethical computation studies have been generated by work on large language models (LLMs) such as ChatGPT and GPT-4:
  • Bommasani, Rishi et al. (2022), "On the Opportunities and Risks of Foundation Models", esp. §5, "Society"; §5.6, "Ethics of Scale"; and — very especially — §5.6.4, "When Not to Build".

  • Weidinger, Laura; Uesato, Jonathan; et al. (2022), "Taxonomy of Risks Posed by Language Models", in FAccT '22, June 21–24 (Seoul, Republic of Korea) (ACM):214–229.

  • OpenAI (2023), "GPT-4 System Card"
    On the notion of a "system card", see:
    • MetaAI (2022), "System Cards, a New Resource for Understanding How AI Systems Work"

  • Zhou, Jiawei; Zhang, Yixuan; et al. (2023), "Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions", in CHI '23: Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (ACM): Article No. 436, pp. 1–20

• On ethical computation in AI, see Floridi 2024

• On whether we should build conscious AIs, see:
  • Seth, Anil K. (2025, forthcoming), "Conscious Artificial Intelligence and Biological Naturalism", Behavioral and Brain Sciences, esp. §6.0: "What Should We (Not) Do?"

  

• The New York Times crossword puzzle for 20 July 2026 had the following clue and answer:
  Clue: Important consideration for A.I. programmers
  Answer: ETHICS