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
    

• 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

• 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