Philosophy of Computer Science: Online Resources

Further Readings for Chapter 17:

Computer Ethics I:
Should We Trust Computers?

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.)

Part V: Further Reading on Computer Ethics:

• For good introductions to computer ethics in general, see:
  • Johnson, D.G. and Snapper, J.W., editors (1985). Ethical Issues in the Use of Computers. Wadsworth
    • Reviewed in:
      Rapaport, W.J. (1986). Review of Johnson and Snapper 1985. Teaching Philosophy, 9(3):275–278

  • Moor, J.H. (1985). What is computer ethics? Metaphilosophy, 16(4):266–275.

  • Johnson, D.G. (2001). Computer Ethics. Prentice Hall, Upper Saddle River, NJ.

  • Anderson 2006

  • Müller, Vincent C., Ethics of Artificial Intelligence and Robotics, The Stanford Encyclopedia of Philosophy (Summer 2021 Edition), Edward N. Zalta (ed.)

  • Pham, K. and Rudra, A. (2021). Teaching Responsible Computing Playbook

  • Awad, Edmond et al. (2022), "Computational Ethics", Trends in Cognitive Sciences 26(5):388–404.

• AAAI's "AI Topics" website is an excellent source, with many links.

• See also The Research Center on Values in Engineering, Science, and Technology's pages on computer ethics.

• Is ethics computable? For an argument that it is not, see:
  • Johnson, Deborah G. & Verdicchio, Mario (2023), "Ethical AI Is Not about AI", Communications of the ACM 66(2) (February): 32–34.
    • "The concepts at the heart of ethics are not fixed or determinate in their precise meaning. To be applied they must be interpreted, and interpretations vary among individuals and groups, from context to context, and may change over time. … Because ethical concepts require social interpretations, they are subject to disagreement, contestation, and change. No computational model can capture all the possible interpretations that can constitute the social meaning of an ethical concept. As such, ethical concepts are not conducive to computational expression." (p. 33, my italics)

§17.1: Introduction

On automated vehicles, see:

• Halpern, S. (2016). Our driverless future. New York Review of Books, 63(18):18 20.
  • Discusses technical and ethical issues concerning the automated decisions made by driverless vehicles.

• Monticello, M. (2016). The state of the self-driving car" and Will self-driving cars make our road safer? Consumer Reports, 81(5):44–49.
  • Offers observations on their relative safety.

• There are other automated vehicles besides self-driving cars, for which some of the same issues arise:
  • Markoff, J. (2015, 7 April). Planes without pilots. New York Times, pages D1, D4,
    • Asks, in the wake of a commercial-airline pilot who who committed suicide, thereby killing all aboard his plane, whether pilots are necessary at all.
    • See also two follow-up letters to the editor (New York Times (14 April): D3, that point out situations in which:
      1. computers should take control over from humans, or
      2. humans might make better decisions — because more creative or context-dependent — than computers.

§17.2: Decisions and Computers

On game-playing computers:

• On checkers, see:
  • Schaeffer, J., Burch, N., Björnsson, Y., Kishimoto, A., Müller, M., Lake, R., Lu, P., and Sutphen, S. (2007). Checkers is solved. Sciencexpress

  • "Chinook (computer program)" (Wikipedia)

• On chess and other games, see:
  • "Deep Blue (chess computer)" (Wikipedia)

  • Silver, D. et al. (2016). Mastering the game of Go with deep neural networks and tree search. Nature, 539:484–489.

  • Campbell, M. (2018). Mastering board games. Science, 362(6419):1118.

  • Kasparov, G. (2018, 7 December). Chess, a Drosophila of reasoning. Science, 362(6419):1087

  • Silver, D. et al. (2018). A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play. Science, 362(6419):1140–1144.

  • Halina, M. (2021). Insightful artificial intelligence. Mind & Language, 36(2):315–329.

§17.5: Should Computers Make Decisions with Us?

• On team efforts, such as the ones that Hutchins discusses, see:
  • Vélez, Natalia; Christian, Brian; Hardy, Mathew; Thompson, Bill D.; & Griffiths, Thomas L. (2023), "How Do Humans Overcome Individual Computational Limitations by Working Together?", Cognitive Science 47(e13232).

• On the Question for the Reader concerning whether human-computer interaction constitutes the use of a (human) oracle, especially in the context of the trustworthiness of large language models, see:
  • Faggioli, Guglielmo et al. (2024), "Who Determines What Is Relevant? Humans or AI? Why Not Both?", Communications of the ACM 67(4, April):31–34.

  

• The following paper, citing Hutchins, argues that we should not trust decisions that computers make by themselves (especially LLMs) (see the next section), but that we could trust decisions that we make with computers (as "hybrid" systems):
  Ferrario, Andrea; Facchini, Alessandro; & Termine, Alberto (2024), "Experts or Authorities? The Strange Case of the Presumed Epistemic Superiority of Artificial Intelligence Systems", Minds & Machines 34(30).
  

§17.6: Should We Trust Decisions Computers Make?

• For a philosophical investigation of the nature of trust, see:
  • Baier, A. (1986). Trust and antitrust. Ethics, 96(2):231–260.

• Heingartner, D. (2006, 18 July). Maybe we should leave that up to the computer. New York Times
  • Discusses whether computers can make better decisions than humans, and observes that "mathematical models generally make more accurate predictions than humans do. … The main reason for computers' edge is their consistency — or rather humans' inconsistency — in applying their knowledge."

• Brachman & Levesque 2022, p. 213, answers a version of Moor's question ("Is it right to trust an AI system with a rock-solid track record that makes decisions we cannot quite follow?") in the negative.

• For scientific research on wrong or illogical explanations by humans, see:
  • Tversky, A. and Kahneman, D. (1974). Judgment under uncertainty: Heuristics and biases. Science, 185(4157):1124–1131.

  • Wainer, H. (2007, May-June). The most dangerous equation. American Scientist, 95(3):249ff
    • On humans' difficulty in reasoning about probability and statistics.

• For other readings on humans' difficulty in reasoning, see my bibliography on "Reasoning"

§17.6.1: The Bias Problem

• For introductions to "Deep Learning", see:
  • LeCun, Y., Bengio, Y., and Hinton, G. (2015). Deep learning. Nature, 521:436–444.
    • An introduction to "deep" machine learning by three of its pioneering Turing Award winners.

  • Buckner, C. (2019). Deep learning: A philosophical introduction. Philosophy Compass, 14(10).
    • An introduction to deep learning for philosophers.

• On the Bias Problem, see:
  • Savage, N. (2016). When computers stand in the schoolhouse door. Communications of the ACM, 59(3):19–21.
    • A discussion of how "researchers are trying to identify … and root … out" biases found in classification algorithms.

  • Metz, C. (2019, 16 August). A.I. is learning from humans. Many humans. New York Times.
    • Reports on minimally trained non-professionals who supply the training for machine-learning systems, which raises the question of how accurate those systems algorithms could be.

  • Singer, N. and Metz, C. (2019, 19 December). Many facial-recognition systems are biased, says U.S. study. New York Times.
    • Discusses evidence of bias in facial-recognition algorithms.

  • Smith, C.S. (2020, 2 January). Dealing with bias in artificial intelligence. New York Times.
    • Contains interviews with three female AI researchers on how they deal with such bias in algorithms.

  • For a detailed analysis of bias problems in natural-language processing systems (such as GPT-3) — and as opposed to natural-language understanding systems — see:
    • Bender, Emily M.; Gebru, Timnit; McMillan-Major, Angelina; & Shmitchell, Shmargaret (2021), "On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? 🦜", CFAccT '21: Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency: 610–623.
      • Note the parrot in the title of this paper!

§17.6.2: The Black Box Problem:

• On the black-box problem, see:
  • Brachman, R.J. (2002). Systems that know what they're doing. IEEE Intelligent Systems, pages 67–71.
    • A leading AI researcher suggests how and why decision-making computers should be able to explain their decisions.

  • Brachman & Levesque 2022 suggest that AI systems should not only be intelligent but also intelligible.

  • There are (at least) two strategies for trying to understand how deep learning systems work. One is to explicate the statistical algorithms that underlie them. Another is to provide a higher-level, "psychological" explanation or description. For discussions of attempts to get systems to be able to "account" for themselves, see:
    • Lipton, Z.C. (2016). The mythos of model interpretability. In 2016 ICML Workshop on Human Interpretability in Machine Learning (WHI 2016).

    • Kuang, C. (2017, 26 November). Can A.I. be taught to explain itself? New York Times Magazine, pages MM46ff

    • Metz, C. (2018, 7 March). Google researchers are learning how machines learn. New York Times, page B3.

    • Cappelen, Herman; & Dever, Josh (2021), Making AI Intelligible: Philosophical Foundations (Oxford: Oxford University Press).

    • Hill, Robin K. (2023, 29 July), "Bigger than a Blackbox", Blog@CACM:
      … we know exactly how deep learning works. It's not a black box. It's a complex data-gobbling number-crunching white box. We just can't force the results into our familiar concepts. When we try, and fail, it all starts to look mysterious.

• Machine-learning programs are not the only technology to suffer from a black-box problem:
  "Medicines without obvious mechanisms of action are still vetted by the FDA."
  — Herrman, John (2023), "The AI Magic Show", New York Magazine/Intelligencer (18 January)

• Although the following observation is not directly related to the black-box problem as it is discussed in this chapter, it is not exactly irrelevant:
  "There are at least three philosophies that are used in the choice of test data. … Most users of a large program are not interested in the details of its functioning; they only wish to obtain answers. That is, they wish to treat the program as a black box. … In the glass-box method of testing, the logical structure of the program is examined, and for each alternative that may occur, test data are derived that will lead to that alternative. … Let us mention one further philosophy of program testing, the philosophy that is, unfortunately, quite widely used. This might be called the ticking-box method. It consists of doing no testing at all after the project is fairly well debugged, but instead turning it over to the customer for trial and acceptance. The result, of course, is a time bomb."
  — Robert L. Kruse, Data Structures and Program Designs (1987)
  [quoted on the cover of Computing Reviews 31(2) (February 1990)]

• For other arguments on the value of symbolic computation, see:
  • Levesque, Hector J. (2014), "On Our Best Behaviour", Artificial Intelligence 212:27–35.

  • Levesque, H.J. (2017). Common Sense, the Turing Test, and the Quest for Real AI. MIT Press, Cambridge, MA.

  • Bringsjord et al. 2018

  • Garnelo, M. and Shanahan, M. (2019). Reconciling deep learning with symbolic artificial intelligence: Representing objects and relations. Current Opinion in Behavioral Sciences, 29:17–23

  • Seabrook 2019

  • Landgrebe, J. and Smith, B. (2021). Making AI meaningful again. Synthese, Vol. 198, pages 2061–2081.

  • Sablé-Meyer, M.; Fagot, J.; Caparos, S.; van Kerkoerle, T.; Amalric, M.; and Dehaene, S. (2021). Sensitivity to Geometric Shape Regularity in Humans and Baboons: A Putative Signature of Human Singularity. PNAS 118(16).

  • Brachman & Levesque 2022 argues that common sense is needed for AI and that common sense can — or perhaps must — be based on symbolic computation.

  • Quilty-Dunn, Jake; Porot, Nicolas; & Mandelbaum, Eric (2023), "The Best Game in Town: The Re-Emergence of the Language of Thought Hypothesis across the Cognitive Sciences", Behavioral and Brain Sciences.

§17.7: Are There Decisions Computers Must Make for Us?

• For contrasting discussions of the US Airways case, see:
  • Haberman, C. (2009, 27 November). The story of a landing. New York Times Book Review

  • Salter, J. (2010, 14 January). The art of the ditch. New York Review of Books, 57(1).

• For another airplane incident involving "rogue" computers, see:
  • O'Sullivan, M. (2017, 12 May). The untold story of QF72: What happens when 'psycho' automation leaves pilots powerless? Sydney Morning Herald

• The 2019 crashes of two Boeing 737 Max 8 jets are another important case study. The crashes seem to have been due to some combination of one or more of the following: a faulty sensor that input erroneous information to certain software, the software itself that may have been flawed, or lack of proper pilot training in the use of the software.
  • For a summary, see "Boeing 737 MAX Groundings" (Wikipedia).

  • See also:
    Nicas, J., Kitroeff, N., Gelles, D., and Glanz, J. (2019, 1 June). Boeing built deadly assumptions into 737 Max, blind to a late design change. New York Times.

• Zremski, J. (2009, 18 February). Perspectives differ on autopilot, icing. Buffalo News, pages A1–A2.
  • Explores the possibility that a decision-making computer might make things more difficult for a human who is in the decision-making loop.

• Halpern, S. (2015, 2 April). How robots & algorithms are taking over. New York Review of Books, 62(6):24, 26, 28.
  • Points out, among other things, that an "overreliance on automation, and on a tendency to trust computer data even in the face of contradictory physical evidence, can be dangerous", in part because the human in the decision-making loop might not be paying attention: "over half of all airplane accidents were the result of the mental autopilot brought on by actual autopilot".

§17.8: Are There Decisions Computers Shouldn't Make?

• On decisions that computers shouldn't make, see:
  • Asimov, I. (1950). The evitable conflict. Astounding Science Fiction.
    • Reprinted in Isaac Asimov, I, Robot (Garden City, NY: Doubleday, 1950), Ch. 9, pp. 195–218.
    • A fictional approach to Moor's question about decisions made by machines.

  • OHeigeartaigh, S. (2013, 9 August). Would you hand over a moral decision to a machine? Why not? Moral outsourcing and Artificial Intelligence. Practical Ethics.
    • A blog that considers many of the issues discussed in Moor 1979.

  • Friedman and Kahn argue that programmers should not design computer systems so that users think that the systems are "intelligent".
    • The April 2004 issue of Communications of the ACM (Miller 2004) has a whole section devoted to this.

• On belief-desire-intention (BDI) theory, see, e.g.:
  • Kumar, D. (1994). From beliefs and goals to intentions and actions: An amalgamated model of inference and acting. Unpublished PhD dissertation, Department of Computer Science, SUNY Buffalo.

  • Kumar, D. (1996). The SNePS BDI architecture. Decision Support Systems, 16(1):3–19.

  • "Belief-desire-intention software model" (Wikipedia)

  • And for an argument against BDI theory, see:
    • Rosenberg, Alex (2018, 2 November), "Is Neuroscience a Bigger Threat than Artificial Intelligence?", 3:AM Magazine
      • For commentary on Rosenberg's essay, see:
        "Rosenberg on the 'threat' to our self-understanding posed by neuroscience", Leiter Reports: A Philosophy Blog (20 November 2018).
        • See especially comment #28 by "Howard".

• On what I refer to in the book as a "classic theme" in AI — the appearance of beliefs, desires, and intentions vs. real beliefs, desires, and intentions — see:
  • Joseph Weizenbaum's "Eliza" program, which, in its most famous version, allegedly simulated a Rogerian psychotherapist. See:
    • Weizenbaum, J. (1966). ELIZA — A computer program for the study of natural language communication between man and machine. Communications of the ACM, 9(1):36–45. Reprinted in Communications of the ACM 26(1) (January 1983): 23–28.
      • Be sure to read the correspondence between Weizenbaum and Arbib on p. 28 of the reprint.

    • Weizenbaum, J. (1967). Contextual understanding by computers. Communications of the ACM, 10(8):474–480.

    • Weizenbaum 1976

    • Shapiro, Stuart C. and Kwasny, S.C. (1975). Interactive consulting via natural language. Communications of the ACM, 18(8):459–462.

    • Winograd, T. (1983). Language as a Cognitive Process; Vol. 1: Syntax. Addison-Wesley, Reading, MA, Ch. 2.

    • "Programming Project 1: NATURAL-LANGUAGE PROCESSING BY PATTERN MATCHING"

• In literature, I highly recommend:
  • Powers, R. (1995). Galatea 2.2: A Novel. Farrar Straus & Giroux, New York.
    • A cognitive-science graduate student is assigned the task of programming a computer to pass the PhD exam in English literature; the grad student falls in love with the computational cognitive agent
    • Excerpts at: Amazon.com and Google.com/books

  • Dick, P.K. (1968). Do Androids Dream of Electric Sheep? Doubleday, New York. Reprinted by Random House/Del Rey/Ballantine, 1996.
    • The basis of the film Blade Runner
    • See also:
      Beebee, H. (2017, 5 October). Who is Rachel? Blade Runner and personal identity. IAI [Institute of Art and Ideas] News

• In cinema (in addition to Blade Runner), there are:
  • Steven Spielberg's A.I Artificial Intelligence (2001),
  • Spike Jonze's Her (2013),
  • Alex Garland's Ex Machina (2014),

  to name just three.