AI Machines vs the Non-Processing Minds of Classical Theism
Transparency, Trust, and the Deep Black-box Problem
DOI:
https://doi.org/10.12775/SetF.2026.018Schlagworte
artificial intelligence, classical theism, unchangeable mind, inferential internalism, religious faith, black-box problemAbstract
In the paper I contrast what I take to be a universal feature of all the AI research – namely focusing on processing information or transitions from one state of a machine to another – with the role assigned to non-processing minds in the classical theism framework. I briefly discuss the latter in epistemological context – the issue of inferential internalism and some problems of transparency, opacity, and trust. In this I show that the contrast between AI and the classical theism outlook is highly relevant for the cluster of issues relating to transparency or opacity, reliabilism, and trust, which is discussed today as the black-box problem. In particular, I identify in this context what I take to be the most serious aspect of the black-box problem; I reject the idea that the problem is a superficial one; and I claim that it poses basic limits on the AI research and cannot be solved along the lines of Explainable Artificial Intelligence.
Literaturhinweise
Auriol, Peter. 1952. Scriptum Super Primum Sententiarum. Prologue – Distinction I, ed. By E.M. Buytaert OFM. St. Bonaventure, N.Y.: The Franciscan Institute.
Boghossian, Paul. 2001. “Inference and Insight.” Philosophy and Phenomenological Research, 63: 633–40.
Boghossian, Paul. 2003. “Blind Reasoning.” Proceedings of the Aristotelian Society, Supplementary Volumes 77: 225–48.
Boghossian, Paul. 2014. “What is inference?” Philosophical Studies 169: 1–18.
Bonezzi, Andrea, Ostinelli, Massimiliano, Melzner, Johann. 2022. “The Human Black-Box: The Illusion of Understanding Human Better Than Algorithmic Decision-Making.” Journal of Experimental Psychology, https://doi.org/10.1037/xge0001181.
Brożek, Bartosz, Furman, Michał, Jakubiec, Marek, Kucharzyk, Bartłomiej. 2024. „The black box problem revisited. Real and imaginary challenges for automated legal decision making.” Artificial Intelligence and Law 32: 427–40.
Burrell, Jena. 2016. “How the machine ‘thinks’: Understanding opacity in machine learning algorithms.” Big Data&Society, https://doi.org/10.1177/2053951715622512.
Davies, Brian. 1993. An Introduction to the Philosophy of Religion. Oxford: Oxford University Press.
Dawson, John W., Jr. 2010. Why Prove it Again? Alternative Proofs in Mathematical Practice. New York: Birkhäuser.
Durán, Juan Manuel, Jongsma Karin Rolanda. 2021. “Who is afraid of black box algorithms? On the epistemological and ethical basis of trust in medical AI.” Journal of Medical Ethics 47: 329–35.
Durán, Juan Manuel, forthcoming. “Beyond transparency: computational reliabilism as an externalist epistemology of algorithms.” Forthcoming in Philosophy of Science for Machine learning: Core Issues and New Perspectives, edited by Juan M. Durán and Giorgia Pozzi, available at https://philpapers.org/archive/DURMLJ.pdf.
Eschenbach, Warren J. von. 2021. “Transparency and the Black Box Problem: Why We Do Not Trust AI.” Philosophy & Technology 34: 1607–22.
Geach, Peter Thomas. 1977. “Can God Fail to Keep Promises?” Philosophy 52: 93–95.
Maurus, Sylvestrus. 1876. Quaestiones Philosophicae, t. 1: Summulae et quaestiones prooemiales logicae, Paris: Ch. Taranne.
Pázmány, Petrus. 1894. Dialectica, ed. S. Bognár. Budapest: Typis Regiae Scientiarum Universitatis.
Polizzius, Iosephus. 1675. Disputationes philosophicae. Tomus Primus De Logica. Palermo.
Quinn, Patrick. 2001. “Aquinas’s Views on Teaching.” New Blackfriars 82: 108–20.
Roosta, Seyed H. 2000. Parallel Processing and Parallel Algorithms. New York: Springer.
Siebert, Matthew Kent. 2015. “Aquinas on Believing God.” Proceedings of the Aristotelian Catholic Philosophical Association 89: 97–107.
Syropoulos, Apostolos. 2008. Hypercomputation. Computing Beyond the Church-Turing Barrier. New York: Springer.
Van Leeuven Jan, Wiedermann Jiří, “The Turing Machine Paradigm in Contemporary Computing.” In Mathematics Unlimited – 2001 and Beyond, edited by Björn Engquist and Wilfried Schmid, 1139–56. New York: Springer.
Zednik, Carlos. 2021. “Solving the Black Box Problem: A Normative Framework for Explainable Artificial Intelligence.” Philosophy & Technology 34: 265–88.
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