PhilSci Archive

Items where Subject is "Specific Sciences > Computer Science"

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Number of items at this level: 167.

A

Abbott, Russ (2015) Causality, computing, and complexity. UNSPECIFIED.

Abbott, Russ (2008) The reductionist blind spot. [Preprint]

Abbott, Russ (2009) The reductionist blind spot. [Preprint]

Abrams, Marshall (2021) Pseudorandomness in Simulations and Nature. In: UNSPECIFIED.

Alvarado, Ramón (2022) AI as an Epistemic Technology. [Preprint]

Alvarado, Ramón (2024) Challenges for Computational Reliabilism. [Preprint]

Andrews, Mel (2023) The Devil in the Data: Machine Learning & the Theory-Free Ideal. [Preprint]

Astorga, E. F. S. and Letelier, Juan Carlos (2026) Anticipation and Structural Coupling: Two Sides of the Same Coin. [Preprint]

B

Baltag, Alexandru and Smets, Sonja (2004) The Logic of Quantum Programs. UNSPECIFIED. (In Press)

Barrett, Jeffrey A. and Chen, Eddy Keming (2025) Algorithmic Randomness and Probabilistic Laws. [Preprint]

Barrett, Jeffrey A. and Chen, Eddy Keming (2025) Algorithmic Randomness, Exchangeability, and the Principal Principle. [Preprint]

Barrett, Jeffrey A. and Gabriel, Nathan (2021) Reinforcement with Iterative Punishment. [Preprint]

Barrett, Jeffrey A. and Skyrms, Brian and Cochran, Calvin (2018) Hierarchical Models for the Evolution of Compositional Language. [Preprint]

Beck, Micah (2016) On The Hourglass Model, The End-to-End Principle and Deployment Scalability. [Preprint]

Beisbart, Claus and Räz, Tim (2022) Philosophy of science at sea: Clarifying the interpretability of machine learning. Philosophy Compass.

Belot, Gordon (2020) Absolutely No Free Lunches! [Preprint]

Binder, Bernd (2002) Spacetime Memory: Phase-Locked Geometric Phases. [Preprint]

Bordg, Anthony (2019) Univalent Foundations and the UniMath Library. The Architecture of Mathematics. in Reflections on the Foundations of Mathematics, Synthese Library, 407.

Boyer-Kassem, Thomas and Imbert, Cyrille (2018) Explaining Scientific Collaboration: a General Functional Account. In: UNSPECIFIED.

Bruineberg, Jelle and Dolega, Krzysztof and Dewhurst, Joe and Baltieri, Manuel (2020) The Emperor’s New Markov Blankets. [Preprint]

Buckner, Cameron (2019) Deep Learning: A Philosophical Introduction. [Preprint]

C

Capraru, Mihnea (2024) Making sense of ‘genetic programs’: biomolecular Post–Newell production systems. Biology & Philosophy, 39. ISSN 1572-8404

Coelho Mollo, Dimitri (2019) Against Computational Perspectivalism. [Preprint]

Coelho Mollo, Dimitri (2019) Are There Teleological Functions to Compute? [Preprint]

Colombo, Matteo (2023) Oron Shagrir, The Nature of Physical Computation. BJPS Review of Books.

Corbeel, Aude and De Haro, Sebastian (2025) Black Holes are about Quantum Information. [Preprint]

Creel, Kathleen A. (2019) Transparency in Complex Computational Systems. [Preprint]

Cuffaro, Michael E. (2018) Universality, Invariance, and the Foundations of Computational Complexity in the light of the Quantum Computer. [Preprint]

Curtis-Trudel, Andre E (2024) Computation in Context. [Preprint]

Curtis-Trudel, Andre E (2020) Implementation as Resemblance. In: UNSPECIFIED.

Curtis-Trudel, Andre E (2022) The ~In~Determinacy of Computation. [Preprint]

Curtis-Trudel, Andre E (2022) Mathematical Explanation in Computer Science. In: UNSPECIFIED.

Curtis-Trudel, Andre E (2020) Why do we need a theory of implementation? [Preprint]

D

De Florio, Vincenzo (2014) Behavior, Organization, Substance: Three Gestalts of General Systems Theory. Proc. of the 2014 Conference on Norbert Wiener in the 21st Century.

De Jong, Eline and De Haro, Sebastian (2026) A Contextual Approach to Technological Understanding and Its Assessment. [Preprint]

Dong, Zili and Cai, Weixin and Zhao, Shimin (2024) Simpson's Paradox Beyond Confounding. [Preprint]

Duede, Eamon (2022) Deep Learning Opacity in Scientific Discovery. In: UNSPECIFIED.

Duran, Juan Manuel (2025) Beyond transparency: computational reliabilism as an externalist epistemology of algorithms. [Preprint] (Submitted)

Duran, Juan Manuel (2023) Machine learning, justification, and computational reliabilism. [Preprint]

de Lima Prestes, José Augusto (2025) Pseudo-Consciousness in AI: Bridging the Gap Between Narrow AI and True AGI. [Preprint]

E

E. Szabó, László (2003) Formal Systems as Physical Objects: A Physicalist Account of Mathematical Truth. [Preprint]

Efstathiou, Sophia and Nydal, Rune and Laegreid, Astrid and Kuiper, Martin (2019) Scientific knowledge in the age of computation: Explicated, computable and manageable? THEORIA. An International Journal for Theory, History and Foundations of Science, 34 (2). pp. 213-236. ISSN 2171-679X

Eva, Benjamin and Shear, Ted and Fitelson, Branden (2020) Four Approaches to Supposition. [Preprint]

F

Facchini, Alessandro and Termine, Alberto (2022) Towards a Taxonomy for the Opacity of AI Systems. [Preprint]

Fehr, Carla and Jones, Janet (2022) Culture, exploitation, and the epistemic approach to diversity. [Preprint]

Floridi, Luciano (2008) Against Digital Ontology. [Preprint]

Floridi, Luciano (2008) Understanding Epistemic Relevance. [Preprint]

Freeborn, David Peter Wallis (2026) A Model of Understanding in Deep Learning Systems. [Preprint]

Freeman, N. Will (2025) From Computation to Coherence: Toward a Structural Symbolic Theory of General Intelligence. [Preprint]

Fuentes, Jorge I. (2024) Computational systems as higher-order mechanisms. [Preprint]

Fuentes, Jorge I. (2023) Efficient Mechanisms. [Preprint]

G

Gopnik, Alison (2024) Empowerment Gain as Causal Learning, Causal Learning as Empowerment Gain: A bridge between Bayesian causal hypothesis testing and reinforcement learning. Presented at the 29th Meeting of The Philosophy of Science Association, New Orleans, Dec 2024. In: UNSPECIFIED.

Greif, Hajo (2020) Invention, Intension and the Limits of Computation. [Preprint]

Grimsley, Christopher (2020) Causal and Non-Causal Explanations of Artificial Intelligence. In: UNSPECIFIED.

H

Hagar, Amit and Korolev, Alex (2007) Quantum Hypercomputation - Hype or Computation? [Preprint]

Hewitt, Carl (2019) For Cybersecurity, Computer Science Must Rely on Strong Types. [Preprint]

Hewitt, Carl (2019) For Cybersecurity, Computer Science Must Rely on Strongly-Typed Actors. [Preprint]

Hewitt, Carl (2019) For Cybersecurity, Computer Science Must Rely on Strongly-Typed Actors. [Preprint]

Hewitt, Carl (2019) For Cybersecurity, Computer Science Must Rely on the Opposite of Gödel’s Results. [Preprint]

Hocquet, Alexandre and Wieber, Frederic (2021) Epistemic issues in computational reproducibility: software as the elephant in the room. European Journal for Philosophy of Science. ISSN 1879-4912

Hocquet, Alexandre and Wieber, Frederic (2017) “Only the Initiates Will Have the Secrets Revealed”: Computational Chemists and the Openness of Scientific Software. IEEE Annals of the History of Computing, 39 (4). pp. 40-58. ISSN 1058-6180

Holik, Federico (2022) Non-Kolmogorovian Probabilities and Quantum Technologies. Entropy.

Hudetz, Laurenz and Crawford, Neil (2022) Variation semantics: when counterfactuals in explanations of algorithmic decisions are true. [Preprint]

I

Imbert, Cyrille (2005) Why diachronically emergent properties must also be salient. [Preprint]

Imbert, Cyrille and Ardourel, Vincent (2022) Formal verification, scientific code, and the epistemological heterogeneity of computational science. [Preprint]

Inhasz, Rafael and Stern, Julio Michael (2010) Emergent Semiotics in Genetic Programming and the Self-Adaptive Semantic Crossover. [Preprint]

J

Jebeile, Julie and Lam, Vincent and Räz, Tim (2020) Understanding Climate Change with Statistical Downscaling and Machine Learning. [Preprint]

Johnson, Gabbrielle (2020) Algorithmic Bias: On the Implicit Biases of Social Technology. [Preprint]

K

Kasirzadeh, Atoosa and Klein, Colin (2021) The Ethical Gravity Thesis: Marrian Levels and the Persistence of Bias in Automated Decision-making Systems. Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society.

Kelly, Matthew (2026) Compression, Dynamics, and Control in Large Language Models: Toward a High-Level Theory. [Preprint]

Ketland, Jeffrey (2022) Boolos’s Curious Inference in Isabelle/HOL. Archive of Formal Proofs.

Ketland, Jeffrey (2020) Computation and Indispensability. Logic and Logical Philosophy, 30.

Khosrowi, Donal and Beck, Lukas (2026) Like It or Not — Recommender Systems Lack a Coherent Normative Foundation. The 2026 ACM Conference on Fairness, Accountability, and Transparency (FAccT '26).

Khosrowi, Donal and Finn, Finola (2025) Can Generative AI Produce Novel Evidence? [Preprint]

Khosrowi, Donal and van Basshuysen, Philippe (2023) Making a Murderer –- How risk assessment tools may produce rather than predict criminal behavior. [Preprint]

Kieval, Phillip Hintikka and Westerblad, Oscar (2024) Deep Learning as Method-Learning: Pragmatic Understanding, Epistemic Strategies and Design-Rules. In: UNSPECIFIED.

Kim, Bryce (2017) Role of information and its processing in statistical analysis. [Preprint]

Kim, Bryce (2017) Role of information and its processing in statistical analysis. [Preprint]

Kim, Bryce (2016) What if we have only one universe and closed timelike curves exist? [Preprint]

Krzanowski, Roman (2017) Minimal Information Structural Realism. In: UNSPECIFIED.

Kuczynski, John-Michael (2025) Applied Set Theory and Logic. [Preprint]

Kugele, Sean and Neemeh, Zachariah and Kronsted, Christian and Gallagher, Shaun and Franklin, Stan (2025) Virtually Impossible: Obstacles to Generalizing between Simulated and Real Humans. [Preprint]

Kästner, Lena and Crook, Barnaby (2023) Explaining AI Through Mechanistic Interpretability. [Preprint]

L

Ladyman, James and Presnell, Stuart and Short, Anthony J. and Groisman, Berry (2006) The Connection between Logical and Thermodynamic Irreversibility. [Preprint]

Landgrebe, Jobst and Smith, Barry (2019) Making AI meaningful again. Synthese. ISSN 1573-0964

Lapin, Yair (2021) Irreversibility and Complexity. [Preprint]

Leonelli, Sabina and Tempini, N (2020) Data Journeys in the Sciences. Springer.

Levin, Ilya (2026) Epistemology of Generative AI: The Geometry of Knowing. [Preprint]

Levin, Ilya (2026) Navigational Epistemology: Toward a New Understanding of Knowledge in the Age of Generative AI. [Preprint]

Luk, Robert (2010) Understanding scientific study via process modeling. Foundations of Science, 15 (1). pp. 49-78. ISSN 1233-1821

López-Rubio, Ezequiel (2020) The Big Data razor. [Preprint]

López-Rubio, Ezequiel and Ratti, Emanuele (2019) Data science and molecular biology: prediction and mechanistic explanation. [Preprint]

M

Maley, Corey J. (2022) How (and Why) to Think that the Brain is Literally a Computer. [Preprint]

Maroney, O J E and Timpson, C G (2017) How is there a Physics of Information? On characterising physical evolution as information processing. [Preprint]

McCabe, Gordon (2004) Universe creation on a computer. [Preprint]

Miller, Ryan (2022) Nonrational Belief Paradoxes as Byzantine Failures. Logos & Episteme, 13 (4). pp. 343-358.

Mitchell, Sandra D. (2019) Instrumental Perspectivism: Is AI Machine Learning Technology like NMR Spectroscopy? [Preprint]

N

Neth, Sven (2022) A Dilemma for Solomonoff Prediction. [Preprint]

Norelli, Maria Federica and Votsis, Ioannis and Williamson, Jon (2024) The Interplay of Data, Models, and Theories in Machine Learning. In: UNSPECIFIED.

Northcott, Robert (2019) Big data and prediction: four case studies. [Preprint]

O

Ovidiu Cristinel, Stoica (2023) Does a computer think if no one is around to see it? [Preprint]

P

Papayannopoulos, Philippos (2020) Unrealistic Models for Realistic Computations: How Idealisations Help Represent Mathematical Structures and Found Scientific Computing. [Preprint]

Parker, Matthew W. (2006) Computing the Uncomputable, or, The Discrete Charm of Second-Order Simulacra. In: UNSPECIFIED. (Unpublished)

Parker, Matthew W. (2003) Undecidability in Rn: Riddled Basins, the KAM Tori, and the Stability of the Solar System. Philosophy of Science, 70 (2). pp. 359-382.

Parker, Matthew W. (2005) Undecidable Long-term Behavior in Classical Physics: Foundations, Results, and Interpretation.

Pence, Charles H. and Ramsey, Grant (2018) How to do digital philosophy of science. [Preprint]

Peters, Uwe (2022) Algorithmic political bias in artificial intelligence systems. [Preprint]

Piccinini, Gualtiero (2004) Computers. UNSPECIFIED. (Unpublished)

Piccinini, Gualtiero (2004) The Functional Account of Computing Mechanisms. UNSPECIFIED. (Unpublished)

Pietsch, Wolfgang (2014) Aspects of theory-ladenness in data-intensive science. In: UNSPECIFIED.

Pietsch, Wolfgang (2013) Big Data – The New Science of Complexity. In: UNSPECIFIED.

Pietsch, Wolfgang (2026) Nothing New Under the Sun – Large Language Models and Scientific Method. [Preprint]

Plutniak, Sébastien (2021) Assyrian merchants meet nuclear physicists: history of the early contributions from social sciences to computer science. The case of automatic pattern detection in graphs (1950s-1970s). Interdisciplinary Science Reviews, 46 (4). pp. 547-568. ISSN 0308-0188

Poldrack, Russell A. (2020) The physics of representation. [Preprint]

R

Rabiza, Marcin (2024) A Mechanistic Explanatory Strategy for XAI. [Preprint]

Ramstead, Maxwell J. D. and Wiese, Wanja and Miller, Mark and Friston, Karl J. (2020) Deep neurophenomenology: An active inference account of some features of conscious experience and of their disturbance in major depressive disorder. [Preprint]

Ratti, Emanuele (2022) Integrating Artificial Intelligence in Scientific Practice: Explicable AI as an Interface. Philosophy & Technology, 35.

Ratti, Emanuele (2019) Phronesis and Automated Science: The Case of Machine Learning and Biology. [Preprint]

Ratti, Emanuele (2020) What Kind of Novelties Can Machine Learning Possibly Generate? The Case of Genomics. [Preprint]

Ratti, Emanuele and D'Agostino, Giuseppe (2025) Beyond 'Trapped Pets' and 'Red Buttons': Bioinformatics as an Experimental Discipline. [Preprint]

Ray, Faron (2022) Two Types of Explainability for Machine Learning Models. In: UNSPECIFIED.

Rodin, Andrei (2025) Epistemology of Topological Data Analysis. [Preprint]

Rosenstock, Sarita (2020) Learning from the Shape of Data. In: UNSPECIFIED.

Roth, Aaron and Tolbert, Alexander (2025) Resolving the Reference Class Problem at Scale. [Preprint]

Räz, Tim (2024) From Explanations to Interpretability and Back. [Preprint]

Räz, Tim (2020) Understanding Deep Learning With Statistical Relevance. [Preprint]

Räz, Tim (2022) Understanding risk with FOTRES? AI and Ethics.

Räz, Tim and Beisbart, Claus (2022) The Importance of Understanding Deep Learning. Erkenntnis. ISSN 0165-0106

S

Sanctus, J (2025) Imperfection as a Constitutive Property of Artificial Intelligence. [Preprint]

Sassoli de Bianchi, Massimiliano and Sassoli de Bianchi, Luca (2026) Understanding without Consciousness: Quantum Structures and the Autonomy of Meaning. [Preprint]

Sawaf, Khaled (2026) The Incoherence of Reflexive AI Governance: An Architectural Theory Across the Threat Spectrum. [Preprint]

Schmitz, Timothy (2023) On Epistemically Useful Physical Computation. [Preprint]

Scorzato, Luigi (2024) Reliability and Interpretability in Science and Deep Learning. [Preprint]

Sergeyev, Yaroslav and Garro, Alfredo (2010) Observability of Turing Machines: a refinement of the theory of computation. Informatica, 21 (3). pp. 425-454.

Sergeyev, Yaroslav and Garro, Alfredo (2013) Single-tape and multi-tape Turing machines through the lens of the Grossone methodology. Journal of Supercomputing, 65 (2). pp. 645-663.

Shkliarevsky, Gennady (2023) THE EMPEROR WITH NO CLOTHES: Chomsky Against ChatGPT. [Preprint]

Short, Tony and Ladyman, James and Groisman, Berry and Presnell, Stuart (2005) The Connection between Logical and Thermodynamical Irreversibility. [Preprint]

Sprenger, Jan (2017) Foundations of a Probabilistic Theory of Causal Strength. [Preprint]

Sterkenburg, Tom F. (2019) The Meta-Inductive Justification of Induction. Episteme.

Sterkenburg, Tom F. (2019) The Meta-Inductive Justification of Induction: The Pool of Strategies. Philosophy of Science.

Sterkenburg, Tom F. (2016) Solomonoff Prediction and Occam's Razor. Philosophy of Science, 83 (4). pp. 459-479.

Sterkenburg, Tom F. and Grünwald, Peter D. (2020) The No-Free-Lunch Theorems of Supervised Learning. [Preprint]

Sterkenburg, Tom F. and Grünwald, Peter D. (2021) The No-Free-Lunch Theorems of Supervised Learning. Synthese.

Sterner, Beckett and Witteveen, Joeri and Franz, Nico (2020) Coordinating dissent as an alternative to consensus classification: insights from systematics for bio‐ontologies. History and Philosophy of the Life Sciences. ISSN 0391-9714

Sterrett, S. G. (2014) Turing on the Integration of Human and Machine Intelligence. [Preprint]

Stinson, Catherine (2019) From Implausible Artificial Neurons to Idealized Cognitive Models: Rebooting Philosophy of Artificial Intelligence. [Preprint]

Stuart, Michael T. and Winters, Sabine (2026) Learning Curves in Orbit: Progress with AI in Space Science. [Preprint]

Sullivan, Emily (2022) Link Uncertainty, Implementation, and ML Opacity: A Reply to Tamir and Shech. Scientific Understanding and Representation (Eds) Insa Lawler, Kareem Khalifa & Elay Shech. pp. 341-345.

Sullivan, Emily (2019) Understanding from Machine Learning Models. British Journal for the Philosophy of Science. ISSN 1464-3537

Szabó, Máté (2021) Péter on Church's Thesis, Constructivity and Computers. [Preprint]

T

Tabatabaei Ghomi, Hamed (2022) Setting the demons loose: computational irreducibility does not guarantee unpredictability or emergence. Philosophy of Science, 89. pp. 761-783.

Tempini, N and Leonelli, Sabina (2018) Concealment and Discovery: The Role of Information Security in Biomedical Data Re-Use. [Preprint]

Tsementzis, Dimitris (2017) A Meaning Explanation for HoTT. [Preprint]

V

Vorobyev, Oleg Yu (2016) Postulating the theory of experience and chance as a theory of co~events (co~beings). [Preprint]

W

Wallace, Rodrick (2008) Lurching Toward Chernobyl: Dysfunctions of Real-Time Computation. [Preprint]

Weinstein, Galina (2023) Debating the Reliability and Robustness of the Learned Hamiltonian in the Traversable Wormhole Experiment. [Preprint]

Weinstein, Galina (2023) Navigating the Conjectural Labyrinth of the Black Hole Information Paradox. [Preprint]

Weinstein, Galina (2023) The Neverending Story of the Eternal Wormhole and the Noisy Sycamore. [Preprint]

Weinstein, Galina (2023) Reframing the Event Horizon: The Harlow-Hayden Computational Approach to the Firewall Paradox. [Preprint]

Weinstein, Galina (2023) Revisiting Nancy Cartwright's Notion of Reliability: Addressing Quantum Devices' Noise. [Preprint]

Wieber, Frederic and Hocquet, Alexandre (2018) Computational Chemistry as Voodoo Quantum Mechanics : Models, Parameterization, and Software. [Preprint]

Wieber, Frederic and Hocquet, Alexandre (2020) Models, parameterization, and software: epistemic opacity in computational chemistry. Perspectives on Science, 28 (5). pp. 610-629. ISSN 1063-6145

Wilson, Joseph (2024) The ghost in the machine: Metaphors of the ‘virtual’ and the ‘artificial’ in post-WW2 computer science. Perspectives on Science, 32 (3). pp. 372-393. ISSN 1063-6145

Wolpert, David (2024) Implications of computer science theory for the simulation hypothesis. [Preprint]

Wronski, Leszek (2012) Branching Space-Times and Parallel Processing. H. Andersen et al. (eds.), New Challenges to Philosophy of Science, The Philosophy of Science in a European Perspective, 4. pp. 135-148.

Z

Zhang, Jianqiu (2024) What is Lacking in Sora and V-JEPA’s World Models? -A Philosophical Analysis of Video AIs Through the Theory of Productive Imagination. [Preprint]

Zhou, Nongjian (2024) Digital Analysis of Logical Equivalences. [Preprint]

This list was generated on Tue May 26 06:56:15 2026 EDT.