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

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

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

Barrett, Jeffrey A. and Chen, Eddy Keming (2023) Algorithmic Randomness and Probabilistic Laws. [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]

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

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

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]

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

Coelho Mollo, Dimitri (2019) Are There Teleological Functions to Compute? [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 (2022) The ~In~Determinacy of Computation. [Preprint]

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

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

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

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

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]

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

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]

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

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]

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]

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

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]

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

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

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

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]

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]

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

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

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

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

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

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]

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

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

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

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

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

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

Sterner, Beckett and Witteveen, Joeri and Franz, Nico (2019) Alternatives to Realist Consensus in Bio-Ontologies: Taxonomic Classification as a Basis for Data Discovery and Integration. [Preprint]

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]

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

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]

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

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]

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

Conference or Workshop Item

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

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

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

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

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

Gopnik, Alison (2024) Empowerment as Causal Learning, Causal Learning as Empowerment: A bridge between Bayesian causal hypothesis testing and reinforcement learning. In: UNSPECIFIED.

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

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

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

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

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

Wilson, Joseph (2024) The ghost in the machine: Metaphors of the ‘virtual’ and the ‘artificial’ in post-WW2 computer science. In: UNSPECIFIED.

Published Article or Volume

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

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

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.

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

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.

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.

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.

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

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

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

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.

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

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

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

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. (2021) The No-Free-Lunch Theorems of Supervised Learning. Synthese.

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

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

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

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.

Open Access Book

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

Other

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

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

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

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