Number of items at this level: 142.
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]
Andrews, Mel
(2023)
The Devil in the Data: Machine Learning & the Theory-Free Ideal.
[Preprint]
B
Baltag, Alexandru and Smets, Sonja
(2004)
The Logic of Quantum Programs.
UNSPECIFIED.
(In Press)
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]
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
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
(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.
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]
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]
G
Gopnik, Alison
(2024)
Empowerment as Causal Learning, Causal Learning as Empowerment: A bridge between Bayesian causal hypothesis testing and reinforcement learning.
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.
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 Finn, Finola
(2024)
Can Generative AI Produce Novel Evidence?
In: UNSPECIFIED.
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.
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.
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.
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]
Ray, Faron
(2022)
Two Types of Explainability for Machine Learning Models.
In: UNSPECIFIED.
Rosenstock, Sarita
(2020)
Learning from the Shape of Data.
In: UNSPECIFIED.
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
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
(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]
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.
In: UNSPECIFIED.
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]
This list was generated on Thu Nov 21 04:24:34 2024 EST.