Number of items at this level: 121.
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]
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
(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.
Duede, Eamon
(2022)
Deep Learning Opacity in Scientific Discovery.
In: UNSPECIFIED.
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
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 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]
Krzanowski, Roman
(2017)
Minimal Information Structural Realism.
In: UNSPECIFIED.
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]
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
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
(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]
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)
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
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.
This list was generated on Thu Sep 21 19:20:45 2023 EDT.