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Arsiwalla, Xerxes and Elshatlawy, Hatem and Rickles, Dean (2023) Pregeometry, Formal Language and Constructivist Foundations of Physics. [Preprint]

Barrett, Jeffrey A. (2015) On the Evolution of Truth. [Preprint]

Barrett, Jeffrey A. and Skyrms, Brian (2015) Self-Assembling Games. [Preprint]

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

Bartlett, Steven James (2015) THE SPECIES PROBLEM AND ITS LOGIC: Inescapable Ambiguity and Framework-relativity. [Preprint]

Bochman, Alexander (2023) Causal reasoning from almost first principles. [Preprint]

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Buckner, Cameron (2019) The Comparative Psychology of Artificial Intelligences. [Preprint]

Buckner, Cameron (2018) Empiricism without Magic: Transformational Abstraction in Deep Convolutional Neural Networks. [Preprint]

Buckner, Cameron (2023) From Deep Learning to Rational Machines -- What the History of Philosophy Can Teach Us about the Future of Artificial Intelligence -- Sample Chapter 1 -- "Moderate Empiricism and Machine Learning". [Preprint]

Buijsman, Stefan (2023) Causal scientific explanations from Machine Learning. [Preprint]

Buijsman, Stefan (2024) Machine Learning models as Mathematics: interpreting explainable AI in non-causal terms. [Preprint]

Coelho Mollo, Dimitri (2022) Intelligent Behaviour. [Preprint]

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Datteri, Edoardo (2020) Interactive Biorobotics. [Preprint]

Datteri, Edoardo (2016) The epistemic value of brain-machine systems for the study of the brain. [Preprint]

De Pretis, Francesco and Landes, Juergen and Peden, William and Osimani, Barbara (2021) Pharmacovigilance as Personalized Medicine. In: Chiara Beneduce and Marta Bertolaso (eds.) Personalized Medicine: A Multidisciplinary Approach to Complexity, Springer Nature. [Preprint]

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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]

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

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Gonçalves, Bernardo (2021) Can machines think? The controversy that led to the Turing test. [Preprint]

Greif, Hajo (2019) Exploring Minds. Modes of Modelling and Simulation in Artificial Intelligence. [Preprint]

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

Grujicic, Bojana and Illari, Phyllis (2023) Using deep neural networks and similarity metrics to predict and control brain responses. [Preprint]

Helman, Daniel (2022) Finding or Creating a Living Organism? Past and Future Thought Experiments in Astrobiology Applied to Artificial Intelligence. [Preprint]

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Kasirzadeh, Atoosa (2022) In conversation with Artificial Intelligence: aligning language models with human values. [Preprint]

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Koskinen, Inkeri (2023) We have no satisfactory social epistemology of AI-based science. [Preprint]

Kästner, Lena and Crook, Barnaby (2023) Don't Fear the Bogeyman: On Why There is No Prediction-Understanding Trade-Off for Deep Learning in Neuroscience. [Preprint]

LaCroix, Travis (2022) The Linguistic Blind Spot of Value-Aligned Agency, Natural and Artificial. [Preprint]

LaCroix, Travis (2022) Moral Dilemmas for Moral Machines. [Preprint]

List, Christian (2015) What is it like to be a group agent? [Preprint]

Livengood, Jonathan and Sytsma, Justin (2017) Empirical Investigations: Reflecting on Turing and Wittgenstein on Thinking Machines. [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]

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Miller, Ryan (2021) Does Artificial Intelligence Use Private Language? [Preprint]

Moruzzi, Caterina (2020) Measuring Creativity: An Account of Natural and Artificial Creativity. [Preprint]

Mussgnug, Alexander (2022) The Predictive Reframing of Machine Learning Applications: Good Predictions and Bad Measurements. [Preprint]

Müller, Thomas and Briegel, Hans (2016) Stochastic libertarianism: How to maintain integrity in action without determinism. [Preprint]

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

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

Pantsar, Markus and Fabry, Regina E. (2024) How Not to Talk about Chatbot Mistakes. [Preprint]

Patil, Kaustubh R. and Heinrichs, Bert (2022) Verifiability as a Complement to AI Explainability: A Conceptual Proposal. [Preprint]

Pedro, Leonardo (2023) Non-informative priors vs. Fermi Paradox and Artificial General Intelligence. [Preprint]

Peters, Uwe and Carman, Mary (2024) Cultural Bias in Explainable AI Research: A Systematic Analysis. [Preprint]

Peters, Uwe and Ojea Quintana, Ignacio (2024) Are Generics and Negativity about Social Groups Common on Social Media? – A Comparative Analysis of Twitter (X) Data. [Preprint]

Pietsch, Wolfgang (2017) A Causal Approach to Analogy. [Preprint]

Pigozzi, Gabriella (2006) Belief Merging and the Discursive Dilemma: An Argument-Based Account to Paradoxes of Judgment Aggregation. [Preprint]

Pigozzi, Gabriella (2005) Two aggregation paradoxes in social decision making: the Ostrogorski paradox and the discursive dilemma. [Preprint]

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

Ramstead, Maxwell J. D. and Kirchhoff, Michael D. and Friston, Karl J. (2019) A tale of two densities: Active inference is enactive inference. [Preprint]

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Rochefort-Maranda, Guillaume and Liu, Mo (2019) Finding True Clusters: On the Importance of Simplicity in Science. [Preprint]

Rushing, Bruce (2024) AI Safety Collides with the Overattribution Bias. [Preprint]

Räz, Tim (2016) The Necessity of Learning for Agency. [Preprint]

Saba, Walid (2019) On the Winograd Schema: Situating Language Understanding in the Data-Information-Knowledge Continuum. [Preprint]

Saint-Mont, Uwe (2016) Roads to Consciousness: Crucial steps in mental development. [Preprint]

Schubbach, Arno (2019) Judging Machines. Philosophical Aspects of Deep Learning. [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]

Steinert-Threlkeld, Shane (2019) Towards the Emergence of Non-trivial Compositionality. [Preprint]

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

Sterrett, Susan G. (1999) Turing's Two Tests for Intelligence. [Preprint]

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Stoica, Ovidiu Cristinel (2008) Convergence and free-will. [Preprint]

Sullivan, Emily (2023) Do ML models represent their targets? [Preprint]

Taddeo, Mariarosaria and Floridi, Luciano (2008) A Praxical Solution of the Symbol Grounding Problem. [Preprint]

Taddeo, Mariarosaria and Floridi, Luciano (2005) Solving the Symbol Grounding Problem: a Critical Review of Fifteen Years of Research. [Preprint]

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Thagard, Paul (2021) Energy Requirements Undermine Substrate Independence and Mind-Body Functionalism. [Preprint]

Thobani, Imran (2024) A Triviality Worry for the Internal Model Principle. [Preprint]

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Veit, Walter and Browning, Heather (2023) Defending Sentientism. [Preprint]

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Wallace, Rodrick (2006) New mathematical foundations for AI and Alife: Are the necessary conditions for animal consciousness sufficient for the design of intelligent machines? [Preprint]

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Conference or Workshop Item

Boccignone, Giuseppe and Cordeschi, Roberto (2007) Bayesian models and simulations in cognitive science. In: UNSPECIFIED.

Dawidowicz, Edward and Jackson, Vairzora and Bryant, Thomas E and Adams, Martin (2003) The Right Information… and Intelligent Nodes. In: UNSPECIFIED.

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

Eberhardt, Frederick and Scheines, Richard (2006) Interventions and Causal Inference. In: UNSPECIFIED.

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

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

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 (2016) A difference-making account of causation. In: UNSPECIFIED.

Ratti, Emanuele and López-Rubio, Ezequiel (2018) Mechanistic Models and the Explanatory Limits of Machine Learning. In: UNSPECIFIED.

Rushing, Bruce and Gomez-Lavin, Javier (2024) Is the Scaling Hypothesis Falsifiable? 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

Bodanza, Gustavo Adrián (2015) Abstract Argumentation in Artificial Intelligence. Problems of Interpretation and Adequacy of Semantics for Decision Making. THEORIA. An International Journal for Theory, History and Foundations of Science, 30 (3). pp. 395-414. ISSN 2171-679X

Colaço, David (2024) When remediating one artifact results in another: control, confounders, and correction. History and Philosophy of the Life Sciences, 46 (5).

Friederich, Simon (2023) Symbiosis, not alignment, as the goal for liberal democracies in the transition to artificial general intelligence. AI and Ethics.

Friederich, Simon and Symons, Jonathan (2023) Norms for Academic Writing in the Era of Advanced Artificial Intelligence. Digital Society, 2.

Gao, Shan (2023) Does quantum cognition imply quantum minds? Journal of Consciousness Studies, 28 (3-4). pp. 100-111.

Gonçalves, Bernardo (2021) Machines will think: structure and interpretation of Alan Turing’s imitation game. The Digital Library of Theses and Dissertations of the University of São Paulo. pp. 1-291.

Graves, Mark and Ratti, Emanuele (2022) Who Is a Good Data Scientist? A Reply to Curzer and Epstein. Philosophy & Technology, 35.

Kasirzadeh, Atoosa (2022) Taxonomy of Risks posed by Language Models. FAccT '22: 2022 ACM Conference on Fairness, Accountability, and Transparency. pp. 214-229.

Ling, Maurice HT (2012) Re-creating the Philosopher’s Mind: Artificial Life from Artificial Intelligence. Human-Level Intelligence, 3. p. 1. ISSN 2219-5173

List, Christian (2021) Group Agency and Artificial Intelligence. Philosophy & Technology, Online.

Miller, Ryan (2023) Holding Large Language Models to Account. Proceedings of the AISB Convention 2023. pp. 7-14.

Miłkowski, Marcin (2017) Situatedness and Embodiment of Computational Systems. Entropy, 19 (4). p. 162. ISSN 1099-4300

Otsuka, Jun (2021) Why does statistics matter to philosophy? Tetsugaku-Kenkyu (The Journal of Philosophical Studies), 606. pp. 1-24. ISSN 0386-9563

Plutniak, Sébastien (2017) Is an archaeological contribution to the theory of social science possible? Archaeological data and concepts in the dispute between Jean-Claude Gardin and Jean-Claude Passeron. Palethnologie, 9. pp. 7-21.

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

Reynolds, Jack (2024) Framing the Predictive Mind: Why we should think again about Dreyfus. Phenomenology and the cognitive sciences. ISSN 1572-8676

Sarma, Gopal P. and Faundez, Victor (2017) Integrative biological simulation praxis: Considerations from physics, philosophy, and data/model curation practices. Cellular Logistics, 7 (4).

Sarma, Gopal P. and Hay, Nick J. (2017) Mammalian Value Systems. Informatica, 41 (4). pp. 441-449.

Sarma, Gopal P. and Hay, Nick J. (2017) Mammalian Value Systems. Informatica, 41 (4). pp. 441-449.

Sarma, Gopal P. and Hay, Nick J. (2017) Robust Computer Algebra, Theorem Proving, and Oracle AI. Informatica, 41 (4). pp. 451-461.

Sterkenburg, Tom F. (2018) Universal Prediction.

Tamir, Michael and Elay, Shech (2023) Machine Understanding and Deep Learning Representation. Synthese, 201 (51). ISSN 1573-0964

Votsis, Ioannis (2016) Ad Hoc Hypotheses and the Monsters within. Fundamental Issues of Artificial Intelligence. pp. 299-313.

Open Access Book

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

Other

Vaas, Ruediger (2006) Dark Energy And Life's Ultimate Future. UNSPECIFIED.