Preprint

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

Anderson, Michael L and Champion, Heather (2021) Some dilemmas for an account of neural representation: A reply to Poldrack. [Preprint]

Andrews, Mel (2022) Making Reification Concrete: A Response to Bruineberg et al. [Preprint]

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

Boge, Florian J. and Grünke, Paul (2019) Computer simulations, machine learning and the Laplacean demon: Opacity in the case of high energy physics∗. [Preprint]

Buchholz, Oliver (2023) The Deep Neural Network Approach to the Reference Class Problem. [Preprint]

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

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

Davies-Barton, Tyeson and Raja, Vicente and Baggs, Edward and Anderson, Michael L (2022) Debt-free intelligence: Ecological information in minds and machines. [Preprint]

Dotan, Ravit (2020) Theory Choice, Non-epistemic Values, and Machine Learning. [Preprint]

Duede, Eamon (2022) Instruments, Agents, and Artificial Intelligence: Novel Epistemic Categories of Reliability. [Preprint]

Facchin, Marco (2021) Are generative models structural representations? [Preprint]

Facchin, Marco (2021) Troubles with mathematical contents. [Preprint]

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

Gonzalez-Cabrera, Ivan (2022) A Lineage Explanation of Human Normative Guidance: The Coadaptive Model of Instrumental Rationality and Shared Intentionality. [Preprint]

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

Jebari, Karim and Lundborg, Joakim (2019) Artificial superintelligence and its limits: why AlphaZero cannot become a general agent. [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]

K. Yee, Adrian (2023) Information Deprivation and Democratic Engagement. [Preprint]

Korbak, Tomasz (2019) Unsupervised learning and the natural origins of content. [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]

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

López-Rubio, Ezequiel (2020) Throwing light on black boxes: emergence of visual categories from deep learning. [Preprint]

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

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

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

Noichl, Maximilian (2019) Modeling the Structure of Recent Philosophy. [Preprint]

Otsuka, Jun and Saigo, Hayato (2022) The process theory of causality: an overview. [Preprint]

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

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

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

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

Shech, Elay and Tamir, Michael (2023) Understanding from Deep Learning Models in Context. [Preprint]

Sprevak, Mark (2021) Predictive coding I: Introduction. [Preprint]

Sprevak, Mark (2021) Predictive coding II: The computational level. [Preprint]

Sprevak, Mark (2021) Predictive coding III: The algorithmic level. [Preprint]

Sprevak, Mark (2021) Predictive coding IV: The implementation level. [Preprint]

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

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

Sullivan, Emily (2022) Inductive Risk, Understanding, and Opaque Machine Learning Models. [Preprint]

Thompson, Jessica A. F. (2018) Towards a common theory of explanation for artificial and biological intelligence. [Preprint]

Yao, Siyu and Hagar, Amit (2023) Who Makes the Choice? Artificial Neural Networks in Science and Non-Uniqueness. [Preprint]

Zerilli, John (2020) Explaining machine learning decisions. [Preprint]

Conference or Workshop Item

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

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

Zednik, Carlos and Boelsen, Hannes (2020) The Exploratory Role of Explainable Artificial Intelligence. In: UNSPECIFIED.

Published Article or Volume

Babic, Boris and Gerke, Sara and Evgeniou, Theodoros and Cohen, Glenn (2019) Algorithms on Regulatory Lockdown in Medicine. Science.

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

Cabrera, Frank (2020) Correlation Isn’t Good Enough: Causal Explanation and Big Data. Metascience. ISSN 0815-0796

Casacuberta, David and Estany, Anna (2019) Convergence between experiment and theory in the processes of invention and innovation. THEORIA. An International Journal for Theory, History and Foundations of Science, 34 (3). pp. 373-387. ISSN 2171-679X

Climenhaga, Nevin (2019) The Structure of Epistemic Probabilities. Philosophical Studies. pp. 1-30. ISSN 0031-8116

Facchin, Marco (2021) Are generative models structural representations?

Gerke, Sara and Babic, Boris and Evgeniou, Theodoros and Cohen, Glenn (2020) The need for a system view to regulate artificial intelligence/machine learning-based software as medical device. Nature Digital Medicine.

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.

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

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

Ratti, Emanuele and Graves, Mark (2021) Cultivating Moral Attention: A Virtue-oriented Approach to Responsible Data Science in Healthcare. Philosophy & Technology.

Ratti, Emanuele and Graves, Mark (2022) Explainable machine learning practices: opening another black box for reliable medical AI. AI and Ethics.

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. and De Heide, Rianne (2021) On the Truth-Convergence of Open-Minded Bayesianism. The Review of Symbolic Logic.

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

Sullivan, Emily (2022) How Values Shape the Machine Learning Opacity Problem. Scientific Understanding and Representation (Eds) Insa Lawler, Kareem Khalifa & Elay Shech. pp. 306-322.

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

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

Woodward, James (2022) Flagpoles anyone? Causal and explanatory asymmetries. THEORIA. An International Journal for Theory, History and Foundations of Science, 37 (1). pp. 7-52. ISSN 2171-679X