PhilSci Archive

Items where Subject is "Specific Sciences > Artificial Intelligence > Machine Learning"

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Group by: Creators | Item Type
Number of items at this level: 53.

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]

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]

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

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

Facchini, Alessandro and Termine, Alberto (2021) A First Contextual Taxonomy for the Opacity of AI Systems. [Preprint]

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

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

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

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]

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.

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

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 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.

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 (2019) Understanding from Machine Learning Models. British Journal for the Philosophy of Science. ISSN 1464-3537

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

This list was generated on Tue Jun 28 17:59:23 2022 EDT.