PhilSci Archive

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

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Number of items at this level: 40.

B

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

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]

C

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

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

D

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

F

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

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

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

G

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.

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

J

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

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.

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

L

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

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]

N

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

R

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

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

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

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

S

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

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.

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

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

T

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

Z

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

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

This list was generated on Wed Dec 1 13:06:28 2021 EST.