Crossref Citations
This article has been cited by the following publications. This list is generated based on data provided by
Crossref.
Watson, David
2020.
Conceptual Challenges for Interpretable Machine Learning.
SSRN Electronic Journal,
Karaca, Koray
2021.
Values and inductive risk in machine learning modelling: the case of binary classification models.
European Journal for Philosophy of Science,
Vol. 11,
Issue. 4,
Liu, Jen
and
Sengers, Phoebe
2021.
Legibility and the Legacy of Racialized Dispossession in Digital Agriculture.
Proceedings of the ACM on Human-Computer Interaction,
Vol. 5,
Issue. CSCW2,
p.
1.
Erasmus, Adrian
Brunet, Tyler D. P.
and
Fisher, Eyal
2021.
What is Interpretability?.
Philosophy & Technology,
Vol. 34,
Issue. 4,
p.
833.
Dupre, Gabe
2021.
(What) Can Deep Learning Contribute to Theoretical Linguistics?.
Minds and Machines,
Vol. 31,
Issue. 4,
p.
617.
López-Rubio, Ezequiel
2021.
Throwing light on black boxes: emergence of visual categories from deep learning.
Synthese,
Vol. 198,
Issue. 10,
p.
10021.
Long, Robert
2021.
Fairness in Machine Learning: Against False Positive Rate Equality as a Measure of Fairness.
Journal of Moral Philosophy,
Vol. 19,
Issue. 1,
p.
49.
Stacewicz, Paweł
and
Greif, Hajo
2021.
Concepts as decision functions. The issue of epistemic opacity of conceptual representations in artificial computing systems.
Procedia Computer Science,
Vol. 192,
Issue. ,
p.
4120.
Fleisher, Will
2021.
Understanding, Idealization, and Explainable AI.
SSRN Electronic Journal,
Fazelpour, Sina
and
Danks, David
2021.
Algorithmic bias: Senses, sources, solutions.
Philosophy Compass,
Vol. 16,
Issue. 8,
Fleisher, Will
2022.
Understanding, Idealization, and Explainable AI.
Episteme,
Vol. 19,
Issue. 4,
p.
534.
Olague, Gustavo
Menendez-Clavijo, Jose Armando
Olague, Matthieu
Ocampo, Arturo
Ibarra-Vazquez, Gerardo
Ochoa, Rocio
and
Pineda, Roberto
2022.
Automated Design of Salient Object Detection Algorithms with Brain Programming.
Applied Sciences,
Vol. 12,
Issue. 20,
p.
10686.
Vredenburgh, Kate
2022.
The Right to Explanation*.
Journal of Political Philosophy,
Vol. 30,
Issue. 2,
p.
209.
Martínez-Ordaz, María del Rosario
2022.
Philosophy of Computing.
Vol. 143,
Issue. ,
p.
113.
Creel, Kathleen
and
Hellman, Deborah
2022.
The Algorithmic Leviathan: Arbitrariness, Fairness, and Opportunity in Algorithmic Decision-Making Systems.
Canadian Journal of Philosophy,
Vol. 52,
Issue. 1,
p.
26.
Boge, Florian J.
2022.
Two Dimensions of Opacity and the Deep Learning Predicament.
Minds and Machines,
Vol. 32,
Issue. 1,
p.
43.
Grote, Thomas
and
Keeling, Geoff
2022.
Enabling Fairness in Healthcare Through Machine Learning.
Ethics and Information Technology,
Vol. 24,
Issue. 3,
Vredenburgh, Kate
2022.
Freedom at Work: Understanding, Alienation, and the AI-Driven Workplace.
Canadian Journal of Philosophy,
Vol. 52,
Issue. 1,
p.
78.
Watson, David S.
2022.
Conceptual challenges for interpretable machine learning.
Synthese,
Vol. 200,
Issue. 2,
Greif, Hajo
2022.
Analogue Models and Universal Machines. Paradigms of Epistemic Transparency in Artificial Intelligence.
Minds and Machines,
Vol. 32,
Issue. 1,
p.
111.