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Approximate and Situated Causality in Deep Learning

Philosophy Department, Universitat Autònoma de Barcelona, 08193 Bellaterra (BCN), Spain
Philosophies 2020, 5(1), 2; https://doi.org/10.3390/philosophies5010002
Received: 5 July 2019 / Revised: 1 February 2020 / Accepted: 1 February 2020 / Published: 6 February 2020
(This article belongs to the Special Issue Philosophy and Epistemology of Deep Learning)
Causality is the most important topic in the history of western science, and since the beginning of the statistical paradigm, its meaning has been reconceptualized many times. Causality entered into the realm of multi-causal and statistical scenarios some centuries ago. Despite widespread critics, today deep learning and machine learning advances are not weakening causality but are creating a new way of finding correlations between indirect factors. This process makes it possible for us to talk about approximate causality, as well as about a situated causality. View Full-Text
Keywords: causality; deep learning; machine learning; counterfactual; explainable AI; blended cognition; mechanisms; system causality; deep learning; machine learning; counterfactual; explainable AI; blended cognition; mechanisms; system
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MDPI and ACS Style

Vallverdú, J. Approximate and Situated Causality in Deep Learning. Philosophies 2020, 5, 2. https://doi.org/10.3390/philosophies5010002

AMA Style

Vallverdú J. Approximate and Situated Causality in Deep Learning. Philosophies. 2020; 5(1):2. https://doi.org/10.3390/philosophies5010002

Chicago/Turabian Style

Vallverdú, Jordi. 2020. "Approximate and Situated Causality in Deep Learning" Philosophies 5, no. 1: 2. https://doi.org/10.3390/philosophies5010002

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