Article
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Approximate and Situated Causality in Deep Learning
Version 1
: Received: 5 July 2019 / Approved: 8 July 2019 / Online: 8 July 2019 (08:10:29 CEST)
A peer-reviewed article of this Preprint also exists.
Vallverdú, J. Approximate and Situated Causality in Deep Learning. Philosophies 2020, 5, 2. Vallverdú, J. Approximate and Situated Causality in Deep Learning. Philosophies 2020, 5, 2.
Abstract
Causality is the most important topic in the history of Western Science, and since the beginning of the statistical paradigm, it meaning has been reconceptualized many times. Causality entered into the realm of multi-causal and statistical scenarios some centuries ago. Despite of widespread critics, today Deep Learning and Machine Learning advances are not weakening causality but are creating a new way of finding indirect factors correlations. This process makes possible us to talk about approximate causality, as well as about a situated causality.
Keywords
causality; deep learning; machine learning; counterfactual; explainable AI; blended cognition; mechanisms; system
Subject
Arts and Humanities, Philosophy
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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