Preprint Article Version 1 This version is not peer-reviewed

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)

How to cite: Vallverdú, J. Approximate and Situated Causality in Deep Learning. Preprints 2019, 2019070110 (doi: 10.20944/preprints201907.0110.v1). Vallverdú, J. Approximate and Situated Causality in Deep Learning. Preprints 2019, 2019070110 (doi: 10.20944/preprints201907.0110.v1).

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.

Subject Areas

causality; deep learning; machine learning; counterfactual; explainable AI; blended cognition; mechanisms; system

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