Approximate and Situated Causality in Deep Learning
Vallverdú, Jordi 1973- (Universitat Autònoma de Barcelona. Departament de Filosofia)

Date:	2020
Abstract:	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.
Grants:	Ministerio de Ciencia e Innovación FFI2017-85711-P Agència de Gestió d'Ajuts Universitaris i de Recerca 2017/SGR-568 European Commission 73169
Note:	Altres ajuts: ICREA Academia 2019, and "AppPhil: Applied Philosophy for the Value-Design of Social Networks Apps" project, funded by Caixabank in Recercaixa2017.
Rights:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.
Language:	Anglès
Document:	Article ; recerca ; Versió publicada
Subject:	Causality ; Deep learning ; Machine learning ; Counterfactual ; Explainable AI ; Blended cognition ; Mechanisms ; System
Published in:	Philosophies, Vol. 5 Núm. 1 (2020) , p. 2, ISSN 2409-9287

DOI: 10.3390/philosophies5010002

12 p, 2.5 MB

The record appears in these collections:
Articles > Research articles
Articles > Published articles