Unmasking Clever Hans predictors and assessing what machines really learn

Nonlinear machine learning methods have good predictive ability but the lack of transparency of the algorithms can limit their use. Here the authors investigate how these methods approach learning in order to assess the dependability of their decision making.

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Autores principales:	Sebastian Lapuschkin, Stephan Wäldchen, Alexander Binder, Grégoire Montavon, Wojciech Samek, Klaus-Robert Müller
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2019
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/ae85cf785d3645e68f9b5bfe27287566
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spelling	oai:doaj.org-article:ae85cf785d3645e68f9b5bfe272875662021-12-02T16:57:35ZUnmasking Clever Hans predictors and assessing what machines really learn10.1038/s41467-019-08987-42041-1723https://doaj.org/article/ae85cf785d3645e68f9b5bfe272875662019-03-01T00:00:00Zhttps://doi.org/10.1038/s41467-019-08987-4https://doaj.org/toc/2041-1723Nonlinear machine learning methods have good predictive ability but the lack of transparency of the algorithms can limit their use. Here the authors investigate how these methods approach learning in order to assess the dependability of their decision making.Sebastian LapuschkinStephan WäldchenAlexander BinderGrégoire MontavonWojciech SamekKlaus-Robert MüllerNature PortfolioarticleScienceQENNature Communications, Vol 10, Iss 1, Pp 1-8 (2019)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Sebastian Lapuschkin Stephan Wäldchen Alexander Binder Grégoire Montavon Wojciech Samek Klaus-Robert Müller Unmasking Clever Hans predictors and assessing what machines really learn
description	Nonlinear machine learning methods have good predictive ability but the lack of transparency of the algorithms can limit their use. Here the authors investigate how these methods approach learning in order to assess the dependability of their decision making.
format	article
author	Sebastian Lapuschkin Stephan Wäldchen Alexander Binder Grégoire Montavon Wojciech Samek Klaus-Robert Müller
author_facet	Sebastian Lapuschkin Stephan Wäldchen Alexander Binder Grégoire Montavon Wojciech Samek Klaus-Robert Müller
author_sort	Sebastian Lapuschkin
title	Unmasking Clever Hans predictors and assessing what machines really learn
title_short	Unmasking Clever Hans predictors and assessing what machines really learn
title_full	Unmasking Clever Hans predictors and assessing what machines really learn
title_fullStr	Unmasking Clever Hans predictors and assessing what machines really learn
title_full_unstemmed	Unmasking Clever Hans predictors and assessing what machines really learn
title_sort	unmasking clever hans predictors and assessing what machines really learn
publisher	Nature Portfolio
publishDate	2019
url	https://doaj.org/article/ae85cf785d3645e68f9b5bfe27287566
work_keys_str_mv	AT sebastianlapuschkin unmaskingcleverhanspredictorsandassessingwhatmachinesreallylearn AT stephanwaldchen unmaskingcleverhanspredictorsandassessingwhatmachinesreallylearn AT alexanderbinder unmaskingcleverhanspredictorsandassessingwhatmachinesreallylearn AT gregoiremontavon unmaskingcleverhanspredictorsandassessingwhatmachinesreallylearn AT wojciechsamek unmaskingcleverhanspredictorsandassessingwhatmachinesreallylearn AT klausrobertmuller unmaskingcleverhanspredictorsandassessingwhatmachinesreallylearn
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Unmasking Clever Hans predictors and assessing what machines really learn

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