A Review of Recent Deep Learning Approaches in Human-Centered Machine Learning

After Deep Learning (DL) regained popularity recently, the Artificial Intelligence (AI) or Machine Learning (ML) field is undergoing rapid growth concerning research and real-world application development. Deep Learning has generated complexities in algorithms, and researchers and users have raised...

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Main Authors:	Tharindu Kaluarachchi, Andrew Reis, Suranga Nanayakkara
Format:	Article
Language:	English
Published:	MDPI AG 2021-04-01
Series:	Sensors
Subjects:	human-centered machine learning HCML HCAI human-centered artificial intelligence Deep Learning
Online Access:	https://www.mdpi.com/1424-8220/21/7/2514

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spelling	doaj-3994b8697cef4978a6c6d851023dbcd42021-04-03T23:03:09ZengMDPI AGSensors1424-82202021-04-01212514251410.3390/s21072514A Review of Recent Deep Learning Approaches in Human-Centered Machine LearningTharindu Kaluarachchi0Andrew Reis1Suranga Nanayakkara2Augmented Human Lab, Auckland Bioengineering Institue, The University of Auckland, 70 Symonds Street, Grafton, Auckland 1010, New ZealandAugmented Human Lab, Auckland Bioengineering Institue, The University of Auckland, 70 Symonds Street, Grafton, Auckland 1010, New ZealandAugmented Human Lab, Auckland Bioengineering Institue, The University of Auckland, 70 Symonds Street, Grafton, Auckland 1010, New ZealandAfter Deep Learning (DL) regained popularity recently, the Artificial Intelligence (AI) or Machine Learning (ML) field is undergoing rapid growth concerning research and real-world application development. Deep Learning has generated complexities in algorithms, and researchers and users have raised concerns regarding the usability and adoptability of Deep Learning systems. These concerns, coupled with the increasing human-AI interactions, have created the emerging field that is Human-Centered Machine Learning (HCML). We present this review paper as an overview and analysis of existing work in HCML related to DL. Firstly, we collaborated with field domain experts to develop a working definition for HCML. Secondly, through a systematic literature review, we analyze and classify 162 publications that fall within HCML. Our classification is based on aspects including contribution type, application area, and focused human categories. Finally, we analyze the topology of the HCML landscape by identifying research gaps, highlighting conflicting interpretations, addressing current challenges, and presenting future HCML research opportunities.https://www.mdpi.com/1424-8220/21/7/2514human-centered machine learningHCMLHCAIhuman-centered artificial intelligenceDeep Learning
collection	DOAJ
language	English
format	Article
sources	DOAJ
author	Tharindu Kaluarachchi Andrew Reis Suranga Nanayakkara
spellingShingle	Tharindu Kaluarachchi Andrew Reis Suranga Nanayakkara A Review of Recent Deep Learning Approaches in Human-Centered Machine Learning Sensors human-centered machine learning HCML HCAI human-centered artificial intelligence Deep Learning
author_facet	Tharindu Kaluarachchi Andrew Reis Suranga Nanayakkara
author_sort	Tharindu Kaluarachchi
title	A Review of Recent Deep Learning Approaches in Human-Centered Machine Learning
title_short	A Review of Recent Deep Learning Approaches in Human-Centered Machine Learning
title_full	A Review of Recent Deep Learning Approaches in Human-Centered Machine Learning
title_fullStr	A Review of Recent Deep Learning Approaches in Human-Centered Machine Learning
title_full_unstemmed	A Review of Recent Deep Learning Approaches in Human-Centered Machine Learning
title_sort	review of recent deep learning approaches in human-centered machine learning
publisher	MDPI AG
series	Sensors
issn	1424-8220
publishDate	2021-04-01
description	After Deep Learning (DL) regained popularity recently, the Artificial Intelligence (AI) or Machine Learning (ML) field is undergoing rapid growth concerning research and real-world application development. Deep Learning has generated complexities in algorithms, and researchers and users have raised concerns regarding the usability and adoptability of Deep Learning systems. These concerns, coupled with the increasing human-AI interactions, have created the emerging field that is Human-Centered Machine Learning (HCML). We present this review paper as an overview and analysis of existing work in HCML related to DL. Firstly, we collaborated with field domain experts to develop a working definition for HCML. Secondly, through a systematic literature review, we analyze and classify 162 publications that fall within HCML. Our classification is based on aspects including contribution type, application area, and focused human categories. Finally, we analyze the topology of the HCML landscape by identifying research gaps, highlighting conflicting interpretations, addressing current challenges, and presenting future HCML research opportunities.
topic	human-centered machine learning HCML HCAI human-centered artificial intelligence Deep Learning
url	https://www.mdpi.com/1424-8220/21/7/2514
work_keys_str_mv	AT tharindukaluarachchi areviewofrecentdeeplearningapproachesinhumancenteredmachinelearning AT andrewreis areviewofrecentdeeplearningapproachesinhumancenteredmachinelearning AT surangananayakkara areviewofrecentdeeplearningapproachesinhumancenteredmachinelearning AT tharindukaluarachchi reviewofrecentdeeplearningapproachesinhumancenteredmachinelearning AT andrewreis reviewofrecentdeeplearningapproachesinhumancenteredmachinelearning AT surangananayakkara reviewofrecentdeeplearningapproachesinhumancenteredmachinelearning
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A Review of Recent Deep Learning Approaches in Human-Centered Machine Learning

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