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

Szczegóły
Abstrakt

Tytuł:: A Review of Recent Deep Learning Approaches in Human-Centered Machine Learning.
Autorzy:: Kaluarachchi T; Augmented Human Lab, Auckland Bioengineering Institue, The University of Auckland, 70 Symonds Street, Grafton, Auckland 1010, New Zealand.
Reis A; Augmented Human Lab, Auckland Bioengineering Institue, The University of Auckland, 70 Symonds Street, Grafton, Auckland 1010, New Zealand.
Nanayakkara S; Augmented Human Lab, Auckland Bioengineering Institue, The University of Auckland, 70 Symonds Street, Grafton, Auckland 1010, New Zealand.
Źródło:: Sensors (Basel, Switzerland) [Sensors (Basel)] 2021 Apr 03; Vol. 21 (7). Date of Electronic Publication: 2021 Apr 03.
Typ publikacji:: Journal Article; Review; Systematic Review
Język:: English
Imprint Name(s):: Original Publication: Basel, Switzerland : MDPI, c2000-
MeSH Terms:: Artificial Intelligence*
Deep Learning*
Algorithms ; Humans ; Machine Learning
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Grant Information:: 3716751 New Zealand Government
Contributed Indexing:: Keywords: Deep Learning; HCAI; HCML; human-centered artificial intelligence; human-centered machine learning
Entry Date(s):: Date Created: 20210430 Date Completed: 20210503 Latest Revision: 20231111
Update Code:: 20240104
PubMed Central ID:: PMC8038476
DOI:: 10.3390/s21072514
PMID:: 33916850
: Czasopismo naukowe

Pełny tekst

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.

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