DecisionMind: revealing human cognition states in data analytics-driven decision making with a multimodal interface

Zhou, J; Chen, F

DecisionMind: revealing human cognition states in data analytics-driven decision making with a multimodal interface

Zhou, J

Chen, F

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Publication Type:: Journal Article
Citation:: Journal on Multimodal User Interfaces, 2018, 12 (2), pp. 67 - 76
Issue Date:: 2018-06-01

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Field	Value	Language
dc.contributor.author	Zhou, J https://orcid.org/0000-0001-6034-644X	en_US
dc.contributor.author	Chen, F https://orcid.org/0000-0003-4971-8729	en_US
dc.date.issued	2018-06-01	en_US
dc.identifier.citation	Journal on Multimodal User Interfaces, 2018, 12 (2), pp. 67 - 76	en_US
dc.identifier.issn	1783-7677	en_US
dc.identifier.uri	http://hdl.handle.net/10453/131805
dc.description.abstract	© 2017, Springer International Publishing AG. Despite the recognized value of machine learning (ML) techniques and high expectation of applying ML techniques within various applications, significant barriers to widespread adoption and local implementation of ML approaches still exist in the areas of trust (of ML results), comprehension (of ML processes) and related workload, as well as confidence (in decision making based on ML results) by users. This paper argues that the revealing of human cognition states with a multimodal interface during ML-based data analytics-driven decision making could provide a rich view for both ML researchers and domain experts to learn the effectiveness of ML technologies in applications. On the one hand, human cognition states could help understand to what degree users accept innovative technologies. On the other hand, through understanding human cognition states during data analytics-driven decision making, ML-based decision attributes and even ML models can be adaptively refined in order to make ML transparent. The paper also identifies examples of impact challenges and obstacles, as well as high-demand research directions in making ML transparent.	en_US
dc.relation.ispartof	Journal on Multimodal User Interfaces	en_US
dc.relation.isbasedon	10.1007/s12193-017-0249-8	en_US
dc.title	DecisionMind: revealing human cognition states in data analytics-driven decision making with a multimodal interface	en_US
dc.type	Journal Article
utslib.citation.volume	2	en_US
utslib.citation.volume	12	en_US
utslib.for	0903 Biomedical Engineering	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	0806 Information Systems	en_US
utslib.for	1203 Design Practice and Management	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access
pubs.issue	2	en_US
pubs.publication-status	Published	en_US
pubs.volume	12	en_US

Abstract:

© 2017, Springer International Publishing AG. Despite the recognized value of machine learning (ML) techniques and high expectation of applying ML techniques within various applications, significant barriers to widespread adoption and local implementation of ML approaches still exist in the areas of trust (of ML results), comprehension (of ML processes) and related workload, as well as confidence (in decision making based on ML results) by users. This paper argues that the revealing of human cognition states with a multimodal interface during ML-based data analytics-driven decision making could provide a rich view for both ML researchers and domain experts to learn the effectiveness of ML technologies in applications. On the one hand, human cognition states could help understand to what degree users accept innovative technologies. On the other hand, through understanding human cognition states during data analytics-driven decision making, ML-based decision attributes and even ML models can be adaptively refined in order to make ML transparent. The paper also identifies examples of impact challenges and obstacles, as well as high-demand research directions in making ML transparent.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/131805