2D Transparency Space—Bring Domain Users and Machine Learning Experts Together

Zhou, J; Chen, F

2D Transparency Space—Bring Domain Users and Machine Learning Experts Together

Zhou, J

Chen, F

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Publisher:: Springer
Publication Type:: Chapter
Citation:: Human and Machine Learning, 2018, pp. 3 - 19
Issue Date:: 2018

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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	en_US
dc.identifier.citation	Human and Machine Learning, 2018, pp. 3 - 19	en_US
dc.identifier.isbn	978-3-319-90402-3	en_US
dc.identifier.uri	http://hdl.handle.net/10453/132790
dc.description.abstract	Machine Learning (ML) is currently facing prolonged challenges with the user acceptance of delivered solutions as well as seeing system misuse, disuse, or even failure. These fundamental challenges can be attributed to the nature of the “black-box” of ML methods for domain users when offering ML-based solutions. That is, transparency of ML is essential for domain users to trust and use ML confidently in their practices. This chapter argues for a change in how we view the relationship between human and machine learning to translate ML results into impact. We present a two-dimensional transparency space which integrates domain users and ML experts together to make ML transparent. We identify typical Transparent ML (TML) challenges and discuss key obstacles to TML, which aim to inspire active discussions of making ML transparent with a systematic view in this timely field.	en_US
dc.publisher	Springer	en_US
dc.relation.ispartof	Human and Machine Learning	en_US
dc.relation.isbasedon	10.1007/978-3-319-90403-0_1	en_US
dc.title	2D Transparency Space—Bring Domain Users and Machine Learning Experts Together	en_US
dc.type	Chapter
utslib.location	Germany	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	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.consider-herdc	false	en_US
pubs.place-of-publication	Germany	en_US
pubs.publication-status	Published	en_US

Abstract:

Machine Learning (ML) is currently facing prolonged challenges with the user acceptance of delivered solutions as well as seeing system misuse, disuse, or even failure. These fundamental challenges can be attributed to the nature of the “black-box” of ML methods for domain users when offering ML-based solutions. That is, transparency of ML is essential for domain users to trust and use ML confidently in their practices. This chapter argues for a change in how we view the relationship between human and machine learning to translate ML results into impact. We present a two-dimensional transparency space which integrates domain users and ML experts together to make ML transparent. We identify typical Transparent ML (TML) challenges and discuss key obstacles to TML, which aim to inspire active discussions of making ML transparent with a systematic view in this timely field.

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

http://hdl.handle.net/10453/132790