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Erschienen in:
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2018 | OriginalPaper | Buchkapitel

1. 2D Transparency Space—Bring Domain Users and Machine Learning Experts Together

verfasst von : Jianlong Zhou, Fang Chen

Erschienen in: Human and Machine Learning

Verlag: Springer International Publishing

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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.

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Metadaten
Titel
2D Transparency Space—Bring Domain Users and Machine Learning Experts Together
verfasst von
Jianlong Zhou
Fang Chen
Copyright-Jahr
2018
Buch
Human and Machine Learning

Print ISBN: 978-3-319-90402-3

Electronic ISBN: 978-3-319-90403-0

Copyright-Jahr: 2018

DOI
https://doi.org/10.1007/978-3-319-90403-0_1

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