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

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Human and Machine Learning

Part of the book series: Human–Computer Interaction Series ((HCIS))

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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Acknowledgements

This work was supported in part by AOARD under grant No. FA2386-14-1-0022 AOARD 134131.

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Authors and Affiliations

  1. DATA61, CSIRO, 13 Garden Street, Eveleigh, NSW, 2015, Australia

    Jianlong Zhou & Fang Chen

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  1. Jianlong Zhou
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Correspondence to Jianlong Zhou .

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  1. DATA61, CSIRO, Eveleigh, New South Wales, Australia

    Jianlong Zhou

  2. DATA61, CSIRO, Eveleigh, New South Wales, Australia

    Fang Chen

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Zhou, J., Chen, F. (2018). 2D Transparency Space—Bring Domain Users and Machine Learning Experts Together. In: Zhou, J., Chen, F. (eds) Human and Machine Learning. Human–Computer Interaction Series. Springer, Cham. https://doi.org/10.1007/978-3-319-90403-0_1

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

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-90402-3

  • Online ISBN: 978-3-319-90403-0

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