Human-Centred Machine Learning
Workshop Summaries
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Gillies, Marco
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Fiebrink, Rebecca
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Tanaka, Atau
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Garcia, Jérémie
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Bevilacqua, Frédéric
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Heloir, Alexis
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Nunnari, Fabrizio
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Mackay, Wendy
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Amershi, Saleema
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Lee, Bongshin
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d'Alessandro, Nicolas
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Tilmanne, Joëlle
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Kulesza, Todd
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Caramiaux, Baptiste
Extended Abstracts of the ACM CHI'16 Conference on Human Factors in
Computing Systems
2016-05-07
v.2
p.3558-3565
© Copyright 2016 ACM
Summary: Machine learning is one of the most important and successful techniques in
contemporary computer science. It involves the statistical inference of models
(such as classifiers) from data. It is often conceived in a very impersonal
way, with algorithms working autonomously on passively collected data. However,
this viewpoint hides considerable human work of tuning the algorithms,
gathering the data, and even deciding what should be modeled in the first
place. Examining machine learning from a human-centered perspective includes
explicitly recognising this human work, as well as reframing machine learning
workflows based on situated human working practices, and exploring the
co-adaptation of humans and systems. A human-centered understanding of machine
learning in human context can lead not only to more usable machine learning
tools, but to new ways of framing learning computationally. This workshop will
bring together researchers to discuss these issues and suggest future research
questions aimed at creating a human-centered approach to machine learning.