Human-Centred Machine Learning

Marco Gillies; Rebecca Fiebrink; Atau Tanaka; Jérémie Garcia; Frédéric Bevilacqua; Alexis Heloir; Fabrizio Nunnari; Wendy Mackay; Saleema Amershi; Bongshin Lee; Nicolas d'Alessandro; Joëlle Tilmanne; Todd Kulesza; Baptiste Caramiaux
In: Proceedings of the 2016 CHI Conference Extended Abstracts on Human Factors in Computing Systems. ACM International Conference on Human Factors in Computing Systems (CHI-16), May 7-12, San Jose, CA, USA, Pages 3558-3565, ISBN 978-1-4503-4082-3, ACM, 2016.


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 coadaptation 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.

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