Abstract
Machine learning is one of the most important and successful techniques in contemporary computer science. Although it can be applied to myriad problems of human interest, research in machine learning is often framed in an impersonal way, as merely algorithms being applied to model data. However, this viewpoint hides considerable human work of tuning the algorithms, gathering the data, deciding what should be modeled in the first place, and using the outcomes of machine learning in the real world. Examining machine learning from a human-centered perspective includes explicitly recognizing human work, as well as reframing machine learning workflows based on situated human working practices, and exploring the co-adaptation of humans and intelligent systems. A human-centered understanding of machine learning in human contexts can lead not only to more usable machine learning tools, but to new ways of understanding what machine learning is good for and how to make it more useful. This special issue brings together nine articles that present different ways to frame machine learning in a human context. They represent very different application areas (from medicine to audio) and methodologies (including machine learning methods, human-computer interaction methods, and hybrids), but they all explore the human contexts in which machine learning is used. This introduction summarizes the articles in this issue and draws out some common themes.
- Alan F. Blackwell. 2015. HCI as an inter-discipline. In Proceedings of the 33rd Annual ACM Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA’15). ACM, New York, NY, 503--516. Google ScholarDigital Library
- 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, and Baptiste Caramiaux. 2016. Human-centred machine learning. In Proceedings of the 2016 CHI Conference Extended Abstracts on Human Factors in Computing Systems. ACM, 3558--3565. Google ScholarDigital Library
Index Terms
- Introduction to the Special Issue on Human-Centered Machine Learning
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