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Goldsmiths - University of London

Introduction to the Special Issue on Human-Centered Machine Learning

Fiebrink, Rebecca and Gillies, Marco. 2018. Introduction to the Special Issue on Human-Centered Machine Learning. ACM Transactions on Interactive Intelligent Systems, 8(2), ISSN 2160-6455 [Article] (In Press)

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Abstract or Description

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 papers 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, HCI methods, and hybrids), but they all explore the human contexts in which machine learning is used. This introduction summarizes the papers in this issue and draws out some common themes.

Item Type: Article

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Departments, Centres and Research Units:

Computing
Computing > Embodied AudioVisual Interaction Group (EAVI)

Dates:

DateEvent
6 April 2018Accepted

Item ID:

23259

Date Deposited:

30 Apr 2018 11:59

Last Modified:

30 Apr 2018 11:59

URI: http://research.gold.ac.uk/id/eprint/23259

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