Machine learning is one of the most important and successful techniques in contemporary computer science, with applications ranging from from medical research to the arts, as well as considerable recent interest in its use for interaction design. 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. We will also invite participants to help us in establishing and maintaining a community around human-centred machine learning, including running a follow-up workshop at a machine learning conference such as NIPS.
This will be a 1 day workshop run as part of ACM CHI 2016 in San Jose California.