IUI workshop on interactive machine learning

Abstract

Many applications of Machine Learning (ML) involve interactions with humans. Humans may provide input to a learning algorithm (in the form of labels, demonstrations, corrections, rankings or evaluations) while observing its outputs (in the form of feedback, predictions or executions). Although humans are an integral part of the learning process, traditional ML systems used in these applications are agnostic to the fact that inputs/outputs are from/for humans. However, a growing community of researchers at the intersection of ML and human-computer interaction are making interaction with humans a central part of developing ML systems. These efforts include applying interaction design principles to ML systems, using human-subject testing to evaluate ML systems and inspire new methods, and changing the input and output channels of ML systems to better leverage human capabilities. With this Interactive Machine Learning (IML) workshop at IUI 2013 we aim to bring this community together to share ideas, get up-to-date on recent advances, progress towards a common framework and terminology for the field, and discuss the open questions and challenges of IML.