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.
Contemporary music composition is a highly creative and disciplined activity that requires free expression of ideas and sophisticated computer programming. This paper presents a technique for structured observation of expert creative behavior, as well as Polyphony, a novel interface for systematically studying all phases of computer-aided composition. Polyphony is a unified user interface that integrates interactive paper and electronic user interfaces for composing music. It supports fluid transitions between informal sketches and formal computer-based representations. We asked 12 composers to use Polyphony to compose an electronic accompaniment to a 20-second instrumental composition by Anton Webern. All successfully created a complete, original composition in an hour and found the task challenging but fun. The resulting dozen comparable snapshots of the composition process reveal how composers both adapt and appropriate tools in their own way.
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