Enhancing Interactive Machine Learning, Jürgen Bernard
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Machine Learning (ML) is a key technology in the era of data-centered research and applications.
Given the availability of data, a series of data-driven learning tasks have successfully been formalized in the past and made ML-capable. As a result, many automated ML-based solutions already ease our everyday life or gain other types of promising benefits.
Interactive Machine Learning is a principle that may be applicable for the plethora of data-driven learning tasks for which formalizations and automations could not be achieved so far. Interactive ML explicitly includes the involvement of humans in the iterative and incremental ML process, following the idea to combine the strengths of both humans and machines to create more effective and efficient solutions. There is considerable evidence that many remaining data-driven learning challenges can be tackled with interactive ML principles. Example include data-oriented challenges like heterogeneous data, dirty data, uncertain data, or unlabeled data, as well as process-oriented challenges such as data preprocessing, model building, model parameterization, model quality assessment, model uncertainty assessment, model refinement, model explanation and interpretation, or model comparison. These performance-driven challenges are accompanied by at least two human-centered goals worth to be pursued: 1) creating personalized ML solutions tailored to the information need of individual users or application tasks, as well as 2) making ML applicable for larger user groups far beyond the group of data science experts.