Towards Human-Guided Machine Learning




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Association for Computing Machinery


Automated Machine Learning (AutoML) systems are emerging that automatically search for possible solutions from a large space of possible kinds of models. Although fully automated machine learning is appropriate for many applications, users often have knowledge that supplements and constraints the available data and solutions. This paper proposes human-guided machine learning (HGML) as a hybrid approach where a user interacts with an AutoML system and tasks it to explore different problem settings that reflect the user's knowledge about the data available. We present: 1) a task analysis of HGML that shows the tasks that a user would want to carry out, 2) a characterization of two scientific publications, one in neuroscience and one in political science, in terms of how the authors would search for solutions using an AutoML system, 3) requirements for HGML based on those characterizations, and 4) an assessment of existing AutoML systems in terms of those requirements. © 2019 Copyright is held by the owner/author(s).


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Machine learning, Task analysis, Automation, Job analysis, User interfaces (Computer systems)

This material is based on research sponsored by the Defense Advanced Research Projects Agency (DARPA) under agreement numbers FA8750-17-C-0106 and FA8750-17-2-0114 with additional support from NIH grants AG059874 and MH117601.


©2019 The Authors