Towards human-guided machine learning
By Yolanda Gil, James Honaker, Shikhar Gupta, Yibo Ma, Vito J. D'Orazio, Daniel Garijo, Shruti Prashant Gadewar, Qifan Yang, Neda Jahanshad. 2019
92 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence
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In Artificial Intelligence
- Marco F. Huber
- Marc-André Zöller
- Parikshit Ram
- Werner Geyer
- Michael J. Muller
- Amy X. Zhang
- Alexander G. Gray
- Horst Samulowitz
Citations in the corpus
- Benchmark and Survey of Automated Machine Learning Frameworks (2019, background): “Human-guided ML (Langevin et al., 2018; Gil et al., 2019) aims to present simple questions to the domain expert to guide the exploration of the search space.”
- Survey on Automated Machine Learning (2019, background): “Human-guided ML (Langevin et al., 2018; Gil et al., 2019) aims to present simple questions to the domain expert to guide the exploration of the search space.”
- How do Data Science Workers Collaborate? Roles, Workflows, and Tools (2020, background): “Based on the empirical findings and design suggestions from previous literature [11, 32, 43, 61, 67, 75, 83, 91], some designers and system builders have proposed human-in-the-loop design principles for science tools [2, 3, 28, 49, 50, 92].”
- Human-AI Collaboration in Data Science (2019, methodology): “” [17].”