Revealing Actionable Simplicity in Data
Conference Paper, Proceedings of AAAI '18 Spring Symposium on Design of User Experience for Artificial Intelligence, pp. 382 - 385, March, 2018
Abstract
We present a methodology where we identify simple structure in data, if such structure exists, and present it to end-users, enabling them to interact with data or manipulate a machine learning model. We share our bounding box algorithm which distills complex information into a small set of range rules which yield naturally intuitive visualizations. We demonstrate a few cases where simple, actionable descriptions lead to quantitative improvements in an AI pipeline.
BibTeX
@conference{Gisolfi-2018-121806,author = {Nick Gisolfi and Artur Dubrawski},
title = {Revealing Actionable Simplicity in Data},
booktitle = {Proceedings of AAAI '18 Spring Symposium on Design of User Experience for Artificial Intelligence},
year = {2018},
month = {March},
pages = {382 - 385},
}
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