83 citations · 332 across the 12 of their papers we have counts for
3 papers · 1 filter
Regularizing Black-box Models for Improved Interpretability (HILL 2019 Version)
Gregory Plumb, Maruan Al-Shedivat, Eric Xing +1
Most of the work on interpretable machine learning has focused on designing either inherently interpretable models, which typically trade-off accuracy for interpretability, or post…
FairVis: Visual Analytics for Discovering Intersectional Bias in Machine Learning
Ángel Alexander Cabrera, Will Epperson, Fred Hohman +3
The growing capability and accessibility of machine learning has led to its application to many real-world domains and data about people. Despite the benefits algorithmic systems m…
Regularizing Black-box Models for Improved Interpretability
Gregory Plumb, Maruan Al-Shedivat, Angel Alexander Cabrera +3
Most of the work on interpretable machine learning has focused on designing either inherently interpretable models, which typically trade-off accuracy for interpretability, or post…