270 citations · 624 across the 13 of their papers we have counts for
3 papers · 1 filter
On the Safety of Interpretable Machine Learning: A Maximum Deviation Approach
Dennis Wei, Rahul Nair, Amit Dhurandhar +3
Interpretable and explainable machine learning has seen a recent surge of interest. We focus on safety as a key motivation behind the surge and make the relationship between interp…
Anomaly Attribution with Likelihood Compensation
Tsuyoshi Idé, Amit Dhurandhar, Jiří Navrátil +2
This paper addresses the task of explaining anomalous predictions of a black-box regression model. When using a black-box model, such as one to predict building energy consumption…
Write It Like You See It: Detectable Differences in Clinical Notes By Race Lead To Differential Model Recommendations
Hammaad Adam, Ming Ying Yang, Kenrick Cato +6
Clinical notes are becoming an increasingly important data source for machine learning (ML) applications in healthcare. Prior research has shown that deploying ML models can perpet…