49 citations · 117 across the 4 of their papers we have counts for
3 papers · 1 filter
FASTER-CE: Fast, Sparse, Transparent, and Robust Counterfactual Explanations
Shubham Sharma, Alan H. Gee, Jette Henderson +1
Counterfactual explanations have substantially increased in popularity in the past few years as a useful human-centric way of understanding individual black-box model predictions.…
FaiR-N: Fair and Robust Neural Networks for Structured Data
Shubham Sharma, Alan H. Gee, David Paydarfar +1
Fairness in machine learning is crucial when individuals are subject to automated decisions made by models in high-stake domains. Organizations that employ these models may also ne…
Explaining Deep Classification of Time-Series Data with Learned Prototypes
Alan H. Gee, Diego Garcia-Olano, Joydeep Ghosh +1
The emergence of deep learning networks raises a need for explainable AI so that users and domain experts can be confident applying them to high-risk decisions. In this paper, we l…