7 citations · 13 across the 7 of their papers we have counts for
4 papers · 1 filter
Designing User-Centric Metrics for Evaluation of Counterfactual Explanations
Firdaus Ahmed Choudhury, Ethan Leicht, Jude Ethan Bislig +2
Counterfactual Explanations (CFEs) have grown in popularity as a means of offering actionable guidance by identifying the minimum changes in feature values required to flip an ML m…
Watermarking Counterfactual Explanations
Hangzhi Guo, Firdaus Ahmed Choudhury, Tinghua Chen +1
Counterfactual (CF) explanations for ML model predictions provide actionable recourse recommendations to individuals adversely impacted by predicted outcomes. However, despite bein…
RoCourseNet: Distributionally Robust Training of a Prediction Aware Recourse Model
Hangzhi Guo, Feiran Jia, Jinghui Chen +2
Counterfactual (CF) explanations for machine learning (ML) models are preferred by end-users, as they explain the predictions of ML models by providing a recourse (or contrastive)…
CounterNet: End-to-End Training of Prediction Aware Counterfactual Explanations
Hangzhi Guo, Thanh Hong Nguyen, Amulya Yadav
This work presents CounterNet, a novel end-to-end learning framework which integrates Machine Learning (ML) model training and the generation of corresponding counterfactual (CF) e…