7 citations · 13 across the 7 of their papers we have counts for
7 papers
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…
Hey GPT, Can You be More Racist? Analysis from Crowdsourced Attempts to Elicit Biased Content from Generative AI
Hangzhi Guo, Pranav Narayanan Venkit, Eunchae Jang +7
The widespread adoption of large language models (LLMs) and generative AI (GenAI) tools across diverse applications has amplified the importance of addressing societal biases inher…
Have LLMs Reopened the Pandora's Box of AI-Generated Fake News?
Xinyu Wang, Wenbo Zhang, Sai Koneru +5
With the rise of AI-generated content spewed at scale from large language models (LLMs), genuine concerns about the spread of fake news have intensified. The perceived ability of L…
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…
A Taxonomy of Rater Disagreements: Surveying Challenges & Opportunities from the Perspective of Annotating Online Toxicity
Wenbo Zhang, Hangzhi Guo, Ian D Kivlichan +3
Toxicity is an increasingly common and severe issue in online spaces. Consequently, a rich line of machine learning research over the past decade has focused on computationally det…
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…