4 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…
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…
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…
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…