2 citations · 4 across the 6 of their papers we have counts for
6 papers
FairAgent: Democratizing Fairness-Aware Machine Learning with LLM-Powered Agents
Yucong Dai, Lu Zhang, Feng Luo +2
Training fair and unbiased machine learning models is crucial for high-stakes applications, yet it presents significant challenges. Effective bias mitigation requires deep expertis…
Causally Fair Node Classification on Non-IID Graph Data
Yucong Dai, Lu Zhang, Yaowei Hu +2
Fair machine learning seeks to identify and mitigate biases in predictions against unfavorable populations characterized by demographic attributes, such as race and gender. Recent…
AI-Cybersecurity Education Through Designing AI-based Cyberharassment Detection Lab
Ebuka Okpala, Nishant Vishwamitra, Keyan Guo +7
Cyberharassment is a critical, socially relevant cybersecurity problem because of the adverse effects it can have on targeted groups or individuals. While progress has been made in…
Long-Term Fair Decision Making through Deep Generative Models
Yaowei Hu, Yongkai Wu, Lu Zhang
This paper studies long-term fair machine learning which aims to mitigate group disparity over the long term in sequential decision-making systems. To define long-term fairness, we…
From Identifiable Causal Representations to Controllable Counterfactual Generation: A Survey on Causal Generative Modeling
Aneesh Komanduri, Xintao Wu, Yongkai Wu +1
Deep generative models have shown tremendous capability in data density estimation and data generation from finite samples. While these models have shown impressive performance by…
Algorithmic Recourse in Abnormal Multivariate Time Series
Xiao Han, Lu Zhang, Yongkai Wu +1
Algorithmic recourse provides actionable recommendations to alter unfavorable predictions of machine learning models, enhancing transparency through counterfactual explanations. Wh…