6 papers · 1 filter
ShakyPrepend: A Multi-Group Learner with Improved Sample Complexity
Lujing Zhang, Daniel Hsu, Sivaraman Balakrishnan
Multi-group learning is a learning task that focuses on controlling predictors' conditional losses over specified subgroups. We propose ShakyPrepend, a method that leverages tools…
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…
A Causal Lens for Learning Long-term Fair Policies
Jacob Lear, Lu Zhang
Fairness-aware learning studies the development of algorithms that avoid discriminatory decision outcomes despite biased training data. While most studies have concentrated on imme…
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…
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…
Striking a Balance in Fairness for Dynamic Systems Through Reinforcement Learning
Yaowei Hu, Jacob Lear, Lu Zhang
While significant advancements have been made in the field of fair machine learning, the majority of studies focus on scenarios where the decision model operates on a static popula…