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cs.LG2025
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…
cs.LG2025
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…
cs.LG2025
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…