2023/10/17 by Wei Yao, Yao, Wei, Zhanke Zhou +7 · 6 citations
Mathematics · Social Sciences · #Artificial Intelligence (cs.AI) #Computer science #Economics #Ethics and Social Impacts of AI #FOS: Computer and information sciences #Fairness measure #Function (biology) #Machine Learning (cs.LG) #Machine learning #Margin (machine learning) #Mathematical optimization #Mathematics #Stability (learning theory) #Surrogate model #Throughput #Variance (accounting)
paper · pdf · doi:10.48550/arxiv.2310.11211
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2023/10/17 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
It has been observed that machine learning algorithms exhibit biased predictions against certain population groups. To mitigate such bias while achieving comparable accuracy, a promising approach is to introduce surrogate functions of the concerned fairness definition and solve a constrained optimization problem. However, it is intriguing in previous work that such fairness surrogate functions may yield unfair results and high instability. In this work, in order to deeply understand them, taking a widely used fairness definition--demographic parity as an example, we show that there is a surrogate-fairness gap between the fairness definition and the fairness surrogate function. Also, the theoretical analysis and experimental results about the gap motivate us that the fairness and stability will be affected by the points far from the decision boundary, which is the large margin points issue investigated in this paper. To address it, we propose the general sigmoid surrogate to simultaneously reduce both the surrogate-fairness gap and the variance, and offer a rigorous fairness and stability upper bound. Interestingly, the theory also provides insights into two important issues that deal with the large margin points as well as obtaining a more balanced dataset are beneficial to fairness and stability. Furthermore, we elaborate a novel and general algorithm called Balanced Surrogate, which iteratively reduces the gap to mitigate unfairness. Finally, we provide empirical evidence showing that our methods consistently improve fairness and stability while maintaining accuracy comparable to the baselines in three real-world datasets.