一句话：提出基于仿真的 FairSense 框架，检测并分析 ML 赋能系统在动态反馈下的长期公平性。
现有 ML 公平性方法多以模型为中心并假设静态环境，但许多 ML 赋能系统的预测决策会改变环境，环境变化又反过来影响后续决策，形成自我强化的反馈循环，可能在长期造成公平性违规。FairSense 为此提出基于仿真的分析框架：给定公平性需求后，先通过蒙特卡洛模拟枚举每个系统配置下的演化轨迹，再对可能配置空间做敏感性分析，评估设计选项与环境因素对长期公平性的影响。作者用贷款放贷、阿片类药物风险评分和预测性警务三个真实案例研究展示其潜在效用。
Algorithmic fairness of machine learning (ML) models has raised significant concern in the recent years. Many testing, verification, and bias mitigation techniques have been proposed to identify and reduce fairness issues in ML models. The existing methods are model-centric and designed to detect fairness issues under static settings. However, many ML-enabled systems operate in a dynamic environment where the predictive decisions made by the system impact the environment, which in turn affects future decision-making. Such a self-reinforcing feedback loop can cause fairness violations in the long term, even if the immediate outcomes are fair. In this paper, we propose a simulation-based framework called Fairsenseto detect and analyze long-term unfairness in ML-enabled systems. Given a fairness requirement, Fairsenseperforms Monte-Carlo simulation to enumerate evolution traces for each system configuration. Then, Fairsenseperforms sensitivity analysis on the space of possible configurations to understand the impact of design options and environmental factors on the long-term fairness of the system. We demonstrate Fairsense'spotential utility through three real-world case studies: Loan lending, opioids risk scoring, and predictive policing.
DOI 原文 ·
@inproceedings{paperbot8897,
title = {FairSense: Long-Term Fairness Analysis of ML-Enabled Systems},
author = {Yining She and Sumon Biswas and Christian Kästner and Eunsuk Kang},
booktitle = {ICSE},
year = {2025},
doi = {10.1109/icse55347.2025.00159}
}