不确定性在评估机器学习模型公平性中的作用

中文摘要 AI 辅助

机器学习模型广泛应用于临床、社交媒体、执法和关键基础设施领域。验证其输出是否对弱势群体或个人存在偏见，对于确保其公平性并允许在这些场景中使用至关重要。对可能的公平性违规进行严格的风险评估，需要量化与选择和估计此类模型相关的不确定性。然而，文献中很少这样做，文献侧重于识别在预测准确性和公平性之间具有适当权衡的单一模型。在本文中，我们超越点估计，讨论频率派和贝叶斯方法用于公平机器学习的不确定性量化，并提供模拟和真实数据的实际示例及其影响。

英文摘要

Machine learning models are widely used in clinical applications, social media, law enforcement and critical infrastructure. Verifying whether their outputs are biased against disadvantaged groups or individuals is crucial to ensuring they are fair and allowing their use in such settings. A rigorous risk assessment of possible fairness violations requires quantifying the uncertainty associated with selecting and estimating such models. Yet, this is rarely done in the literature, which focuses on identifying a single model with a suitable trade-off between predictive accuracy and fairness. In this paper, we move beyond point estimation and discuss frequentist and Bayesian approaches to uncertainty quantification for fair machine learning, with practical examples and implications for simulated and real data.

发表机构

• Università della Svizzera Italiana (USI)（瑞士意大利语区大学）

机构由 AI 辅助整理，请以论文原文为准。