发表机构
Università della Svizzera Italiana; Monash University（瑞士意大利语区大学; 莫纳什大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出公平Lasso和公平后验方法，在广义线性模型中选择公平协变量，以在个体公平与群体公平之间取得有利权衡，并可用于条件人口统计平价。
AI 中文摘要
算法日益被用于帮助自动化和改进数据驱动的决策，但必须注意防止此类算法从历史数据中学习歧视性模式并延续其偏见。统计公平性概念旨在缓解模型对弱势群体的不同影响（从而确保群体公平），或对具有相似特征的个体产生的不同待遇（从而确保个体公平）。对于非平凡模型，同时缓解不同影响和不同待遇通常是不可能的，因此需要折衷。在本文中，我们介绍了公平Lasso和公平后验作为广义线性模型中选择公平协变量的方法。通过针对那些同时是响应的强预测因子且与敏感群体成员关系弱相关的变量，我们旨在实现不同待遇与不同影响之间的有利权衡。此外，在没有规定性法律框架的情况下，我们选择的公平特征集可用作条件人口统计平价（CDP）范式中合法特征的条件集。
英文摘要
Algorithms are increasingly being used to help automate and improve data-driven decisions, but care must be taken to prevent such algorithms from learning discriminatory patterns from historical data and perpetuating their biases. Statistical notions of fairness aim to mitigate either a model's disparate impact on disadvantaged groups (thus ensuring group fairness) or the resulting disparate treatment of individuals with similar features (thus ensuring individual fairness). Simultaneously mitigating disparate impact and disparate treatment is generally impossible for non-trivial models, necessitating a compromise. In this paper, we introduce the Fair Lasso and Fair Posterior as methods for selecting fair covariates in generalised linear models. By targeting variables that are simultaneously strong predictors of the response and weakly dependent on the sensitive group memberships, we aim to achieve favourable trade-offs between disparate treatment and disparate impact. Additionally, our selected set of fair features can be used as the conditioning set of legitimate features in the paradigm of Conditional Demographic parity (CDP) when no prescriptive legal framework exists.
