AdaFair：累积公平性自适应提升
AdaFair: Cumulative Fairness Adaptive Boosting
摘要：
在累犯、工作雇用和贷款信贷等具有高度社会影响的领域，基于ML的决策的广泛使用引起了人们对潜在歧视的许多担忧。特别是，在某些情况下，已经观察到ML算法可以基于性别或种族等敏感属性提供不同的决策，因此可能导致歧视。虽然已经提出了几种公平感知ML方法，但它们的重点主要是保持整体分类准确性，同时提高受保护和非受保护组（基于敏感属性定义）的预测公平性。然而，在类别不平衡的情况下，总体准确性不是一个很好的性能指标，因为它偏向于大多数类别。正如我们将在实验中看到的，许多与公平性相关的数据集都存在类不平衡问题，因此，解决公平性也需要解决不平衡问题。为此，我们提出了AdaFair，一个基于AdaBoost的公平性感知分类器，它进一步更新每个提升轮中实例的权重，同时考虑到基于所有当前集成成员的公平性累积概念，同时通过优化集成成员的数量来明确解决类不平衡问题，以平衡分类错误。我们的实验表明，我们的方法可以实现真正的积极和真正的消极率的保护和非保护组的奇偶校验，而它显着优于现有的公平意识的方法高达25%的平衡误差。
英文摘要：
The widespread use of ML-based decision making in domains with high societal impact such as recidivism, job hiring and loan credit has raised a lot of concerns regarding potential discrimination. In particular, in certain cases it has been observed that ML algorithms can provide different decisions based on sensitive attributes such as gender or race and therefore can lead to discrimination. Although, several fairness-aware ML approaches have been proposed, their focus has been largely on preserving the overall classification accuracy while improving fairness in predictions for both protected and non-protected groups (defined based on the sensitive attribute(s)). The overall accuracy however is not a good indicator of performance in case of class imbalance, as it is biased towards the majority class. As we will see in our experiments, many of the fairness-related datasets suffer from class imbalance and therefore, tackling fairness requires also tackling the imbalance problem. To this end, we propose AdaFair, a fairness-aware classifier based on AdaBoost that further updates the weights of the instances in each boosting round taking into account a cumulative notion of fairness based upon all current ensemble members, while explicitly tackling class-imbalance by optimizing the number of ensemble members for balanced classification error. Our experiments show that our approach can achieve parity in true positive and true negative rates for both protected and non-protected groups, while it significantly outperforms existing fairness-aware methods up to 25% in terms of balanced error.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
