Boosting Fair Classifier Generalization through Adaptive Priority Reweighing

IF 4 3区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS ACM Transactions on Knowledge Discovery from Data Pub Date : 2024-05-23 DOI:10.1145/3665895
Zhihao Hu, Yiran Xu, Mengnan Du, Jindong Gu, Xinmei Tian, Fengxiang He
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Abstract

With the increasing penetration of machine learning applications in critical decision-making areas, calls for algorithmic fairness are more prominent. Although there have been various modalities to improve algorithmic fairness through learning with fairness constraints, their performance does not generalize well in the test set. A performance-promising fair algorithm with better generalizability is needed. This paper proposes a novel adaptive reweighing method to eliminate the impact of the distribution shifts between training and test data on model generalizability. Most previous reweighing methods propose to assign a unified weight for each (sub)group. Rather, our method granularly models the distance from the sample predictions to the decision boundary. Our adaptive reweighing method prioritizes samples closer to the decision boundary and assigns a higher weight to improve the generalizability of fair classifiers. Extensive experiments are performed to validate the generalizability of our adaptive priority reweighing method for accuracy and fairness measures (i.e., equal opportunity, equalized odds, and demographic parity) in tabular benchmarks. We also highlight the performance of our method in improving the fairness of language and vision models. The code is available at https://github.com/che2198/APW.

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通过自适应优先级重权提升公平分类器的通用性
随着机器学习应用在关键决策领域的日益普及,对算法公平性的呼声也越来越高。虽然已有多种方法通过带有公平性约束的学习来提高算法的公平性,但它们的性能在测试集中并不能得到很好的推广。因此,我们需要一种具有更好泛化能力的、性能良好的公平算法。本文提出了一种新颖的自适应重权重法,以消除训练数据和测试数据之间的分布偏移对模型泛化能力的影响。以前的大多数重权重方法都是为每个(子)组分配一个统一的权重。相反,我们的方法对样本预测与决策边界之间的距离进行了细化建模。我们的自适应重权重方法优先考虑更接近决策边界的样本,并赋予更高的权重,以提高公平分类器的普适性。我们进行了广泛的实验,以验证我们的自适应优先级重权重方法在准确性和公平性度量(即机会均等、赔率均等和人口均等)方面的通用性。我们还强调了我们的方法在提高语言和视觉模型公平性方面的性能。代码见 https://github.com/che2198/APW。
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来源期刊
ACM Transactions on Knowledge Discovery from Data
ACM Transactions on Knowledge Discovery from Data COMPUTER SCIENCE, INFORMATION SYSTEMS-COMPUTER SCIENCE, SOFTWARE ENGINEERING
CiteScore
6.70
自引率
5.60%
发文量
172
审稿时长
3 months
期刊介绍: TKDD welcomes papers on a full range of research in the knowledge discovery and analysis of diverse forms of data. Such subjects include, but are not limited to: scalable and effective algorithms for data mining and big data analysis, mining brain networks, mining data streams, mining multi-media data, mining high-dimensional data, mining text, Web, and semi-structured data, mining spatial and temporal data, data mining for community generation, social network analysis, and graph structured data, security and privacy issues in data mining, visual, interactive and online data mining, pre-processing and post-processing for data mining, robust and scalable statistical methods, data mining languages, foundations of data mining, KDD framework and process, and novel applications and infrastructures exploiting data mining technology including massively parallel processing and cloud computing platforms. TKDD encourages papers that explore the above subjects in the context of large distributed networks of computers, parallel or multiprocessing computers, or new data devices. TKDD also encourages papers that describe emerging data mining applications that cannot be satisfied by the current data mining technology.
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