School of Automation, Guangdong University of Technology, Guangzhou 510006, China.
School of Computer Science and Technology, Guangdong University of Technology, Guangzhou 510006, China.
Math Biosci Eng. 2023 Jan;20(2):3624-3637. doi: 10.3934/mbe.2023169. Epub 2022 Dec 8.
Artificial-intelligence-assisted decision-making is appearing increasingly more frequently in our daily lives; however, it has been shown that biased data can cause unfairness in decision-making. In light of this, computational techniques are needed to limit the inequities in algorithmic decision-making. In this letter, we present a framework to join fair feature selection and fair meta-learning to do few-shot classification, which contains three parts: (1) a pre-processing component acts as an intermediate bridge between fair genetic algorithm (FairGA) and fair few-shot (FairFS) to generate the feature pool; (2) the FairGA module considers the presence or absence of words as gene expression, and filters out key features by a fairness clustering genetic algorithm; (3) the FairFS part carries out the task of representation and fairness constraint classification. Meanwhile, we propose a combinatorial loss function to cope with fairness constraints and hard samples. Experiments show that the proposed method achieves strong competitive performance on three public benchmarks.
人工智能辅助决策在我们的日常生活中越来越常见；然而，有研究表明，有偏差的数据可能导致决策的不公平。有鉴于此，需要计算技术来限制算法决策中的不平等。在这封信中，我们提出了一个框架，将公平特征选择和公平元学习结合起来进行少镜头分类，它包含三个部分：（1）预处理组件充当公平遗传算法（FairGA）和公平少镜头（FairFS）之间的中间桥梁，以生成特征池；（2）FairGA 模块将单词的存在与否视为基因表达，并通过公平聚类遗传算法过滤出关键特征；（3）FairFS 部分执行表示和公平约束分类任务。同时，我们提出了一种组合损失函数来处理公平约束和硬样本。实验表明，该方法在三个公共基准上取得了很强的竞争性能。
