Unfairness Discovery and Prevention For Few-Shot Regression
Unfairness Discovery and Prevention For Few-Shot Regression
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DOI：
10.1109/icbk50248.2020.00029
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发表时间：
2020-08
期刊：
影响因子：
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通讯作者：
Chengli Zhao;Feng Chen
中科院分区：
文献类型：
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作者：
Chengli Zhao;Feng Chen
我们研究了有监督的少机会元学习模型的公平性，这些模型对历史数据中的歧视(或偏见)很敏感。基于有偏见的数据训练的机器学习模型往往会对少数族裔用户做出不公平的预测。虽然这个问题已经被研究过了，但现有的方法主要是基于大量的训练数据来检测和控制受保护变量(如种族、性别)对目标预测的依赖效应。这些方法有两个主要缺陷：(1)缺乏对所有变量的全局因果可视化；(2)缺乏对看不见的任务的准确性和公平性的概括。在这项工作中，我们首先使用因果贝叶斯知识图从数据中发现区分，该图不仅证明了受保护变量对目标的依赖，而且还表明了所有变量之间的因果关系。接下来，我们开发了一种基于风险差异的新算法，以量化图中每个受保护变量的区别性影响。此外，为了防止预测的不公平性，提出了一种元学习中的快速自适应偏差控制方法，该方法有效地缓解了每个任务的统计差异，从而确保了受保护属性对基于有偏和少射数据样本的预测的独立性。与现有的元学习模型不同，该模型通过利用(非)保护组之间的平均差异来处理回归问题，有效地降低了任务的组不公平性。通过在人工数据集和真实数据集上的大量实验，我们证明了我们提出的不公平性发现和预防方法有效地检测了歧视，减少了模型输出的偏差，并且在有限的训练样本量下推广到看不见的任务的准确性和公平性。
We study fairness in supervised few-shot meta-learning models that are sensitive to discrimination (or bias) in historical data. A machine learning model trained based on biased data tends to make unfair predictions for users from minority groups. Although this problem has been studied before, existing methods mainly aim to detect and control the dependency effect of the protected variables (e.g. race, gender) on target prediction based on a large amount of training data. These approaches carry two major drawbacks that (1) lacking showing a global cause-effect visualization for all variables; (2) lacking generalization of both accuracy and fairness to unseen tasks. In this work, we first discover discrimination from data using a causal Bayesian knowledge graph which not only demonstrates the dependency of the protected variable on target but also indicates causal effects between all variables. Next, we develop a novel algorithm based on risk difference in order to quantify the discriminatory influence for each protected variable in the graph. Furthermore, to protect prediction from unfairness, a fast-adapted bias-control approach in meta-learning is proposed, which efficiently mitigates statistical disparity for each task and it thus ensures independence of protected attributes on predictions based on biased and few-shot data samples. Distinct from existing meta-learning models, group unfairness of tasks are efficiently reduced by leveraging the mean difference between (un)protected groups for regression problems. Through extensive experiments on both synthetic and real-world data sets, we demonstrate that our proposed unfairness discovery and prevention approaches efficiently detect discrimination and mitigate biases on model output as well as generalize both accuracy and fairness to unseen tasks with a limited amount of training samples.