论文标题
在数据集中对歧视的前歧视评估
Ex-Ante Assessment of Discrimination in Dataset
论文作者
论文摘要
数据所有者对他们的数据的使用如何损害较低的社区面临越来越多的责任。利益相关者希望确定导致算法偏向任何特定人群群体的数据的特征，例如，其种族，性别，年龄和/或宗教。具体而言，我们有兴趣识别特征空间的子集，在该特征空间中，从特征到观察到的结果之间的地面真相响应函数在人群之间有所不同。为此，我们提出了一种决策树算法的森林，该算法产生了一个分数，该分数捕获了个人的反应随敏感属性而变化的可能性。从经验上讲，我们发现我们的方法使我们能够识别出最有可能被多个分类器分类的个人，包括随机森林，逻辑回归，支持向量机和K-Neartivt Neighbors。我们方法的优点是，它允许利益相关者表征可能有助于歧视的风险样本，并使用预测来估算即将到来的样本的风险。
Data owners face increasing liability for how the use of their data could harm under-priviliged communities. Stakeholders would like to identify the characteristics of data that lead to algorithms being biased against any particular demographic groups, for example, defined by their race, gender, age, and/or religion. Specifically, we are interested in identifying subsets of the feature space where the ground truth response function from features to observed outcomes differs across demographic groups. To this end, we propose FORESEE, a FORESt of decision trEEs algorithm, which generates a score that captures how likely an individual's response varies with sensitive attributes. Empirically, we find that our approach allows us to identify the individuals who are most likely to be misclassified by several classifiers, including Random Forest, Logistic Regression, Support Vector Machine, and k-Nearest Neighbors. The advantage of our approach is that it allows stakeholders to characterize risky samples that may contribute to discrimination, as well as, use the FORESEE to estimate the risk of upcoming samples.
