论文标题
验证机器学习模型中的个人公平性
Verifying Individual Fairness in Machine Learning Models
论文作者
论文摘要
我们考虑了一个给定决策模型（使用结构化数据）是否具有个人公平性的问题。遵循DWORW的工作，如果有一对有效的输入彼此接近（根据适当的度量），则单独偏置（或不公平），但通过模型（不同的类标签或输出的较大差异）对彼此的处理方式有所不同，并且如果没有这样的对，则它是公正的（或公平的）。我们的目标是构建验证者，以证明给定模型的个人公平性，我们通过考虑适当放松问题来构建验证者。我们构建了声音的验证符，但对于线性分类器而尚不完整，以及内核化的多项式/径向基函数分类器。我们还报告了在公开可用数据集上评估我们提出的算法的实验结果。
We consider the problem of whether a given decision model, working with structured data, has individual fairness. Following the work of Dwork, a model is individually biased (or unfair) if there is a pair of valid inputs which are close to each other (according to an appropriate metric) but are treated differently by the model (different class label, or large difference in output), and it is unbiased (or fair) if no such pair exists. Our objective is to construct verifiers for proving individual fairness of a given model, and we do so by considering appropriate relaxations of the problem. We construct verifiers which are sound but not complete for linear classifiers, and kernelized polynomial/radial basis function classifiers. We also report the experimental results of evaluating our proposed algorithms on publicly available datasets.
