We propose a fairness measure relaxing the equality conditions in the popular
equal odds fairness regime for classification. We design an iterative,
model-agnostic, grid-based heuristic that calibrates the outcomes per sensitive
attribute value to conform to the measure. The heuristic is designed to handle
high arity attribute values and performs a per attribute sanitization of
outcomes across different protected attribute values. We also extend our
heuristic for multiple attributes. Highlighting our motivating application,
fraud detection, we show that the proposed heuristic is able to achieve
fairness across multiple values of a single protected attribute, multiple
protected attributes. When compared to current fairness techniques, that focus
on two groups, we achieve comparable performance across several public data
sets.

本文提出了一种公平度量方法，通过迭代式基于网格的启发式算法来校准敏感属性值的结果，实现对保护属性值的公平。在多个受保护属性值的情况下，通过对欺诈检测进行研究，证明所提出的启发式算法能够实现公平。与现有的公平技术相比，该算法在多个公开数据集上的性能表现相当。

多属性公平性：针对欺诈检测的应用

Multiple Attribute Fairness: Application to Fraud Detection

As an important problem in modern data analytics, classification has
witnessed varieties of applications from different domains. Different from
conventional classification approaches, fair classification concerns the issues
of unintentional biases against the sensitive features (e.g., gender, race).
Due to high nonconvexity of fairness measures, existing methods are often
unable to model exact fairness, which can cause inferior fair classification
outcomes. This paper fills the gap by developing a novel unified framework to
jointly optimize accuracy and fairness. The proposed framework is versatile and
can incorporate different fairness measures studied in literature precisely as
well as can be applicable to many classifiers including deep classification
models. Specifically, in this paper, we first prove Fisher consistency of the
proposed framework. We then show that many classification models within this
framework can be recast as mixed-integer convex programs, which can be solved
effectively by off-the-shelf solvers when the instance sizes are moderate and
can be used as benchmarks to compare the efficiency of approximation
algorithms. We prove that in the proposed framework, when the classification
outcomes are known, the resulting problem, termed "unbiased subdata selection,"
is strongly polynomial-solvable and can be used to enhance the classification
fairness by selecting more representative data points. This motivates us to
develop an iterative refining strategy (IRS) to solve the large-scale
instances, where we improve the classification accuracy and conduct the
unbiased subdata selection in an alternating fashion. We study the convergence
property of IRS and derive its approximation bound. More broadly, this
framework can be leveraged to improve classification models with unbalanced
data by taking F1 score into consideration.

该论文提出了一个新的统一框架，利用深度分类模型对证明的公平性指标进行联合优化，适用于许多分类器，解决了现有方法无法建模精确公平性的问题。