Recent years have seen much research on fairness in machine learning. Here,
mean difference (MD) or demographic parity is one of the most popular measures
of fairness. However, MD quantifies not only discrimination but also
explanatory bias which is the difference of outcomes justified by explanatory
features. In this paper, we devise novel models, called FairCEEs, which remove
discrimination while keeping explanatory bias. The models are based on
estimators of causal effect utilizing propensity score analysis. We prove that
FairCEEs with the squared loss theoretically outperform a naive MD constraint
model. We provide an efficient algorithm for solving FairCEEs in regression and
binary classification tasks. In our experiment on synthetic and real-world data
in these two tasks, FairCEEs outperformed an existing model that considers
explanatory bias in specific cases.

本文通过使用因果效应估计器，利用倾向得分分析，提出了一种名为 FairCEEs 的新模型，该模型可以在保持解释偏差的同时消除歧视。经过实验证明，FairCEEs 在回归和二分类任务中的效果优于既考虑歧视又考虑解释偏差的现有模型。

利用因果效应估计的凸公平约束模型

Convex Fairness Constrained Model Using Causal Effect Estimators

The discovery of discriminatory bias in human or automated decision making is
a task of increasing importance and difficulty, exacerbated by the pervasive
use of machine learning and data mining. Currently, discrimination discovery
largely relies upon correlation analysis of decisions records, disregarding the
impact of confounding biases. We present a method for causal discrimination
discovery based on propensity score analysis, a statistical tool for filtering
out the effect of confounding variables. We introduce causal measures of
discrimination which quantify the effect of group membership on the decisions,
and highlight causal discrimination/favoritism patterns by learning regression
trees over the novel measures. We validate our approach on two real world
datasets. Our proposed framework for causal discrimination has the potential to
enhance the transparency of machine learning with tools for detecting
discriminatory bias both in the training data and in the learning algorithms.

本文介绍一种基于倾向得分分析的原因型歧视方法，并利用回归树学习新的歧视 / 偏袒模式，验证在两个现实世界的数据集上，它可以加强机器学习透明度并探测训练数据和学习算法中的歧视性偏差。