Fairness is becoming a rising concern w.r.t. machine learning model
performance. Especially for sensitive fields such as criminal justice and loan
decision, eliminating the prediction discrimination towards a certain group of
population (characterized by sensitive features like race and gender) is
important for enhancing the trustworthiness of model. In this paper, we present
a new general framework to improve machine learning fairness. The goal of our
model is to minimize the influence of sensitive feature from the perspectives
of both the data input and the predictive model. In order to achieve this goal,
we reformulate the data input by removing the sensitive information and
strengthen model fairness by minimizing the marginal contribution of the
sensitive feature. We propose to learn the non-sensitive input via sampling
among features and design an adversarial network to minimize the dependence
between the reformulated input and the sensitive information. Extensive
experiments on three benchmark datasets suggest that our model achieve better
results than related state-of-the-art methods with respect to both fairness
metrics and prediction performance.

介绍一种新的针对敏感特征的机器学习公平性框架，通过去除敏感信息和最小化敏感特征的边际贡献来改善模型的公平性。实验结果表明，该模型在公平性指标和预测性能方面均优于相关最先进方法。

通过对抗网络逼近机器学习公平性

Approaching Machine Learning Fairness through Adversarial Network

Fairness-aware learning involves designing algorithms that do not
discriminate with respect to some sensitive feature (e.g., race or gender).
Existing work on the problem operates under the assumption that the sensitive
feature available in one's training sample is perfectly reliable. This
assumption may be violated in many real-world cases: for example, respondents
to a survey may choose to conceal or obfuscate their group identity out of fear
of potential discrimination. This poses the question of whether one can still
learn fair classifiers given noisy sensitive features. In this paper, we answer
the question in the affirmative: we show that if one measures fairness using
the mean-difference score, and sensitive features are subject to noise from the
mutually contaminated learning model, then owing to a simple identity we only
need to change the desired fairness-tolerance. The requisite tolerance can be
estimated by leveraging existing noise-rate estimators from the label noise
literature. We finally show that our procedure is empirically effective on two
case-studies involving sensitive feature censoring.

本文研究了含有噪声敏感特征的公平机器学习算法，表明当使用均值差异分数作为公平性量度标准时，只需通过调整所需的公平容错率即可仍然学习出公平分类器，该容错率可以通过现有噪声率估计器进行估计，并在两个敏感特征审查案例研究中得到实证有效性。

抗噪公平分类

Noise-tolerant fair classification

Ensuring that classifiers are non-discriminatory or fair with respect to a
sensitive feature (e.g., race or gender) is a topical problem. Progress in this
task requires fixing a definition of fairness, and there have been several
proposals in this regard over the past few years. Several of these, however,
assume either binary sensitive features (thus precluding categorical or
real-valued sensitive groups), or result in non-convex objectives (thus
adversely affecting the optimisation landscape). In this paper, we propose a
new definition of fairness that generalises some existing proposals, while
allowing for generic sensitive features and resulting in a convex objective.
The key idea is to enforce that the expected losses (or risks) across each
subgroup induced by the sensitive feature are commensurate. We show how this
relates to the rich literature on risk measures from mathematical finance. As a
special case, this leads to a new convex fairness-aware objective based on
minimising the conditional value at risk (CVaR).

本研究提出了一个新的公平定义，通过对敏感特征感兴趣的亚组的期望损失进行衡量，从而实现了通用的敏感特征以及凸性目标的结果。该目标是基于条件风险最小化构建的，这与数学金融中风险度量的丰富文献相关。