Most existing works on fairness assume the model has full access to
demographic information. However, there exist scenarios where demographic
information is partially available because a record was not maintained
throughout data collection or due to privacy reasons. This setting is known as
demographic scarce regime. Prior research have shown that training an attribute
classifier to replace the missing sensitive attributes (proxy) can still
improve fairness. However, the use of proxy-sensitive attributes worsens
fairness-accuracy trade-offs compared to true sensitive attributes. To address
this limitation, we propose a framework to build attribute classifiers that
achieve better fairness-accuracy trade-offs. Our method introduces uncertainty
awareness in the attribute classifier and enforces fairness on samples with
demographic information inferred with the lowest uncertainty. We show
empirically that enforcing fairness constraints on samples with uncertain
sensitive attributes is detrimental to fairness and accuracy. Our experiments
on two datasets showed that the proposed framework yields models with
significantly better fairness-accuracy trade-offs compared to classic attribute
classifiers. Surprisingly, our framework outperforms models trained with
constraints on the true sensitive attributes.

本研究通过引入不确定性感知，对可能存在不确定性的人口敏感属性样本实施公平性要求，并提出一种建立更好的公平性 - 准确性权衡的属性分类器框架，实验证明该框架相对于传统方法在两个数据集上都取得显著的公平性 - 准确性权衡表现优势，并且超过了对真实敏感属性进行约束训练的模型。

人口有限制度下的公平性

Fairness Under Demographic Scarce Regime

In recent years, machine learning algorithms have become ubiquitous in a
multitude of high-stakes decision-making applications. The unparalleled ability
of machine learning algorithms to learn patterns from data also enables them to
incorporate biases embedded within. A biased model can then make decisions that
disproportionately harm certain groups in society -- limiting their access to
financial services, for example. The awareness of this problem has given rise
to the field of Fair ML, which focuses on studying, measuring, and mitigating
unfairness in algorithmic prediction, with respect to a set of protected groups
(e.g., race or gender). However, the underlying causes for algorithmic
unfairness still remain elusive, with researchers divided between blaming
either the ML algorithms or the data they are trained on. In this work, we
maintain that algorithmic unfairness stems from interactions between models and
biases in the data, rather than from isolated contributions of either of them.
To this end, we propose a taxonomy to characterize data bias and we study a set
of hypotheses regarding the fairness-accuracy trade-offs that fairness-blind ML
algorithms exhibit under different data bias settings. On our real-world
account-opening fraud use case, we find that each setting entails specific
trade-offs, affecting fairness in expected value and variance -- the latter
often going unnoticed. Moreover, we show how algorithms compare differently in
terms of accuracy and fairness, depending on the biases affecting the data.
Finally, we note that under specific data bias conditions, simple
pre-processing interventions can successfully balance group-wise error rates,
while the same techniques fail in more complex settings.

机器学习算法应用广泛，但也可能存在偏见和不公平。本研究提出了一种刻画数据偏见的分类法，并探究了算法的不公平和精度之间的权衡。实证研究表明，在不同的数据偏见设置下，算法的公平性和精度会受到不同的影响，而简单的预处理干预技术可以提高算法的公平性。

通过模型和数据偏差交互理解欺诈检测中的不公平性

Understanding Unfairness in Fraud Detection through Model and Data Bias  Interactions

As multi-task models gain popularity in a wider range of machine learning
applications, it is becoming increasingly important for practitioners to
understand the fairness implications associated with those models. Most
existing fairness literature focuses on learning a single task more fairly,
while how ML fairness interacts with multiple tasks in the joint learning
setting is largely under-explored. In this paper, we are concerned with how
group fairness (e.g., equal opportunity, equalized odds) as an ML fairness
concept plays out in the multi-task scenario. In multi-task learning, several
tasks are learned jointly to exploit task correlations for a more efficient
inductive transfer. This presents a multi-dimensional Pareto frontier on (1)
the trade-off between group fairness and accuracy with respect to each task, as
well as (2) the trade-offs across multiple tasks. We aim to provide a deeper
understanding on how group fairness interacts with accuracy in multi-task
learning, and we show that traditional approaches that mainly focus on
optimizing the Pareto frontier of multi-task accuracy might not perform well on
fairness goals. We propose a new set of metrics to better capture the
multi-dimensional Pareto frontier of fairness-accuracy trade-offs uniquely
presented in a multi-task learning setting. We further propose a
Multi-Task-Aware Fairness (MTA-F) approach to improve fairness in multi-task
learning. Experiments on several real-world datasets demonstrate the
effectiveness of our proposed approach.

本文讨论了多任务学习中组公平性对于准确性的影响，提出了一种新的测量多维 Pareto 前沿的公平性 - 准确性权衡的方法，并提出了一种多任务感知公平性（MTA-F）方法来改善多任务学习中的公平性。实验结果表明，该方法的有效性。