Evaluating fairness can be challenging in practice because the sensitive
attributes of data are often inaccessible due to privacy constraints. The go-to
approach that the industry frequently adopts is using off-the-shelf proxy
models to predict the missing sensitive attributes, e.g. Meta [Alao et al.,
2021] and Twitter [Belli et al., 2022]. Despite its popularity, there are three
important questions unanswered: (1) Is directly using proxies efficacious in
measuring fairness? (2) If not, is it possible to accurately evaluate fairness
using proxies only? (3) Given the ethical controversy over inferring user
private information, is it possible to only use weak (i.e. inaccurate) proxies
in order to protect privacy? Our theoretical analyses show that directly using
proxy models can give a false sense of (un)fairness. Second, we develop an
algorithm that is able to measure fairness (provably) accurately with only
three properly identified proxies. Third, we show that our algorithm allows the
use of only weak proxies (e.g. with only 68.85%accuracy on COMPAS), adding an
extra layer of protection on user privacy. Experiments validate our theoretical
analyses and show our algorithm can effectively measure and mitigate bias. Our
results imply a set of practical guidelines for practitioners on how to use
proxies properly. Code is available at this http URL.

本研究提出一种算法来利用仅有的三个适当识别出的虚拟属性来精确测量公平性和减轻偏见，并展示其允许仅使用虚弱代理来增加用户隐私。

缺失敏感属性的公平性​，使用弱代理足够且更好

Weak Proxies are Sufficient and Preferable for Fairness with Missing Sensitive Attributes

Fairness is an increasingly important concern as machine learning models are
used to support decision making in high-stakes applications such as mortgage
lending, hiring, and prison sentencing. This paper introduces a new open source
Python toolkit for algorithmic fairness, AI Fairness 360 (AIF360), released
under an Apache v2.0 license {this https URL). The main
objectives of this toolkit are to help facilitate the transition of fairness
research algorithms to use in an industrial setting and to provide a common
framework for fairness researchers to share and evaluate algorithms.
The package includes a comprehensive set of fairness metrics for datasets and
models, explanations for these metrics, and algorithms to mitigate bias in
datasets and models. It also includes an interactive Web experience
(this https URL) that provides a gentle introduction to the
concepts and capabilities for line-of-business users, as well as extensive
documentation, usage guidance, and industry-specific tutorials to enable data
scientists and practitioners to incorporate the most appropriate tool for their
problem into their work products. The architecture of the package has been
engineered to conform to a standard paradigm used in data science, thereby
further improving usability for practitioners. Such architectural design and
abstractions enable researchers and developers to extend the toolkit with their
new algorithms and improvements, and to use it for performance benchmarking. A
built-in testing infrastructure maintains code quality.

介绍了一种新的开源 Python 工具包 - AI Fairness 360 (AIF360)，用于支持决策性应用程序的算法公平性，该工具包包括公平度量标准、算法对数据集和模型执行偏差检测和修复以及参考指南和教程。