Racial diversity has become increasingly discussed within the AI and
algorithmic fairness literature, yet little attention is focused on justifying
the choices of racial categories and understanding how people are racialized
into these chosen racial categories. Even less attention is given to how racial
categories shift and how the racialization process changes depending on the
context of a dataset or model. An unclear understanding of \textit{who}
comprises the racial categories chosen and \textit{how} people are racialized
into these categories can lead to varying interpretations of these categories.
These varying interpretations can lead to harm when the understanding of racial
categories and the racialization process is misaligned from the actual
racialization process and racial categories used. Harm can also arise if the
racialization process and racial categories used are irrelevant or do not exist
in the context they are applied.
In this paper, we make two contributions. First, we demonstrate how racial
categories with unclear assumptions and little justification can lead to
varying datasets that poorly represent groups obfuscated or unrepresented by
the given racial categories and models that perform poorly on these groups.
Second, we develop a framework, CIRCSheets, for documenting the choices and
assumptions in choosing racial categories and the process of racialization into
these categories to facilitate transparency in understanding the processes and
assumptions made by dataset or model developers when selecting or using these
racial categories.

在人工智能和算法公平性的文献中，种族多样性已成为越来越受关注的议题，然而鲜有关注对种族类别的选择进行合理化解释以及如何让人们置于这些选取的种族类别中。更少的关注度放在种族类别如何转变以及种族化过程如何随着数据集或模型的背景而改变。对于选取种族类别中包含的人群和他们如何置于这些类别中的不清楚理解，可能会导致对这些类别的不同解读。当真实的种族化过程和使用的种族类别的理解与实际不符时，这些不同的解读可能会引发伤害。如果种族化过程和使用的种族类别在所应用的背景中无关或不存在，也可能引发伤害。在本文中，我们进行了两方面的贡献。首先，我们演示了不明确假设和不合理解释的种族类别如何导致各种数据集的多样性，这些数据集无法很好地代表被这些种族类别混淆或未被代表的群体，以及如何导致模型在这些群体上表现不佳。其次，我们开发了一个框架 CIRCSheets，用于记录选择种族类别和进行种族化过程的假设，以促进理解数据集或模型开发者在选择或使用这些种族类别时所做的过程和假设的透明度。

AI 与算法公平中的种族 / 族裔分类：其重要性与代表意义

Racial/Ethnic Categories in AI and Algorithmic Fairness: Why They Matter  and What They Represent

Computer vision is widely deployed, has highly visible, society altering
applications, and documented problems with bias and representation. Datasets
are critical for benchmarking progress in fair computer vision, and often
employ broad racial categories as population groups for measuring group
fairness. Similarly, diversity is often measured in computer vision datasets by
ascribing and counting categorical race labels. However, racial categories are
ill-defined, unstable temporally and geographically, and have a problematic
history of scientific use. Although the racial categories used across datasets
are superficially similar, the complexity of human race perception suggests the
racial system encoded by one dataset may be substantially inconsistent with
another. Using the insight that a classifier can learn the racial system
encoded by a dataset, we conduct an empirical study of computer vision datasets
supplying categorical race labels for face images to determine the
cross-dataset consistency and generalization of racial categories. We find that
each dataset encodes a substantially unique racial system, despite nominally
equivalent racial categories, and some racial categories are systemically less
consistent than others across datasets. We find evidence that racial categories
encode stereotypes, and exclude ethnic groups from categories on the basis of
nonconformity to stereotypes. Representing a billion humans under one racial
category may obscure disparities and create new ones by encoding stereotypes of
racial systems. The difficulty of adequately converting the abstract concept of
race into a tool for measuring fairness underscores the need for a method more
flexible and culturally aware than racial categories.

本文通过对计算机视觉中的人脸图像进行对比实验，发现虽然数据集中使用的种族类别表面上相似，但每个数据集编码了一个大不相同的种族系统，一些种族类别在数据集间的一致性程度比其他种族类别更低，并且种族类别可能存在刻板印象和忽略某些民族群体的问题，因此需要更具文化感知性和灵活性的公正度量方法。

一个标签，十亿人脸：计算机视觉中种族类别的使用和一致性

One Label, One Billion Faces: Usage and Consistency of Racial Categories  in Computer Vision

We examine the way race and racial categories are adopted in algorithmic
fairness frameworks. Current methodologies fail to adequately account for the
socially constructed nature of race, instead adopting a conceptualization of
race as a fixed attribute. Treating race as an attribute, rather than a
structural, institutional, and relational phenomenon, can serve to minimize the
structural aspects of algorithmic unfairness. In this work, we focus on the
history of racial categories and turn to critical race theory and sociological
work on race and ethnicity to ground conceptualizations of race for fairness
research, drawing on lessons from public health, biomedical research, and
social survey research. We argue that algorithmic fairness researchers need to
take into account the multidimensionality of race, take seriously the processes
of conceptualizing and operationalizing race, focus on social processes which
produce racial inequality, and consider perspectives of those most affected by
sociotechnical systems.

本文探讨算法公平框架中如何采纳种族和种族范畴，认为目前的方法未能充分考虑种族的社会构造性质，指出需要理解种族具有的多维性、关注种族不平等的社会过程，并关注最受社会技术系统影响的人的观点。