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。