Controversies around race and machine learning have sparked debate among computer scientists over how to design machine learning systems that guarantee fairness. These debates rarely engage with how racial identity is embedded in our social experience, making for sociological and psychological complexity. This complexity challenges the paradigm of considering fairness to be a formal property of supervised learning with respect to protected personal attributes. Racial identity is not simply a personal subjective quality. For people labeled "Black" it is an ascribed political category that has consequences for social differentiation embedded in systemic patterns of social inequality achieved through both social and spatial segregation. In the United States, racial classification can best be understood as a system of inherently unequal status categories that places whites as the most privileged category while signifying the Negro/black category as stigmatized. Social stigma is reinforced through the unequal distribution of societal rewards and goods along racial lines that is reinforced by state, corporate, and civic institutions and practices. This creates a dilemma for society and designers: be blind to racial group disparities and thereby reify racialized social inequality by no longer measuring systemic inequality, or be conscious of racial categories in a way that itself reifies race. We propose a third option. By preceding group fairness interventions with unsupervised learning to dynamically detect patterns of segregation, machine learning systems can mitigate the root cause of social disparities, social segregation and stratification, without further anchoring status categories of disadvantage.

Nearly a decade into the new millennium, many traditionally black ghettos like Harlem, the Fillmore, and Chicago's South Side have experienced declining population and gentrification. Now seems like a fitting time to evaluate the conceptual merits of the term and the trajectory of research on the “ghetto.” Much of the research on poverty neighborhoods focuses on Chicago—but is Chicago's South Side representative of poverty neighborhoods (and ghettos) in other cities? Recently, this issue has been widely discussed on the Community and Urban Sociology listserve; as a follow‐up, we invited an international group of scholars to offer their views on the subject in this Symposium on the ghetto.

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The steady growth of the post‐war suburban Black middle class has been overshadowed by the mis‐characterization of the suburbs as conformist and racially homogeneous. Until recently, race remained an ever present yet unexplored dimension of studies of suburban communities. However, new suburban histories and a growing collection of black middle‐class suburban community case studies replace the monochrome descriptions of suburban life with an analysis that places the suburb within its regional, political, economic, and ideological landscape.