Implicit biases, expressed as differential treatment towards out-group members, are pervasive in human societies. These biases are often racial or ethnic in nature and create disparities and inequities across many aspects of life.

Recent research has revealed that implicit biases are, for the most part, driven by social contexts and local histories. However, it has remained unclear how and if the regular ways in which human societies self-organize in cities produce systematic variation in implicit bias strength.

Here we leverage extensions of the mathematical models of urban scaling theory to predict and test between-city differences in implicit racial biases. Our model comprehensively links scales of organization from city-wide infrastructure to individual psychology to quantitatively predict that cities that are (1) more populous, (2) more diverse, and (3) less segregated have lower levels of implicit biases.

We find broad empirical support for each of these predictions in U.S. cities for data spanning a decade of racial implicit association tests from millions of individuals.

We conclude that the organization of cities strongly drives the strength of implicit racial biases and provides potential systematic intervention targets for the development and planning of more equitable societies.

A. Stier, S. Sajjadi, F. Karimi, L. Bettencourt, M. G. Berman, City Population, Majority Group Size, and Residential Segregation Drive Implicit Racial Biases in U.S. Cities, Mansueto Institute for Urban Innovation Research Paper 36 (2023).