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Abstract Clinical prediction models (CPMs) are tools that compute the risk of an outcome given a set of patient characteristics and are routinely used to inform patients, guide treatment decision-making, and resource allocation. Although much hope has been placed on CPMs to mitigate human biases, CPMs may potentially contribute to racial disparities in decision-making and resource allocation. While some policymakers, professional organizations, and scholars have called for eliminating race as a variable from CPMs, others raise concerns that excluding race may exacerbate healthcare disparities and this controversy remains unresolved. The Guidance for Unbiased predictive Information for healthcare Decision-making and Equity (GUIDE) provides expert guidelines for model developers and health system administrators on the transparent use of race in CPMs and mitigation of algorithmic bias across contexts developed through a 5-round, modified Delphi process from a diverse 14-person technical expert panel (TEP). Deliberations affirmed that race is a social construct and that the goals of prediction are distinct from those of causal inference, and emphasized: the importance of decisional context (e.g., shared decision-making versus healthcare rationing); the conflicting nature of different anti-discrimination principles (e.g., anticlassification versus antisubordination principles); and the importance of identifying and balancing trade-offs in achieving equity-related goals with race-aware versus race-unaware CPMs for conditions where racial identity is prognostically informative. The GUIDE, comprising 31 key items in the development and use of CPMs in healthcare, outlines foundational principles, distinguishes between bias and fairness, and offers guidance for examining subgroup invalidity and using race as a variable in CPMs. This GUIDE presents a living document that supports appraisal and reporting of bias in CPMs to support best practice in CPM development and use.

Abstract There is debate over whether Asian American students face additional barriers, relative to white students, when applying to selective colleges. Here we present the results from analyzing 685,709 applications submitted over five application cycles to 11 highly selective colleges (the “Ivy-11”). We estimate that Asian American applicants had 28% lower odds of ultimately attending an Ivy-11 school than white applicants with similar academic and extracurricular qualifications. The gap was particularly pronounced for students of South Asian descent (49% lower odds). Given the high yield rates and competitive financial aid policies of the schools we consider, the disparity in attendance rates is likely driven, at least in part, by admissions decisions. In particular, we offer evidence that this pattern stems from two factors. First, many selective colleges give preference to the children of alumni in admissions. We find that white applicants were substantially more likely to have such legacy status than Asian applicants. Second, we identify geographic disparities potentially reflective of admissions policies that disadvantage students from certain regions of the United States. We hope these results inform discussions on equity in higher education.

In studies of discrimination, researchers often seek to estimate a causal effect of race or gender on outcomes. For example, in the criminal justice context, one might ask whether arrested individuals would have been subsequently charged or convicted had they been a different race. It has long been known that such counterfactual questions face measurement challenges related to omitted-variable bias, and conceptual challenges related to the definition of causal estimands for largely immutable characteristics. Another concern, which has been the subject of recent debates, is post-treatment bias: many studies of discrimination condition on apparently intermediate outcomes, like being arrested, that themselves may be the product of discrimination, potentially corrupting statistical estimates. There is, however, reason to be optimistic. By carefully defining the estimand—and by considering the precise timing of events—we show that a primary causal quantity of interest in discrimination studies can be estimated under an ignorability condition that may hold approximately in some observational settings. We illustrate these ideas by analyzing both simulated data and the charging decisions of a prosecutor’s office in a large county in the United States.

In almost every state, courts can jail those who fail to pay fines, fees, and other court debts-even those resulting from traffic or other non-criminal violations. While debtors' prisons for private debts have been widely illegal in the United States for more than 150 years, the effect of courts aggressively pursuing unpaid fines and fees is that many Americans are nevertheless jailed for unpaid debts. However, heterogeneous, incomplete, and siloed records have made it difficult to understand the scope of debt imprisonment practices. We culled data from millions of records collected through hundreds of public records requests to county jails to produce a first-of-its-kind dataset documenting imprisonment for court debts in three U.S. states. Using these data, we present novel order-of-magnitude estimates of the prevalence of debt imprisonment, finding that between 2005 and 2018, around 38,000 residents of Texas and around 8,000 residents of Wisconsin were jailed each year for failure to pay (FTP), with the median individual spending one day in jail in both Texas and Wisconsin. Drawing on additional data on FTP warrants from Oklahoma, we also find that unpaid fines and fees leading to debt imprisonment most commonly come from traffic offenses, for which a typical Oklahoma court debtor owes around $250, or $500 if a warrant was issued for their arrest.

In a response to our article “Political Ideology and Racial Preferences in Online Dating,” Kevin Lewis (2015) has raised concerns about various aspects of our analysis of user behavior on a national online dating site. The core of Lewis’ critique is that we do not report specifics of the sample, the website, and the analysis. Lewis also details numerous smaller points with respect to details of the analysis. The goal of this response is not to reply to every point that Lewis makes. As with any research project, our design decisions require tradeoffs, and readers should evaluate the evidence on their own. Rather, we use this space to make broader points about the general value for sociology of research using online data and how such research should be practiced.

What explains the relative persistence of same-race romantic relationships? One possible explanation is structural–this phenomenon could reflect the fact that social interactions are already stratified along racial lines–while another attributes these patterns to individual-level preferences. We present novel evidence from an online dating community involving more than 250,000 people in the United States about the frequency with which individuals both express a preference for same-race romantic partners and act to choose same-race partners. Prior work suggests that political ideology is an important correlate of conservative attitudes about race in the United States, and we find that conservatives, including both men and women and blacks and whites, are much more likely than liberals to state a preference for same-race partners. Further, conservatives are not simply more selective in general; they are specifically selective with regard to race. Do these stated preferences predict real behaviors? In general, we find that stated preferences are a strong predictor of a behavioral preference for same-race partners, and that this pattern persists across ideological groups. At the same time, both men and women of all political persuasions act as if they prefer same-race relationships even when they claim not to. As a result, the gap between conservatives and liberals in revealed same-race preferences, while still substantial, is not as pronounced as their stated attitudes would suggest. We conclude by discussing some implications of our findings for the broader issues of racial homogamy and segregation.