Science as a tool for justice.

Elle Lett is the winner of the 2021 Rising Black Scientists Award for a post-graduate scholar. For this award, we asked emerging Black scientists to tell us about the experiences that sparked their interest in the life sciences, their vision and goals, and how they want to contribute to a more inclusive scientific community. This is her story.

Still we rise.

Olufolakemi "Fola" Olusanya is the winner of the first Rising Black Scientist Award for an undergraduate scholar. For this award, we asked emerging Black scientists to tell us about the experiences that sparked their journey in the life sciences. This is her story.

Transforming myself and academia for good.

Dr. Chrystal Starbird is the winner of the first Rising Black Scientist Award for a post-graduate scholar. For this award, we asked emerging Black scientists to tell us about the experiences that sparked their journey in the life sciences. This is her story.

Interdisciplinary approaches are fundamental to decode the biology of adversity.

The COVID-19 pandemic has highlighted structural inequalities and racism promoting health disparities among communities of color. Taking cardiovascular disease as an example, we provide a framework for multidisciplinary efforts leveraging translational and epidemiologic approaches to decode the biological impacts of inequalities and racism and develop targeted interventions that promote health equity.

Starting Conversations toward Inclusion.

Communal discussions on anti-racism and inclusion are crucial to addressing the history of racism in scientific communities. Unfortunately, universities are not universally implementing these conversations. We provide a blueprint for initiating and executing student-led discussions to empower young scientists to take action toward making science more welcoming and inclusive.

To See a Face Like Mine.

As an underrepresented scientist navigating her way through the field, I have either noticed or experienced barriers at key stages in the scientific journey that hinder the representation and visibility of diverse people in the community. To see a face like mine represented in science requires intentional action to turn a system not initially built to include all into a community that reflects, embraces, and celebrates people from all demographics.

Revising the a Priori Hypothesis: Systemic Racism Has Penetrated Scientific Funding.

To manifest our sincerest aspirations to "enhance health, lengthen life, and reduce illness and disability," the US biomedical research enterprise must directly confront the reality of structural racism in scientific funding and the widespread denial of its existence. I believe that moment in American history has, at long last, arrived.

Not All Champions Are Allies in Health Disparities Research.

Responsible health disparities research requires a multifaceted approach to address genetic, biological, socioeconomic, and lifestyle contributors to disease. For researchers dedicated to the cause, thorough experimental standards at the bench, in the clinic, and within the community must prioritize adequate representation and cultural competency. Not everyone is up for the challenge.

Race Matters.

Despite their initial high interest in science, students who belong to excluded racial and ethnic groups leave science at unacceptably high rates. "Fixing the student" approaches are not sufficient at stemming the loss. It is time to change the culture of science by putting inclusive diversity at the center.

AI generates covertly racist decisions about people based on their dialect.

Hundreds of millions of people now interact with language models, with uses ranging from help with writing1,2 to informing hiring decisions3. However, these language models are known to perpetuate systematic racial prejudices, making their judgements biased in problematic ways about groups such as African Americans4-7. Although previous research has focused on overt racism in language models, social scientists have argued that racism with a more subtle character has developed over time, particularly in the United States after the civil rights movement8,9. It is unknown whether this covert racism manifests in language models. Here, we demonstrate that language models embody covert racism in the form of dialect prejudice, exhibiting raciolinguistic stereotypes about speakers of African American English (AAE) that are more negative than any human stereotypes about African Americans ever experimentally recorded. By contrast, the language models' overt stereotypes about African Americans are more positive. Dialect prejudice has the potential for harmful consequences: language models are more likely to suggest that speakers of AAE be assigned less-prestigious jobs, be convicted of crimes and be sentenced to death. Finally, we show that current practices of alleviating racial bias in language models, such as human preference alignment, exacerbate the discrepancy between covert and overt stereotypes, by superficially obscuring the racism that language models maintain on a deeper level. Our findings have far-reaching implications for the fair and safe use of language technology.

Voices on Diversity: Combating Racial Inequality to Increase Diversity.

As part of our commitment to amplifying the voices of underrepresented scientists, we are publishing the insights and experiences of a panel of underrepresented scientists in a series of questions and answers. Here, they discuss ways that the scientific community can combat racial inequality and increase diversity. These are the personal opinions of the authors and may not reflect the views of their institutions.

Voices on diversity: confronting racial bias.

As part of our commitment to amplifying the voices of underrepresented scientists, we are publishing the insights and experiences of a panel of underrepresented scientists. Here, they discuss the impact of racial bias and share strategies for addressing it. These are the personal opinions of the authors and may not reflect the views of their institutions.

Black in Cancer: An interview with founders Sigourney Bell and Henry J. Henderson.

We talk to Sigourney Bell and Henry J. Henderson about what motivated them to found Black in Cancer, the importance of community and representation, as well as the resources the organization provides, future directions, and how we and our readers can provide support.

Social capital I: measurement and associations with economic mobility.

Social capital-the strength of an individual's social network and community-has been identified as a potential determinant of outcomes ranging from education to health1-8. However, efforts to understand what types of social capital matter for these outcomes have been hindered by a lack of social network data. Here, in the first of a pair of papers9, we use data on 21 billion friendships from Facebook to study social capital. We measure and analyse three types of social capital by ZIP (postal) code in the United States: (1) connectedness between different types of people, such as those with low versus high socioeconomic status (SES); (2) social cohesion, such as the extent of cliques in friendship networks; and (3) civic engagement, such as rates of volunteering. These measures vary substantially across areas, but are not highly correlated with each other. We demonstrate the importance of distinguishing these forms of social capital by analysing their associations with economic mobility across areas. The share of high-SES friends among individuals with low SES-which we term economic connectedness-is among the strongest predictors of upward income mobility identified to date10,11. Other social capital measures are not strongly associated with economic mobility. If children with low-SES parents were to grow up in counties with economic connectedness comparable to that of the average child with high-SES parents, their incomes in adulthood would increase by 20% on average. Differences in economic connectedness can explain well-known relationships between upward income mobility and racial segregation, poverty rates, and inequality12-14. To support further research and policy interventions, we publicly release privacy-protected statistics on social capital by ZIP code at https://www.socialcapital.org .

People who share encounters with racism are silenced online by humans and machines, but a guideline-reframing intervention holds promise.

Are members of marginalized communities silenced on social media when they share personal experiences of racism? Here, we investigate the role of algorithms, humans, and platform guidelines in suppressing disclosures of racial discrimination. In a field study of actual posts from a neighborhood-based social media platform, we find that when users talk about their experiences as targets of racism, their posts are disproportionately flagged for removal as toxic by five widely used moderation algorithms from major online platforms, including the most recent large language models. We show that human users disproportionately flag these disclosures for removal as well. Next, in a follow-up experiment, we demonstrate that merely witnessing such suppression negatively influences how Black Americans view the community and their place in it. Finally, to address these challenges to equity and inclusion in online spaces, we introduce a mitigation strategy: a guideline-reframing intervention that is effective at reducing silencing behavior across the political spectrum.