About

Jennifer L. Eberhardt is a Professor of Psychology at Stanford University and the author of the book "Biased: Uncovering the Hidden Prejudice That Shapes What We See, Think, and Do." Her research focuses on race, bias, and inequality, with a particular emphasis on how people grapple with race in various societal contexts including the criminal justice system, neighborhoods, schools, and workplaces. Through her work, she explores the underlying mechanisms of prejudice and its impact on perception and behavior.

Research topics

• Political Science
• Sociology
• Psychology
• Social psychology
• Law
• History
• Gender studies
• Medicine
• Gerontology
• Biology
• Genetics
• Developmental psychology
• Criminology
• Demography

Selected publications

• Tell, Don’t Show: Leveraging Language Models’ Abstractive Retellings to Model Literary Themes

  2025-01-01 · 1 citations

  articleOpen access

  Conventional bag-of-words approaches for topic modeling, like latent Dirichlet allocation (LDA), struggle with literary text.Literature challenges lexical methods because narrative language focuses on immersive sensory details instead of abstractive description or exposition: writers are advised to show, don't tell.We propose Retell, a simple, accessible topic modeling approach for literature.Here, we prompt resource-efficient, generative language models (LMs) to tell what passages show, thereby translating narratives' surface forms into higherlevel concepts and themes.By running LDA on LMs' retellings of passages, we can obtain more precise and informative topics than by running LDA alone or by directly asking LMs to list topics.To investigate the potential of our method for cultural analytics, we compare our method's outputs to expert-guided annotations in a case study on racial/cultural identity in high school English language arts books.

  PublisherOA PDFDOI

• Tell, Don't Show: Leveraging Language Models' Abstractive Retellings to Model Literary Themes

  ArXiv.org · 2025-05-29

  preprintOpen access

  Conventional bag-of-words approaches for topic modeling, like latent Dirichlet allocation (LDA), struggle with literary text. Literature challenges lexical methods because narrative language focuses on immersive sensory details instead of abstractive description or exposition: writers are advised to "show, don't tell." We propose Retell, a simple, accessible topic modeling approach for literature. Here, we prompt resource-efficient, generative language models (LMs) to tell what passages show, thereby translating narratives' surface forms into higher-level concepts and themes. By running LDA on LMs' retellings of passages, we can obtain more precise and informative topics than by running LDA alone or by directly asking LMs to list topics. To investigate the potential of our method for cultural analytics, we compare our method's outputs to expert-guided annotations in a case study on racial/cultural identity in high school English language arts books.

  PublisherOA PDFDOI

• Racial and Ethnic Representation in Literature Taught in US High Schools

  Journal of Cultural Analytics · 2025-03-19 · 1 citations

  articleOpen access

  We quantify the representation, or presence, of characters of color in English Language Arts (ELA) instruction in the United States to better understand possible racial/ethnic emphases and gaps in literary curricula. We contribute two datasets: the first consists of books listed in widely-adopted Advanced Placement (AP) Literature & Composition exams, and the second is a set of books taught by teachers surveyed from schools with substantial Black and Hispanic student populations. In addition to these book lists, we provide an unprecedented collection of hand-annotated sociodemographic labels of not only literary authors, but also their characters. We use computational methods to measure all main characters’ presence through three distinct and nuanced metrics: frequency, narrative perspective, and burstiness. Our annotations and measurements show that the sociodemographic composition of characters in books recommended by AP Literature has not shifted much for over twenty years. As a case study of how ELA curricula may deviate from the curricula prescribed by AP, our teacher-provided sample shows a greater emphasis on books featuring first-person, primary characters of color. We also find that only a few books in either dataset feature both White main characters and main characters of color. Arguably, these books may uphold a view of racial/ethnic segregation as a societal norm.

  PublisherOA PDFDOI

• Racial Disparities in the Discretionary Context of Traffic Stops: How Organizational Practices Shape Institutional Interactions

  Journal of Social Issues · 2025-09-01 · 1 citations

  articleOpen accessSenior authorCorresponding

  ABSTRACT Traffic stops are common and consequential for citizens’ legal socialization and for racial gaps in police‐community trust. Efforts to change the tenor of police interactions, however, may discount the discretionary context of stops—the degree of choice in the decision to stop a driver—and how organizations influence those circumstances. Discretionary stops entail more choice and thus create more ambiguity for the driver regarding the officer's intent. We examine racial disparities in the discretionary context of traffic stops, their disparate influence on community members’ impressions, and the power of departmental guidance to close them. We find that stops of Black (vs. White) drivers are more likely to be for high‐discretion equipment violations, and Black community members evaluate recordings of high‐discretion, but not low‐discretion, stops more negatively than White participants. At the same time, we find promising evidence that organizational directives to curtail equipment stops can reduce this disparity.

  PublisherOA PDFDOI

• Film intervention increases empathic understanding of formerly incarcerated people and support for criminal justice reform

  Proceedings of the National Academy of Sciences · 2024-10-21 · 6 citations

  articleOpen accessCorresponding

  Nuanced portrayals of stigmatized groups in media have been shown to reduce prejudice. In an online experiment (N = 749), we tested whether a feature film depicting incarcerated peoples' experiences in the criminal justice system can increase a) empathic accuracy and compassion toward people who have been incarcerated and b) support for criminal justice reform. We measured baseline empathic accuracy via a well-validated task, where participants infer the emotions of people sharing stories about difficult life events. All storytellers were formerly incarcerated and students. However, in half the videos we labeled them as "formerly incarcerated" and in the remaining half as "college student." We then surveyed people's baseline attitudes toward criminal justice reform. Next, we assigned participants to watch one of three films. The intervention film chronicled the true stories of Black men on death row. Two docudramas of similar length served as control films. Finally, participants completed the empathic accuracy task and survey again and were given the opportunity to sign a petition. Compared to those who watched a control film, participants who watched the intervention film more accurately inferred the emotions of storytellers labeled "formerly incarcerated," and increased their support for criminal justice reform. These effects held for conservative and liberal participants alike. However, the film had no effect on feelings of compassion. Together, these results demonstrate the power of narrative interventions to not only increase empathic accuracy for members of a severely stigmatized group, but to increase support for reforms designed to improve their lives.

  PublisherOA PDFDOI

• “When the Cruiser Lights Come On”: Using the Science of Bias & Culture to Combat Racial Disparities in Policing

  Daedalus · 2024-01-01 · 7 citations

  articleOpen accessSenior author

  Abstract In this essay, we highlight the interplay between individuals' psychological processes and sociocultural systems in producing and maintaining racial bias. We use a conceptual tool we call the culture cycle to map these dynamics, and illustrate them with research and in-depth examples from our work reducing racial disparities in routine policing in Oakland, California. We feature the most common police encounter – the vehicle stop – and highlight evidence-based interventions we developed both to reduce the frequency of vehicle stops and mitigate racial disparities in stops. Throughout, we draw on our expertise in the social psychology of bias, culture, and inequality, as well as our experiences building research-driven partnerships with public- and private-sector leaders, to inform organizational and societal change.

  PublisherOA PDFDOI

• Leveraging body-worn camera footage to assess the effects of training on officer communication during traffic stops

  PNAS Nexus · 2024-09-01 · 11 citations

  articleOpen accessSenior author

  Abstract Can training police officers on how to best interact with the public actually improve their interactions with community members? This has been a challenging question to answer. Interpersonal aspects of policing are consequential but largely invisible in administrative records commonly used for evaluation. In this study, we offer a solution: body-worn camera footage captures police–community interactions and how they might change as a function of training. Using this footage-as-data approach, we consider changes in officers’ communication following procedural justice training in Oakland, CA, USA, one module of which sought to increase officer-communicated respect during traffic stops. We applied natural language processing tools and expert annotations of traffic stop recordings to detect whether officers enacted the five behaviors recommended in this module. Compared with recordings of stops that occurred prior to the training, we find that officers employed more of these techniques in posttraining stops; officers were more likely to express concern for drivers’ safety, offer reassurance, and provide explicit reasons for the stop. These methods demonstrate the promise of a footage-as-data approach to capture and affect change in police–community interactions.

  PublisherOA PDFDOI

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

  Proceedings of the National Academy of Sciences · 2024-09-09 · 14 citations

  articleOpen accessSenior authorCorresponding

  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.

  PublisherOA PDFDOI

• Observers of social media discussions about racial discrimination condemn denial but also adopt it

  Scientific Reports · 2024-08-06 · 3 citations

  articleOpen accessSenior author

  Sharing experiences with racism (racial discrimination disclosure) has the power to raise awareness of discrimination and spur meaningful conversations about race. Sharing these experiences with racism on social media may prompt a range of responses among users. While previous work investigates how disclosure impacts disclosers and listeners, we extend this research to explore the impact of observing discussions about racial discrimination online-what we call vicarious race talk. In a series of experiments using real social media posts, we show that the initial response to racial discrimination disclosure-whether the response denies or validates the poster's perspective-influences observers' own perceptions and attitudes. Despite observers identifying denial as less supportive than validation, those who observed a denial response showed less responsive attitudes toward the poster/target (Studies 1-3) and less support for discussions about discrimination on social media in general (Studies 2-3). Exploratory findings revealed that those who viewed denial comments also judged the transgressor as less racist, and expressed less support and more denial in their own comments. This suggests that even as observers negatively judge denial, their perceptions of the poster are nonetheless negatively influenced, and this impact extends to devaluing the topic of discrimination broadly. We highlight the context of social media, where racial discrimination disclosure-and how people respond to it-may be particularly consequential.

  PublisherOA PDFDOI

• The dynamic nature of student discipline and discipline disparities

  Proceedings of the National Academy of Sciences · 2023-04-17 · 11 citations

  articleOpen access

  Researchers have long used end-of-year discipline rates to identify punitive schools, explore sources of inequitable treatment, and evaluate interventions designed to stem both discipline and racial disparities in discipline. Yet, this approach leaves us with a "static view"-with no sense of how disciplinary responses fluctuate throughout the year. What if daily discipline rates, and daily discipline disparities, shift over the school year in ways that could inform when and where to intervene? This research takes a "dynamic view" of discipline. It leverages 4 years of atypically detailed data regarding the daily disciplinary experiences of 46,964 students from 61 middle schools in one of the nation's largest school districts. Reviewing these data, we find that discipline rates are indeed dynamic. For all student groups, the daily discipline rate grows from the beginning of the school year to the weeks leading up to the Thanksgiving break, falls before major breaks, and grows following major breaks. During periods of escalation, the daily discipline rate for Black students grows significantly faster than the rate for White students-widening racial disparities. Given this, districts hoping to stem discipline and disparities may benefit from timing interventions to precede these disciplinary spikes. In addition, early-year Black-White disparities can be used to identify the schools in which Black-White disparities are most likely to emerge by the end of the school year. Thus, the results reported here provide insights regarding not only when to intervene, but where to intervene to reduce discipline rates and disparities.

  PublisherOA PDFDOI

Recent grants

• NIH Grant R21MH066747

  NIH · $393k · 2006

• Constructing Race

  NSF · $200k · 2000–2005

Frequent coauthors

• Hazel Rose Markus

  Stanford University

  43 shared

• Alia J. Crum

  Stanford University

  37 shared

• Emerson J. Hardebeck

  Antioch University Seattle

  36 shared

• Lauren C. Howe

  Swiss Finance Institute

  36 shared

• Rebecca C. Hetey

  9 shared

• Nicholas P. Camp

  University of Michigan–Ann Arbor

  9 shared

• Dan Jurafsky

  8 shared

• John D. E. Gabrieli

  Massachusetts Institute of Technology

  7 shared

Education

• B.A.

  University of Cincinnati

  1987

• Other

  Harvard University

  1990

• Ph.D.

  Harvard University

  1993