About

Tessa Charlesworth is an Assistant Professor of Management and Organizations at the Kellogg School of Management, Northwestern University. Her research focuses on understanding how and why thoughts (beliefs) and feelings (attitudes) change over time, with particular attention to biases about social groups and the processes that lead to shifts towards or away from prejudice. Her work adopts a multi-level and multi-method approach to explore change within individuals, organizations, and broader social collectives. Her research has been published in leading outlets such as Proceedings of the National Academy of Sciences, Psychological Science, Harvard Business Review, and American Psychologist, and has been featured in popular press including Scientific American, NPR, The New York Times, and Forbes. Professor Charlesworth earned her B.A. in Psychology, summa cum laude and phi beta kappa, from Columbia University, and her Ph.D. in Social Psychology from Harvard University, where she received the dissertation award from the Federation of Associations in Behavioral and Brain Sciences. She was awarded a postdoctoral fellowship from the Social Sciences and Humanities Research Council of Canada, conducted at the University of Toronto and Harvard University. Her research has been recognized with awards such as the Rising Star Award from the Association for Psychological Science and the Best Dissertation Award from the Federation of Associations in Behavioral and Brain Sciences.

Research topics

• Developmental psychology
• Psychology
• Sociology
• Computer Science
• Social psychology
• Political Science
• Artificial Intelligence
• Gender studies
• Linguistics

Selected publications

• Author response for "What Is Rationality, Whom Is It Ascribed To, and Why Does It Matter? Evidence From Internet Text for 66 Social Groups and 101 Occupations"

  2025-03-03

  peer-reviewSenior author
  PublisherDOI

• Biased AI Outputs Can Impact Humans’ Implicit Bias: A Case Study of the Impact of Gender-Biased Text-to-Image Generators

  Proceedings of the AAAI/ACM Conference on AI Ethics and Society · 2025-10-15

  articleOpen access

  A wave of recent work demonstrates that text-to-image generators (i.e., t2i) can perpetuate and amplify stereotypes about social groups. This research asks: what are the implications of biased t2i for humans who interact with these systems? Across three human-subjects studies, 1,881 participants engaged in a simulated t2i interaction in which the output was controlled to appear either stereotypic, gender-balanced, or counter-stereotypic, via the ratio of perceived women and men in the output of occupation prompts (e.g., a physicist). We then measured people's implicit gender bias using a gender-brilliance implicit association task (IAT), a bias that both relates to stereotypic occupation output in t2i and that has implications for women's representation in different fields. Participants who interacted with neutral t2i output (including only gender-neutral objects, e.g., DVDs) showed relatively high implicit gender-brilliance bias at baseline. Stereotypic t2i output did not increase implicit gender bias relative to this baseline (Study 1). However, participants exposed to counter-stereotypic t2i output had significantly lower implicit gender bias than participants exposed to only gender-neutral output (Studies 1 and 2). Although counter-stereotypic t2i may reduce implicit gender bias amongst users, less than 5% of participants actually preferred the counter-stereotypic representations of women and men. Instead, most participants preferred representations that accurately reflect gender distributions in society or that are more gender-balanced (Study 3). This work demonstrates a novel approach to studying human-AI interaction and reveals important insights for designing generative AI that seeks to mitigate harm. In particular, these findings have implications for understanding the impact of stereotypic t2i on human users, bias mitigation strategies via counter-stereotypic t2i output, and how these impacts (mis)align with people's preferences for t2i representations.

  PublisherOA PDFDOI

• Innovations in Intersectionality: Examining the Interplay of Identities in Organizational Research

  Academy of Management Proceedings · 2025-07-01

  article

  We all embody multiple identities, both visible and invisible, that influence our experiences in interpersonal interactions, social groups, and organizations. These identities intersect in complex ways to shape our perceptions, opportunities, and challenges. Despite this reality, the vast majority of research in both micro- and macro-organizational behavior has focused on single identity categories in an effort to simplify experimental designs, data demands, and inferences. Excitingly, new advances in theory and methodology are now enhancing our ability to take on the complexities of intersectionality research. This symposium brings together leading scholars who are advancing how we conceptualize, examine, and apply intersectionality in organizational research. The symposium papers utilize a wide array of research methodologies (lab experiments, natural language processing, interviews) and contribute to multiple bodies of literature (organizational behavior, psychology, linguistics), demonstrating both theoretically and methodologically diverse research. A Lens Model of Intersectional Stereotyping: Theoretical Arguments and Organizational Applications Author: Christopher Petsko; The Theory of Gendered Prejudice Redux: An Organizational Behavior Perspective Author: Sa-kiera Tiarra Jolynn Hudson; UC Berkeley, Haas School of Business Relational (In)Visibility: Variation in the Invisibility of Black Women in Women Versus Black ERGs Author: Rebecca Ponce de Leon; Columbia Business School Author: Lumumba Seegars; Harvard Business School Body Weight Stereotypes at the Intersections of Gender, Race, and Class Author: Anne Zola; Northwestern University Author: Tessa Charlesworth; Northwestern University

  PublisherDOI

• The Stigma Stability Framework: An Integrated Theory of How and Why Society Transmits Stigma Across History

  Social and Personality Psychology Compass · 2025-03-01 · 5 citations

  articleOpen access1st authorCorresponding

  ABSTRACT Psychologists have long treated stigma—the labeling, stereotyping, separation, status loss, and discrimination of social groups—as a static process. Yet recent evidence has shown that, in fact, contemporary indicators of stigma (e.g., racial prejudice, violence against Jewish people) are strongly correlated with historical measures of stigmatization (e.g., slavery, anti‐Jewish pogroms, respectively), even over timespans of centuries. What explains this striking persistence? Here, we seek an answer to this question by reviewing the emerging, interdisciplinary body of social science research using big data and computational methods to study long‐term historical trends of stigma. We first review perspectives on why society is motivated to maintain (vs. change) stigma over history, as well as how stigma might be maintained to satisfy such motivations. Specifically, we present an integrated theory, the Stigma Stability Framework, which argues that stigma persists, on average, because society (1) devises new methods to stigmatize the same group (i.e., stigma reproducibility ) and/or (2) transfers stigma hydraulically between groups (i.e., stigma replacement ). We use this general framework to organize a diverse set of empirical findings from across the social sciences, which underscore the widespread prevalence of stigma persistence mechanisms. Finally, we close with a discussion of open questions for future research, including how researchers and practitioners can use an historical and multi‐level perspective on stigma persistence to design more effective stigma reduction strategies. Indeed, we argue that it is only by shedding light on historical processes that we might hope to durably alter stigmatization in the future.

  PublisherOA PDFDOI

• International stability and change in explicit and implicit attitudes: An investigation spanning 33 countries, five social groups, and 11 years (2009–2019).

  Journal of Experimental Psychology General · 2025-03-31 · 4 citations

  article

  Whether and when explicit (self-reported) and implicit (automatically revealed) social group attitudes can change has been a central topic of psychological inquiry over the past decades. Here, we take a novel approach to answering these longstanding questions by leveraging data collected via the Project Implicit International websites from 1.4 million participants across 33 countries, five social group targets (age, body weight, sexuality, skin tone, and race), and 11 years (2009-2019). Bayesian time-series modeling using Integrated Nested Laplace Approximation revealed changes toward less bias in all five explicit attitudes, ranging from a decrease of 18% for body weight to 43% for sexuality. By contrast, implicit attitudes showed more variation in trends: Implicit sexuality attitudes decreased by 36%; implicit race, age, and body weight attitudes remained stable; and implicit skin tone attitudes showed a curvilinear effect, first decreasing and then increasing in bias, with a 20% increase overall. These results suggest that cultural-level explicit attitude change is best explained by domain-general mechanisms (e.g., the adoption of egalitarian norms), whereas implicit attitude change is best explained by mechanisms specific to each social group target. Finally, exploratory analyses involving ecological correlates of change (e.g., population density and temperature) identified consistent patterns for all explicit attitudes, thus underscoring the domain-general nature of underlying mechanisms. Implicit attitudes again showed more variation, with body-related (age and body weight) and sociodemographic (sexuality, race, and skin tone) targets exhibiting opposite patterns. These insights facilitate novel theorizing about processes and mechanisms of cultural-level change in social group attitudes. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

  PublisherDOI

• Biases Propagate in Encoder-based Vision-Language Models: A Systematic Analysis From Intrinsic Measures to Zero-shot Retrieval Outcomes

  2025-01-01 · 1 citations

  articleOpen access

  To build fair AI systems we need to understand how social-group biases intrinsic to foundational encoder-based vision-language models (VLMs) manifest in biases in downstream tasks.In this study, we demonstrate that intrinsic biases in VLM representations systematically "carry over" or propagate into zero-shot retrieval tasks, revealing how deeply rooted biases shape a model's outputs.We introduce a controlled framework to measure this propagation by correlating (a) intrinsic measures of bias in the representational space with (b) extrinsic measures of bias in zero-shot text-to-image (TTI) and image-to-text (ITT) retrieval.Results show substantial correlations between intrinsic and extrinsic bias, with an average = 0.83 0.10.This pattern is consistent across 114 analyses, both retrieval directions, six social groups, and three distinct VLMs.Notably, we find that larger/better-performing models exhibit greater bias propagation, a finding that raises concerns given the trend towards increasingly complex AI models.Our framework introduces baseline evaluation tasks to measure the propagation of group and valence signals.Investigations reveal that underrepresented groups experience less robust propagation, further skewing their model-related outcomes.

  PublisherOA PDFDOI

• From Data to Discovery: Unsupervised Machine Learning's Role in Social Cognition

  2025-02-21

  preprintOpen accessSenior author

  The study of how cognition and society interact is a complex endeavor that demands multiple methods and tools. Yet research in social cognition has only begun to capitalize on unsupervised machine learning (UML) tools that can uncover hidden patterns in data. In this tutorial we introduce UML as a complementary approach to traditional statistical methods. We illustrate four methods (K-means clustering, DBSCAN, PCA, and Market Basket Analysis) applied to data from Project Implicit and the Implicit Association Test. In the process, we show how UML can identify patterns and relationships that conventional methods might overlook. Throughout, we provide clear (and openly available) code and highlight important researcher decision-points in implementing UML in social cognition work. By bringing the advances of UML into social cognition we will be better equipped to tackle larger, more diverse, or multi-level datasets that reveal the complexities of our social world.

  PublisherDOI

• Biases Propagate in Encoder-based Vision-Language Models: A Systematic Analysis From Intrinsic Measures to Zero-shot Retrieval Outcomes

  ArXiv.org · 2025-06-06

  preprintOpen access

  To build fair AI systems we need to understand how social-group biases intrinsic to foundational encoder-based vision-language models (VLMs) manifest in biases in downstream tasks. In this study, we demonstrate that intrinsic biases in VLM representations systematically ``carry over'' or propagate into zero-shot retrieval tasks, revealing how deeply rooted biases shape a model's outputs. We introduce a controlled framework to measure this propagation by correlating (a) intrinsic measures of bias in the representational space with (b) extrinsic measures of bias in zero-shot text-to-image (TTI) and image-to-text (ITT) retrieval. Results show substantial correlations between intrinsic and extrinsic bias, with an average $ρ$ = 0.83 $\pm$ 0.10. This pattern is consistent across 114 analyses, both retrieval directions, six social groups, and three distinct VLMs. Notably, we find that larger/better-performing models exhibit greater bias propagation, a finding that raises concerns given the trend towards increasingly complex AI models. Our framework introduces baseline evaluation tasks to measure the propagation of group and valence signals. Investigations reveal that underrepresented groups experience less robust propagation, further skewing their model-related outcomes.

  PublisherOA PDFDOI

• Moralizing partisanship when surrounded by copartisans versus in mixed company

  PNAS Nexus · 2025-03-27

  articleOpen access

  ) express these partisan moralization views. Critically, we compare the rates of partisan moralization not only when users are in contexts (subreddits) of their ingroup (e.g. r/democrats, r/vegetarian, r/Conservative, r/Hunting) but also when in mixed-company contexts populated mostly by users without partisan engagement (e.g. r/Music, r/Parenting). First, we developed four word embedding models-two for the users of each political side, one based on their comments in their ingroup contexts and one based on their comments in mixed-company contexts. Then, we evaluated the words of each model on two semantic dimensions, partisanship and morality, and we examined their correlation as an indicator of the expressed partisan moralization. Our first analysis demonstrated that LW users express moralized partisanship to a similar degree when surrounded by copartisans and when in mixed company. However, the moralized partisanship expressed by RW users in mixed company is weaker than that they express among copartisans, as well as that expressed by LW users in mixed company. In a second analysis, we divided partisan contexts based on whether they are inherently political (e.g. r/democrats) or not (e.g. r/vegetarian). This second analysis revealed that RW users express moralized partisanship more strongly than LW users in inherently political contexts, but right- and left-wingers are similar in nonpolitical partisan contexts. The discussion considers potential explanations for these asymmetries.

  PublisherOA PDFDOI

• Author response for "What Is Rationality, Whom Is It Ascribed To, and Why Does It Matter? Evidence From Internet Text for 66 Social Groups and 101 Occupations"

  2025-06-20

  peer-reviewSenior author
  PublisherDOI

Frequent coauthors

• Benedek Kurdi

  38 shared

• Mayan Navon

  29 shared

• Nicole Lofaro

  University of Florida

  28 shared

• Frank Kaiyuan Xu

  27 shared

• Anthony G. Greenwald

  27 shared

• Brian A. Nosek

  Center for Open Science

  27 shared

• Mahzarin R. Banaji

  Harvard University

  27 shared

• Aylin Caliskan

  4 shared

Awards & honors

• Rising Star Award, Association for Psychological Science
• Best Dissertation Award, Federation of Associations in Behav…