To reduce bias and promote equality, we cannot rely simply on changing individual minds (micro-level interventions), nor on waiting for governments to pass legislation (macro-level interventions). Instead, research suggests a critical but often-overlooked role can be played by leaders positioned at the “meso-level” between the populace and elites in power (e.g., middle managers, influencers, community mobilizers). Especially in contexts of political polarization and anti-elite backlash, these meso-level leaders occupy a uniquely trusted, connected, and persuasive position in change initiatives. After summarizing insights from micro- and macro-level interventions, I review empirical evidence and case studies demonstrating that meso-level leaders are uniquely helpful in: tailoring and adapting initiatives to suit local contexts, including by facilitating feedback loops between senior leaders and everyday people; and spreading adoption of new diversity and inclusion practices by acting as brokers between networks, and by making practices visible and concrete. To best leverage meso-level leaders’ potential, organizations and policy should ensure job security, promote risk-taking, formalize feedback, and empower leaders to be brokers and first movers towards practices of equality and inclusion.

Against the backdrop of increasing ethnic diversity in the U.S., we replicate, extend, and challenge previous examinations of the American = White/Foreign = Asian stereotype in the largest sample to date (N = 666,623 respondents) over 17 years (2007–2023). Six key findings emerged. First, a robust American = White association emerged on implicit (Cohen’s d = 0.50) and explicit (Cohen’s d = 0.51) measures. Second, the strength of this effect varied by respondents’ race/ethnicity with implicit stereotypes strongest among White respondents (Cohen’s d = 0.86) and absent among East Asian respondents (Cohen’s d = 0.02). Third, the strength of implicit stereotypes was modulated by age, religion, and ideology—older, Christian, and conservative respondents displayed stronger implicit American = White associations—but not gender or education. Fourth, respondents living in U.S. metropolitan areas with greater Asian representation or a history of voting for Democratic candidates exhibited weaker implicit American = White associations. Fifth, over the past 17 years, implicit and explicit American = White associations decreased by 41% and 47%, respectively, and 14/14 demographic subgroups changed towards neutrality. Finally, we observed suggestive evidence that implicit stereotype trends towards neutrality were temporarily disrupted during the COVID-19 pandemic for White Americans but not Asian Americans.

Scholars have extolled the virtues of rationality for centuries while also debating what rationality is and who is rational. Advancing these debates, we used word embeddings trained on 840 billion words of internet text-and validated with Prolific workers in the United States-to uncover the representation, group stereotypes, and occupational correlates of rationality at scale in naturalistic language. Four results emerged. First, rather than being synonymous with competence, representations of rationality included both an analytic/logic component and an interpersonal/trust component. Second, irrationality was not merely the opposite of rationality but contained its own unique subcomponents (volatility and unfairness). Third, rationality was consistently ascribed to high-power targets across 66 social groups. Last, rationality (especially its analytic component) was consistently associated with both earnings and wage gaps across 101 occupations. Associations with demographic representation were less consistent. Complementing normative approaches, these descriptive findings advance canonical debates about rationality, extending understanding of its components, stereotypes, and correlates.

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, Density-Based Clustering of Applications With Noise [DBSCAN], Principal Component Analysis [PCA], and Market Basket Analysis) applied to data from Project Implicit and the Implicit Association. 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 multilevel data sets that reveal the complexities of our social world.

Partisans tend to view their ingroup as moral and their outgroup as immoral. Here, we examine whether left-wing (LW) and right-wing (RW) Reddit users ( N > 1,000,000 ) 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.

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.

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).

Social group-based identities intersect. The meaning of "woman" is modulated by adding social class as in "rich woman" or "poor woman." How does such intersectionality operate at-scale in everyday language? Which intersections dominate (are most frequent)? What qualities (positivity, competence, warmth) are ascribed to each intersection? In this study, we make it possible to address such questions by developing a stepwise procedure, Flexible Intersectional Stereotype Extraction (FISE), applied to word embeddings (GloVe; BERT) trained on billions of words of English Internet text, revealing insights into intersectional stereotypes. First, applying FISE to occupation stereotypes across intersections of gender, race, and class showed alignment with ground-truth data on occupation demographics, providing initial validation. Second, applying FISE to trait adjectives showed strong androcentrism (Men) and ethnocentrism (White) in dominating everyday English language (e.g. White + Men are associated with 59% of traits; Black + Women with 5%). Associated traits also revealed intersectional differences: advantaged intersectional groups, especially intersections involving Rich, had more common, positive, warm, competent, and dominant trait associates. Together, the empirical insights from FISE illustrate its utility for transparently and efficiently quantifying intersectional stereotypes in existing large text corpora, with potential to expand intersectionality research across unprecedented time and place. This project further sets up the infrastructure necessary to pursue new research on the emergent properties of intersectional identities.

Abstract Although they are far from biological or social maturity, infants and children show surprising early-emerging capacities in social group cognition. This chapter reviews research on when and how infants and children categorize, evaluate, stereotype, and behave differently toward social groups defined by gender, race, age, and language. Research across these groups reveals three thematic conclusions. First, an early-emerging preference for the familiar (e.g., looking at faces most prevalent in infants’ environments), beyond similarity or in-group status. Second, generally similar trajectories across group targets (e.g., looking preferences at three to six months, evaluative associations formed around nine to twelve months) suggesting domain-general cognitive developments may scaffold infant social group cognition. Third, an additional internalization of culturally dominant beliefs and norms of fairness in early to middle childhood (e.g. the emergence of socially desirable, fair responding in middle childhood). Understanding social group cognition is advanced by understanding its origins in early in life.

Attitudes are intertwined with culture and language. But to what extent? Emerging perspectives in attitude research suggest that cultural representations in language are more related to implicitly measured (vs. explicitly measured) attitudes, and that such relationships persist across history and diverse languages. We offer a comprehensive test of these ideas by correlating (a) attitudes toward 55 topics (e.g., Rich/Poor, Dogs/Cats, Love/Money) from ~100,000 U.S. English-speaking participants with (b) representations of those same topics in word embeddings from contemporary English text, 200 years of English books, and 53 non-English languages. Strong and robust relationships emerged between representations in contemporary English and implicitly but not explicitly measured attitudes. Moreover, strong correlations with implicitly measured attitudes persisted across 200 years of books, and most non-English languages. Results provide new insights into the nature of implicitly measured attitudes and how they are intertwined with cultural representations that are relatively hidden in patterns of language across time and place.

Today, many social groups face negative stereotypes. Is such negativity a stable feature of society and, if so, what mechanisms maintain stability both within and across group targets? Answering these theoretically and practically important questions requires data on dozens of group stereotypes examined simultaneously over historical and societal scales, which is only possible through recent advances in Natural Language Processing. Across two studies, we use word embeddings from millions of English-language books over 100 years (1900-2000) and extract stereotypes for 58 stigmatized groups. Study 1 examines aggregate, societal-level trends in stereotype negativity by averaging across these groups. Results reveal striking persistence in aggregate negativity (no meaningful slope), suggesting that society maintains a stable level of negative stereotypes. Study 2 introduces and tests a new framework identifying potential mechanisms upholding stereotype negativity over time. We find evidence of two key sources of this aggregate persistence: within-group "reproducibility" (e.g., stereotype negativity can be maintained by using different traits with the same underlying meaning) and across-group "replacement" (e.g., negativity from one group is transferred to other related groups). These findings provide novel historical evidence of mechanisms upholding stigmatization in society and raise new questions regarding the possibility of future stigma change.

Abstract Attitudes are argued to reflect widespread cultural information – information that can be revealed through language from contemporary and historical time. Yet research directly quantifying the relationship between attitudes and language patterns at-scale remains limited. Here, we address the question of whether and how attitudes are related to language by combining data from implicit and explicit attitudes of >200,000 participants towards 55 topics alongside language representations from word embeddings trained on 9 contemporary text corpora and 200 years of historical books. Results show strong relationships between implicit attitudes and language patterns, persisting beyond explicit attitudes, cultural attitudes, and even across two centuries. Explicit attitudes were not related to language in any corpus or time-period. Theoretically, language and implicit attitudes reflect widespread and historically-persistent statistical regularities, while explicit attitudes are filtered through values/goals. And methodologically, results provide validation of emerging research using language to expand the study of implicit social cognition.

When viewing the state of the world today, social and behavioral scientists face a puzzling inconsistency: how can it be that evidence of discrimination persists in all significant aspects of life – from housing and jobs to healthcare and law enforcement – even though individuals and institutions adamantly stand for equality? Over the past two decades, research has demonstrated that at least part of the answer to this puzzle can be attributed to the implicit nature of biases – attitudes, beliefs, and identities that are less conscious and controllable but that nevertheless exist and shape behavior. Today, it is taken as a given that evidence is strong and substantial for the presence of implicit bias in the minds and behaviors of individuals. This chapter, however, reviews an emerging body of research that uses large-scale, aggregated data across millions of tests of implicit attitudes and beliefs to understand outcomes of socially significant systemic behaviors ranging from the police use of lethal force to infant healthcare to school suspensions and discipline. Methodologically, the studies quantify social and psychological processes acting in the real world and introduce data of unprecedented scope across geography and time. Theoretically, both the approach and findings of this research underscore a new meaning of the term systemic discrimination that recognizes how implicit bias both shapes and is shaped by broad structural systems and outcomes.

According to early theories, implicit (automatic) social attitudes are difficult if not impossible to change. Although this view has recently been challenged by research relying on experimental, developmental, and cultural approaches, relevant work remains siloed across research communities. As such, the time is ripe to systematize and integrate disparate (and seemingly contradictory) findings and to identify gaps in existing knowledge. To this end, we introduce a 3D framework classifying research on implicit attitude change by levels of analysis (individual vs. collective), sources of change (experimental, ontogenetic, and cultural), and timescales (short term vs. long term). This 3D framework highlights where evidence for implicit attitude change is more versus less well established and pinpoints directions for future research, including at the intersection of fields.

The social world is carved into a complex variety of groups each associated with unique stereotypes that persist and shift over time. Innovations in natural language processing (word embeddings) enabled this comprehensive study on variability and correlates of change/stability in both manifest and latent stereotypes for 72 diverse groups tracked across 115 years of four English-language text corpora. Results showed, first, that group stereotypes changed by a moderate-to-large degree in manifest content (i.e., top traits associated with groups) but remained relatively more stable in latent structure (i.e., average cosine similarity of top traits' embeddings and vectors of valence, warmth, or competence). This dissociation suggests new insights into how stereotypes and their consequences may endure despite documented changes in other aspects of group representations. Second, results showed substantial variability of change/stability across the 72 groups, with some groups revealing large shifts in manifest and latent content, but others showing near-stability. Third, groups also varied in how consistently they were stereotyped across texts, with some groups showing divergent content, but others showing near-identical representations. Fourth, this variability in change/stability across groups was predicted from a combination of linguistic (e.g., frequency of mentioning the group; consistency of group stereotypes across texts) and social (e.g., the type of group) correlates. Groups that were more frequently mentioned in text changed more than those rarely mentioned; sociodemographic groups changed more than other group types (e.g., body-related stigmas, mental illnesses, occupations), providing the first quantitative evidence of specific group features that may support historical stereotype change. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

For decades, researchers across the social sciences have sought to document and explain the worldwide variation in social group attitudes (evaluative representations, e.g., young– good /old– bad ) and stereotypes (attribute representations, e.g., male– science /female– arts ). Indeed, uncovering such country-level variation can provide key insights into questions ranging from how attitudes and stereotypes are clustered across places to why places vary in attitudes and stereotypes (including ecological and social correlates). Here, we introduce the Project Implicit:International ( PI:International ) dataset that has the potential to propel such research by offering the first cross-country dataset of both implicit (indirectly measured) and explicit (directly measured) attitudes and stereotypes across multiple topics and years. PI:International comprises 2.3 million tests for seven topics (race, sexual orientation, age, body weight, nationality, and skin-tone attitudes, as well as men/women–science/arts stereotypes) using both indirect (Implicit Association Test; IAT) and direct (self-report) measures collected continuously from 2009 to 2019 from 34 countries in each country’s native language(s). We show that the IAT data from PI:International have adequate internal consistency (split-half reliability), convergent validity (implicit–explicit correlations), and known groups validity. Given such reliability and validity, we summarize basic descriptive statistics on the overall strength and variability of implicit and explicit attitudes and stereotypes around the world. The PI:International dataset, including both summary data and trial-level data from the IAT, is provided openly to facilitate wide access and novel discoveries on the global nature of implicit and explicit attitudes and stereotypes.

Using word embeddings from 850 billion words in English-language Google Books, we provide an extensive analysis of historical change and stability in social group representations (stereotypes) across a long timeframe (from 1800 to 1999), for a large number of social group targets (Black, White, Asian, Irish, Hispanic, Native American, Man, Woman, Old, Young, Fat, Thin, Rich, Poor), and their emergent, bottom-up associations with 14,000 words and a subset of 600 traits. The results provide a nuanced picture of change and persistence in stereotypes across 200 y. Change was observed in the top-associated words and traits: Whether analyzing the top 10 or 50 associates, at least 50% of top associates changed across successive decades. Despite this changing content of top-associated words, the average valence (positivity/negativity) of these top stereotypes was generally persistent. Ultimately, through advances in the availability of historical word embeddings, this study offers a comprehensive characterization of both change and persistence in social group representations as revealed through books of the English-speaking world from 1800 to 1999.

Gender stereotypes are widely shared “collective representations” that link gender groups (e.g., male/female) with roles or attributes (e.g., career/family, science/arts). Such collective stereotypes, especially implicit stereotypes, are assumed to be so deeply embedded in society that they are resistant to change. Yet over the past several decades, shifts in real-world gender roles suggest the possibility that gender stereotypes may also have changed alongside such shifts. The current project tests the patterns of recent gender stereotype change using a decade (2007–2018) of continuously collected data from 1.4 million implicit and explicit tests of gender stereotypes (male-science/female-arts, male-career/female-family). Time series analyses revealed that, over just 10 years, both implicit and explicit male-science/female-arts and male-career/female-family stereotypes have shifted toward neutrality, weakening by 13%–19%. Furthermore, these trends were observed across nearly all demographic groups and in all geographic regions of the United States and several other countries, indicating worldwide shifts in collective implicit and explicit gender stereotypes.

The statistical regularities in language corpora encode well-known social biases into word embeddings. Here, we focus on gender to provide a comprehensive analysis of group-based biases in widely-used static English word embeddings trained on internet corpora (GloVe 2014, fastText 2017). Using the Single-Category Word Embedding Association Test, we demonstrate the widespread prevalence of gender biases that also show differences in: (1) frequencies of words associated with men versus women; (b) part-of-speech tags in gender-associated words; (c) semantic categories in gender-associated words; and (d) valence, arousal, and dominance in gender-associated words. First, in terms of word frequency: we find that, of the 1,000 most frequent words in the vocabulary, 77% are more associated with men than women, providing direct evidence of a masculine default in the everyday language of the English-speaking world. Second, turning to parts-of-speech: the top male-associated words are typically verbs (e.g., fight, overpower) while the top female-associated words are typically adjectives and adverbs (e.g., giving, emotionally). Gender biases in embeddings also permeate parts-of-speech. Third, for semantic categories: bottom-up, cluster analyses of the top 1,000 words associated with each gender. The top male-associated concepts include roles and domains of big tech, engineering, religion, sports, and violence; in contrast, the top female-associated concepts are less focused on roles, including, instead, female-specific slurs and sexual content, as well as appearance and kitchen terms. Fourth, using human ratings of word valence, arousal, and dominance from a ~20,000 word lexicon, we find that male-associated words are higher on arousal and dominance, while female-associated words are higher on valence.