Analytic racecraft: Race-based averages create illusory group differences in perceptions of racism.

As diversity increases, people paradoxically perceive social groups as more similar

With globalization and immigration, societal contexts differ in sheer variety of resident social groups. Social diversity challenges individuals to think in new ways about new kinds of people and where their groups all stand, relative to each other. However, psychological science does not yet specify how human minds represent social diversity, in homogeneous or heterogenous contexts. Mental maps of the array of society’s groups should differ when individuals inhabit more and less diverse ecologies. Nonetheless, predictions disagree on how they should differ. Confirmation bias suggests more diversity means more stereotype dispersion: With increased exposure, perceivers’ mental maps might differentiate more among groups, so their stereotypes would spread out (disperse). In contrast, individuation suggests more diversity means less stereotype dispersion, as perceivers experience within-group variety and between-group overlap. Worldwide, nationwide, individual, and longitudinal datasets ( n = 12,011) revealed a diversity paradox: More diversity consistently meant less stereotype dispersion. Both contextual and perceived ethnic diversity correlate with decreased stereotype dispersion. Countries and US states with higher levels of ethnic diversity (e.g., South Africa and Hawaii, versus South Korea and Vermont), online individuals who perceive more ethnic diversity, and students who moved to more ethnically diverse colleges mentally represent ethnic groups as more similar to each other, on warmth and competence stereotypes. Homogeneity shows more-differentiated stereotypes; ironically, those with the least exposure have the most-distinct stereotypes. Diversity means less-differentiated stereotypes, as in the melting pot metaphor. Diversity and reduced dispersion also correlate positively with subjective wellbeing.

The Illusion of Asymmetric Insight

The Misconception:  You celebrate diversity and respect others’ points of view. The Truth: You are driven to create and form groups and then believe others are wrong just because they are oth…

Out-group homogeneity

The out-group homogeneity effect is the perception of out-group members as more similar to one another than are in-group members, i.e. "they are alike; we are diverse".[1] Perceivers tend to have impressions about the diversity or variability of group members around those central tendencies or typical attributes of those group members. Thus, outgroup stereotypicality judgments are overestimated, supporting the view that out-group stereotypes are overgeneralizations.[2] The term "outgroup homogeneity effect", "outgroup homogeneity bias" or "relative outgroup homogeneity" have been explicitly contrasted with "outgroup homogeneity" in general,[3] the latter referring to perceived outgroup variability unrelated to perceptions of the ingroup.

The Racialization of Print

Readers since Thomas Jefferson have believed it possible to judge entire races of people based on a single book, yet no one has attempted to explain the orig...

The Racist Beginnings of Standardized Testing | NEA

From grade school to college, students of color have suffered from the effects of biased testing.

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.

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.

Covert Racism in AI: How Language Models Are Reinforcing Outdated Stereotypes | Stanford HAI

Despite advancements in AI, new research reveals that large language models continue to perpetuate harmful racial biases, particularly against speakers of African American English.

Google AI Giving Staggeringly Racist “Advice” About Being Alone With Certain Groups of People

Google's AI Overviews feature is showing overtly racist responses about being alone with certain groups of people.

Misperceptions About Others

Perceptions about others play an important role in shaping people's attitudes and behaviors, as well as social norms more broadly. This review presents a meta-analysis of the recent empirical literature that examines perceptions about others in the field, covering over a million observations for a total of 434 elicited perceptions. We document a number of stylized facts. Misperceptions about others are widespread, asymmetric, much larger when about out-group members, and positively associated with one's own attitudes. Experimental treatments to recalibrate misperceptions generally work as intended; they sometimes lead to meaningful changes in behaviors, though this often occurs only immediately after the treatments. We discuss different conceptual frameworks that could explain the origin, persistence, and rigidity of misperceptions about others. We point to several directions for future research.

If you rise, I fall: Equality is prevented by the misperception that it harms advantaged groups

Nine preregistered studies ( n = 4197) demonstrate that advantaged group members misperceive equality as necessarily harming their access to resources and inequality as necessarily benefitting them. Only when equality is increased within their ingroup, instead of between groups, do advantaged group members accurately perceive it as unharmful. Misperceptions persist when equality-enhancing policies offer broad benefits to society or when resources, and resource access, are unlimited. A longitudinal survey of the 2020 U.S. voters reveals that harm perceptions predict voting against actual equality-enhancing policies, more so than voters’ political and egalitarian beliefs. Finally two novel-groups experiments reveal that advantaged participants’ harm misperceptions predict voting for inequality-enhancing policies that financially hurt them and against equality-enhancing policies that financially benefit them. Misperceptions persist even after an intervention to improve decision-making. This misperception that equality is necessarily zero-sum may explain why inequality prevails even as it incurs societal costs that harm everyone. , Advantaged groups misperceive equality as harming their resource access, which predicts voting against equality policies.

How the LAPD and Palantir Use Data to Justify Racist Policing

In a new book, a sociologist who spent months embedded with the LAPD details how data-driven policing techwashes bias.

Religious intolerance is 'bigger cause of prejudice than race', says report

Attitudes to faith said to drive negative perceptions more than nationality or ethnicity

So It Is, So It Shall Be: Group Regularities License Children's Prescriptive Judgments

Abstract When do descriptive regularities (what characteristics individuals have) become prescriptive norms (what characteristics individuals should have)? We examined children's (4–13 years) and adults' use of group regularities to make prescriptive judgments, employing novel groups (Hibbles and Glerks) that engaged in morally neutral behaviors (e.g., eating different kinds of berries). Participants were introduced to conforming or non‐conforming individuals (e.g., a Hibble who ate berries more typical of a Glerk). Children negatively evaluated non‐conformity, with negative evaluations declining with age (Study 1). These effects were replicable across competitive and cooperative intergroup contexts (Study 2) and stemmed from reasoning about group regularities rather than reasoning about individual regularities (Study 3). These data provide new insights into children's group concepts and have important implications for understanding the development of stereotyping and norm enforcement.