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1. Social-Structure Learning

   https://doi.org/10.1177/0963721420924481  

   Gershman, Samuel J.; Cikara, Mina (October 2020, Current Directions in Psychological Science)

   null (Ed.)

   Social-structure learning is the process by which social groups are identified on the basis of experience. Building on models of structure learning in other domains, we formalize this problem within a Bayesian framework. According to this framework, the probabilistic assignment of individuals to groups is computed by combining information about individuals with prior beliefs about group structure. Experiments with adults and children provide support for this framework, ruling out alternative accounts based on dyadic similarity. More broadly, we highlight the implications of social-structure learning for intergroup cognition, stereotype updating, and coalition formation. 

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2. Perceived warmth and competence predict callback rates in meta-analyzed North American labor market experiments

   https://doi.org/10.1371/journal.pone.0304723  

   Gallo, Marcos; Hausladen, Carina I; Hsu, Ming; Jenkins, Adrianna C; Ona, Vaida; Camerer, Colin F (July 2024, PLOS ONE)

   Otterbring, Tobias (Ed.)

   Extensive literature probes labor market discrimination through correspondence studies in which researchers send pairs of resumes to employers, which are closely matched except for social signals such as gender or ethnicity. Upon perceiving these signals, individuals quickly activate associated stereotypes. The Stereotype Content Model (SCM; Fiske 2002) categorizes these stereotypes into two dimensions: warmth and competence. Our research integrates findings from correspondence studies with theories of social psychology, asking: Can discrimination between social groups, measured through employer callback disparities, be predicted by warmth and competence perceptions of social signals? We collect callback rates from 21 published correspondence studies, varying for 592 social signals. On those social signals, we collected warmth and competence perceptions from an independent group of online raters. We found that social perception predicts callback disparities for studies varying race and gender, which are indirectly signaled by names on these resumes. Yet, for studies adjusting other categories like sexuality and disability, the influence of social perception on callbacks is inconsistent. For instance, a more favorable perception of signals like parenthood does not consistently lead to increased callbacks, underscoring the necessity for further research. Our research offers pivotal strategies to address labor market discrimination in practice. Leveraging the warmth and competence framework allows for the predictive identification of bias against specific groups without extensive correspondence studies. By distilling hiring discrimination into these two dimensions, we not only facilitate the development of decision support systems for hiring managers but also equip computer scientists with a foundational framework for debiasing Large Language Models and other methods that are increasingly employed in hiring processes. 

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3. Neural representations of others’ traits predict social decisions

   https://doi.org/10.1073/pnas.2116944119  

   Kobayashi, Kenji; Kable, Joseph W.; Hsu, Ming; Jenkins, Adrianna C. (May 2022, Proceedings of the National Academy of Sciences)

   To guide social interaction, people often rely on expectations about the traits of other people, based on markers of social group membership (i.e., stereotypes). Although the influence of stereotypes on social behavior is widespread, key questions remain about how traits inferred from social-group membership are instantiated in the brain and incorporated into neural computations that guide social behavior. Here, we show that the human lateral orbitofrontal cortex (OFC) represents the content of stereotypes about members of different social groups in the service of social decision-making. During functional MRI scanning, participants decided how to distribute resources across themselves and members of a variety of social groups in a modified Dictator Game. Behaviorally, we replicated our recent finding that inferences about others' traits, captured by a two-dimensional framework of stereotype content (warmth and competence), had dissociable effects on participants' monetary-allocation choices: recipients' warmth increased participants’ aversion to advantageous inequity (i.e., earning more than recipients), and recipients’ competence increased participants’ aversion to disadvantageous inequity (i.e., earning less than recipients). Neurally, representational similarity analysis revealed that others' traits in the two-dimensional space were represented in the temporoparietal junction and superior temporal sulcus, two regions associated with mentalizing, and in the lateral OFC, known to represent inferred features of a decision context outside the social domain. Critically, only the latter predicted individual choices, suggesting that the effect of stereotypes on behavior is mediated by inference-based decision-making processes in the OFC. 

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4. “Who’s Better at Math, Boys or Girls?”: Changes in Adolescents’ Math Gender Stereotypes and Their Motivational Beliefs from Early to Late Adolescence

   https://doi.org/10.3390/educsci13090866  

   Starr, Christine R; Gao, Yannan; Rubach, Charlott; Lee, Glona; Safavian, Nayssan; Dicke, Anna-Lena; Eccles, Jacquelynne S; Simpkins, Sandra D (September 2023, Education Sciences)

   Though adults tend to endorse the stereotype that boys are better than girls in math, children tend to favor their own gender or be gender egalitarian. When do individuals start endorsing the traditional stereotype that boys are better? Using two longitudinal U.S. datasets that span 1993 to 2011, we examined three questions: (1) What are the developmental changes in adolescents’ gender stereotypes about math abilities from early to late adolescence? (2) Do the developmental changes vary based on gender and race/ethnicity? (3) Are adolescents’ stereotypes related to their math motivational beliefs? Finally, (4) do these patterns replicate across two datasets that vary in historical time? Adolescents in grades 8/9 and 11 were asked whether girls or boys are better at math (n’s = 1186 and 23,340, 49–53% girls, 30–54% White, 13–60% Black, 1–22% Latinx, and 2% to 4% Asian). Early adolescents were more likely to be gender egalitarian or favor their own gender. By late adolescence, adolescents’ stereotypes typically shifted towards the traditional stereotype that boys are better. In terms of race/ethnicity, White and Asian adolescents significantly favored boys, whereas Black and Latinx adolescents were more likely to endorse gender egalitarian beliefs. Adolescents’ stereotypes were significantly related to their expectancy beliefs, negatively for girls and positively for boys. 

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5. Providing Fair Recourse over Plausible Groups

   https://doi.org/10.1609/aaai.v38i19.30175  

   Yetukuri, Jayanth; Hardy, Ian; Vorobeychik, Yevgeniy; Ustun, Berk; Liu, Yang (March 2024, Proceedings of the AAAI Conference on Artificial Intelligence)

   Machine learning models now automate decisions in applications where we may wish to provide recourse to adversely affected individuals. In practice, existing methods to provide recourse return actions that fail to account for latent characteristics that are not captured in the model (e.g., age, sex, marital status). In this paper, we study how the cost and feasibility of recourse can change across these latent groups. We introduce a notion of group-level plausibility to identify groups of individuals with a shared set of latent characteristics. We develop a general-purpose clustering procedure to identify groups from samples. Further, we propose a constrained optimization approach to learn models that equalize the cost of recourse over latent groups. We evaluate our approach through an empirical study on simulated and real-world datasets, showing that it can produce models that have better performance in terms of overall costs and feasibility at a group level. 

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