Abstract Children are not born harboring racial biases, but they are born learning. Young children, even infants, learn from the “mere observation” of other people's behavior. Nonverbal signals of racial biases are abundant in children's everyday social environments. Studies show that preschool children acquire social group biases when they observe other people's social interactions and nonverbal behaviors. These new findings have implications for child development and educational equity. Even before kindergarten, racial biases are caught even when not explicitly taught, suggesting the need for practical actions for parents, teachers, and others concerned about the transmission of racial bias across generations.
{"title":"Young Children & Implicit Racial Biases","authors":"Andrew N. Meltzoff, Walter S. Gilliam","doi":"10.1162/daed_a_02049","DOIUrl":"https://doi.org/10.1162/daed_a_02049","url":null,"abstract":"Abstract Children are not born harboring racial biases, but they are born learning. Young children, even infants, learn from the “mere observation” of other people's behavior. Nonverbal signals of racial biases are abundant in children's everyday social environments. Studies show that preschool children acquire social group biases when they observe other people's social interactions and nonverbal behaviors. These new findings have implications for child development and educational equity. Even before kindergarten, racial biases are caught even when not explicitly taught, suggesting the need for practical actions for parents, teachers, and others concerned about the transmission of racial bias across generations.","PeriodicalId":47980,"journal":{"name":"Daedalus","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140087232","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Explicitly prejudiced attitudes against Black Americans have declined gradually since the 1960s. Yet racial disparities and racial discrimination remain significant problems in the United States. How could discrimination and disparate outcomes remain constant even while racial prejudice decreased? Two prominent explanations have emerged to explain these puzzling trends. Sociologists have proposed that disparities and discrimination are perpetuated by systemic racism, or the policies, practices, and societal structures that disadvantage some racial groups compared with others. Simultaneously, psychologists have proposed that implicit biases may sustain discrimination even in the absence of explicit prejudice. In this essay, we explore newly discovered connections between systemic racism and implicit bias, how they challenge traditional views to reorient our understanding of implicit bias, and how they shed new light on strategies to reduce bias.
{"title":"Implicit Bias as a Cognitive Manifestation of Systemic Racism","authors":"Manuel J. Galvan, B. K. Payne","doi":"10.1162/daed_a_02051","DOIUrl":"https://doi.org/10.1162/daed_a_02051","url":null,"abstract":"Abstract Explicitly prejudiced attitudes against Black Americans have declined gradually since the 1960s. Yet racial disparities and racial discrimination remain significant problems in the United States. How could discrimination and disparate outcomes remain constant even while racial prejudice decreased? Two prominent explanations have emerged to explain these puzzling trends. Sociologists have proposed that disparities and discrimination are perpetuated by systemic racism, or the policies, practices, and societal structures that disadvantage some racial groups compared with others. Simultaneously, psychologists have proposed that implicit biases may sustain discrimination even in the absence of explicit prejudice. In this essay, we explore newly discovered connections between systemic racism and implicit bias, how they challenge traditional views to reorient our understanding of implicit bias, and how they shed new light on strategies to reduce bias.","PeriodicalId":47980,"journal":{"name":"Daedalus","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140086498","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract The civil rights movement spurred U.S. companies and universities to implement antidiscrimination programs. Beginning in the early 1960s, employers adopted anti bias training as their first line of defense against bigotry. Even then, there was substantial evidence that this approach was unlikely to lessen bias. In this essay, we discuss social science research on the effects of antibias training, as well as research on systemic approaches to reducing institutional discrimination based on insights from contact theory. As sociologist Samuel Stouffer and psychologist Gordon Allport, the progenitors of contact theory, might have predicted by the end of World War II, we find that interventions to change career systems to maximize intergroup contact can promote workplace equity.
{"title":"Retooling Career Systems to Fight Workplace Bias: Evidence from U.S. Corporations","authors":"Alexandra Kalev, Frank Dobbin","doi":"10.1162/daed_a_02056","DOIUrl":"https://doi.org/10.1162/daed_a_02056","url":null,"abstract":"Abstract The civil rights movement spurred U.S. companies and universities to implement antidiscrimination programs. Beginning in the early 1960s, employers adopted anti bias training as their first line of defense against bigotry. Even then, there was substantial evidence that this approach was unlikely to lessen bias. In this essay, we discuss social science research on the effects of antibias training, as well as research on systemic approaches to reducing institutional discrimination based on insights from contact theory. As sociologist Samuel Stouffer and psychologist Gordon Allport, the progenitors of contact theory, might have predicted by the end of World War II, we find that interventions to change career systems to maximize intergroup contact can promote workplace equity.","PeriodicalId":47980,"journal":{"name":"Daedalus","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140083149","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract The twenty-first century is witnessing rapid and deep change in the global economy. These changes require innovation-driven solutions and motivated, skilled workforces. The talents of every person will be required to support performance in every domain, and deliberate actions must be taken to address impediments to full engagement. Even with clear government policy and significant investments in encouraging representation and inclusion of diversity of race, sexual orientation, gender identity, and ability, progress continues to lag. This essay captures promising practices and recommendations for structural or systemic change punctuated with stories of leadership driven by the belief that implementing strategies to disrupt the effects of implicit bias are important to develop diverse, fully engaged populations.
{"title":"Implicit Bias versus Intentional Belief: When Morally Elevated Leadership Drives Transformational Change","authors":"Wanda A. Sigur, Nicholas M. Donofrio","doi":"10.1162/daed_a_02057","DOIUrl":"https://doi.org/10.1162/daed_a_02057","url":null,"abstract":"Abstract The twenty-first century is witnessing rapid and deep change in the global economy. These changes require innovation-driven solutions and motivated, skilled workforces. The talents of every person will be required to support performance in every domain, and deliberate actions must be taken to address impediments to full engagement. Even with clear government policy and significant investments in encouraging representation and inclusion of diversity of race, sexual orientation, gender identity, and ability, progress continues to lag. This essay captures promising practices and recommendations for structural or systemic change punctuated with stories of leadership driven by the belief that implementing strategies to disrupt the effects of implicit bias are important to develop diverse, fully engaged populations.","PeriodicalId":47980,"journal":{"name":"Daedalus","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140085596","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Skeptics point out that measures of implicit bias can only weakly predict discrimination. And it is true that under current technologies, the degree of correlation between implicit bias (for example, as measured by the Implicit Association Test) and discriminatory judgment and behavior is small to moderate. In this essay, I argue that these little effects nevertheless matter a lot, in two different senses. First, in terms of practical significance, small burdens can accumulate over time to produce a large impact in a person's life. When these impacts are integrated not only over time but double integrated over large populations, these little things become even more practically significant. Second, in terms of legal significance, an upgraded model of discrimination that incorporates implicit bias has started to reshape antidiscrimination law. This transformation reflects a commitment to “behavioral realism”: a belief that the law should reflect more accurate models of human thinking and behavior.
{"title":"Little Things Matter a Lot: The Significance of Implicit Bias, Practically & Legally","authors":"Jerry Kang","doi":"10.1162/daed_a_02055","DOIUrl":"https://doi.org/10.1162/daed_a_02055","url":null,"abstract":"Abstract Skeptics point out that measures of implicit bias can only weakly predict discrimination. And it is true that under current technologies, the degree of correlation between implicit bias (for example, as measured by the Implicit Association Test) and discriminatory judgment and behavior is small to moderate. In this essay, I argue that these little effects nevertheless matter a lot, in two different senses. First, in terms of practical significance, small burdens can accumulate over time to produce a large impact in a person's life. When these impacts are integrated not only over time but double integrated over large populations, these little things become even more practically significant. Second, in terms of legal significance, an upgraded model of discrimination that incorporates implicit bias has started to reshape antidiscrimination law. This transformation reflects a commitment to “behavioral realism”: a belief that the law should reflect more accurate models of human thinking and behavior.","PeriodicalId":47980,"journal":{"name":"Daedalus","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140086280","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Neuroscience is a fantastic tool for peeking inside our minds and unpacking the component processes that drive social group biases. Brain research is vital for studying racial bias because neuroscientists can investigate these questions without asking people how they think and feel, as some individuals may be unaware or reluctant to report it. For the past twenty-five years, neuroscientists have diligently mapped implicit racial bias's neural foundations. As with any new approach, the emergence of neuroscience in studying implicit racial bias has elicited excitement and skepticism: excitement about connecting social biases to biological machinery, and skepticism that neuroscience may provide little to our understanding of social injustice. In this essay, I dive into what we have learned about implicit racial bias from the brain and the limitations of our current approach. I conclude by discussing what is on the horizon for neuroscience research on racial bias and social injustice.
{"title":"Uncovering Implicit Racial Bias in the Brain: The Past, Present & Future","authors":"Jennifer T. Kubota","doi":"10.1162/daed_a_02050","DOIUrl":"https://doi.org/10.1162/daed_a_02050","url":null,"abstract":"Abstract Neuroscience is a fantastic tool for peeking inside our minds and unpacking the component processes that drive social group biases. Brain research is vital for studying racial bias because neuroscientists can investigate these questions without asking people how they think and feel, as some individuals may be unaware or reluctant to report it. For the past twenty-five years, neuroscientists have diligently mapped implicit racial bias's neural foundations. As with any new approach, the emergence of neuroscience in studying implicit racial bias has elicited excitement and skepticism: excitement about connecting social biases to biological machinery, and skepticism that neuroscience may provide little to our understanding of social injustice. In this essay, I dive into what we have learned about implicit racial bias from the brain and the limitations of our current approach. I conclude by discussing what is on the horizon for neuroscience research on racial bias and social injustice.","PeriodicalId":47980,"journal":{"name":"Daedalus","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140088694","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Debates in AI ethics often hinge on comparisons between AI and humans: which is more beneficial, which is more harmful, which is more biased, the human or the machine? These questions, however, are a red herring. They ignore what is most interesting and important about AI ethics: AI is a mirror. If a person standing in front of a mirror asked you, “Who is more beautiful, me or the person in the mirror?” the question would seem ridiculous. Sure, depending on the angle, lighting, and personal preferences of the beholder, the person or their reflection might appear more beautiful, but the question is moot. AI reflects patterns in our society, just and unjust, and the worldviews of its human creators, fair or biased. The question then is not which is fairer, the human or the machine, but what can we learn from this reflection of our society and how can we make AI fairer? This essay discusses the challenges to developing fairer AI, and how they stem from this reflective property.
{"title":"Mirror, Mirror, on the Wall, Who's the Fairest of Them All?","authors":"Alice Xiang","doi":"10.1162/daed_a_02058","DOIUrl":"https://doi.org/10.1162/daed_a_02058","url":null,"abstract":"Abstract Debates in AI ethics often hinge on comparisons between AI and humans: which is more beneficial, which is more harmful, which is more biased, the human or the machine? These questions, however, are a red herring. They ignore what is most interesting and important about AI ethics: AI is a mirror. If a person standing in front of a mirror asked you, “Who is more beautiful, me or the person in the mirror?” the question would seem ridiculous. Sure, depending on the angle, lighting, and personal preferences of the beholder, the person or their reflection might appear more beautiful, but the question is moot. AI reflects patterns in our society, just and unjust, and the worldviews of its human creators, fair or biased. The question then is not which is fairer, the human or the machine, but what can we learn from this reflection of our society and how can we make AI fairer? This essay discusses the challenges to developing fairer AI, and how they stem from this reflective property.","PeriodicalId":47980,"journal":{"name":"Daedalus","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140088933","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Among the general public and behavioral scientists alike, the Implicit Association Test (IAT) is the best known and most widely used tool for demonstrating implicit bias: the unintentional impact of social group information on behavior. More than forty million IATs have been completed at the Project Implicit research website. These public datasets are the most comprehensive documentation of IAT and self-reported bias scores in existence. In this essay, we describe the IAT procedure, summarize key findings using the IAT to document the pervasiveness and correlates of implicit bias, and discuss various ways to interpret IAT scores. We also highlight the most common uses of the IAT. Finally, we discuss unanswered questions and future directions for the IAT specifically, and implicit bias research more generally.
{"title":"The Implicit Association Test","authors":"Kate A. Ratliff, Colin Tucker Smith","doi":"10.1162/daed_a_02048","DOIUrl":"https://doi.org/10.1162/daed_a_02048","url":null,"abstract":"Abstract Among the general public and behavioral scientists alike, the Implicit Association Test (IAT) is the best known and most widely used tool for demonstrating implicit bias: the unintentional impact of social group information on behavior. More than forty million IATs have been completed at the Project Implicit research website. These public datasets are the most comprehensive documentation of IAT and self-reported bias scores in existence. In this essay, we describe the IAT procedure, summarize key findings using the IAT to document the pervasiveness and correlates of implicit bias, and discuss various ways to interpret IAT scores. We also highlight the most common uses of the IAT. Finally, we discuss unanswered questions and future directions for the IAT specifically, and implicit bias research more generally.","PeriodicalId":47980,"journal":{"name":"Daedalus","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140092562","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Two models have dominated portrayals of depression. The medical model views depression as a disease that has distinct symptoms with predictable courses and outcomes. It typically relies on brain-related explanations and responses, although many adherents also use social and psychological causes and treatments. A second model conceives of depression as the result of external stressors, loss events, and other problems of living that naturally subsides when these conditions improve. In this view, optimal responses lie in addressing the social conditions that underlie depressed states. In this essay, we examine how each edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM) since DSM-III in 1980 has blurred the medical and social approaches and conceived of all sorts of depressive symptoms as needing medicinal responses. Although the distinction between the social and medical types is often difficult to make, it is an essential first step in developing accurate conceptions of the two sides of depression.
{"title":"Two Sides of Depression: Medical & Social","authors":"A. Horwitz, Jerome C. Wakefield","doi":"10.1162/daed_a_02039","DOIUrl":"https://doi.org/10.1162/daed_a_02039","url":null,"abstract":"Abstract Two models have dominated portrayals of depression. The medical model views depression as a disease that has distinct symptoms with predictable courses and outcomes. It typically relies on brain-related explanations and responses, although many adherents also use social and psychological causes and treatments. A second model conceives of depression as the result of external stressors, loss events, and other problems of living that naturally subsides when these conditions improve. In this view, optimal responses lie in addressing the social conditions that underlie depressed states. In this essay, we examine how each edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM) since DSM-III in 1980 has blurred the medical and social approaches and conceived of all sorts of depressive symptoms as needing medicinal responses. Although the distinction between the social and medical types is often difficult to make, it is an essential first step in developing accurate conceptions of the two sides of depression.","PeriodicalId":47980,"journal":{"name":"Daedalus","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139294497","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract The well-being of American Indian and other Indigenous communities has long been compromised by ruthless processes of European colonial dispossession and subjugation. As a result, contemporary Indigenous communities contend with sometimes overwhelming degrees of demoralization, distress, and disability. The concept of Indigenous historical trauma has arisen during the past thirty years as an alternative mental health discourse that critically contests prevailing categories of psychological disability, psychiatric distress, and mental disorders (including addiction, trauma, and suicide). Indigenous adoption and promotion of historical trauma affords an explanatory account for community mental health inequities that designates the historical legacies of colonization as central for understanding contemporary Indigenous suffering. In so doing, Indigenous advocates of historical trauma creatively recast these problems as postcolonial pathologies, and ardently call for overdue advances in reconciliation, redress, and repair with respect to Indigenous Peoples. Ideally, such advances will be evidenced by societal transformations, structural reforms, and social justice that can enhance and ensure Indigenous futurity and well-being.
{"title":"Indigenous Historical Trauma: Alter-Native Explanations for Mental Health Inequities","authors":"Joseph P. Gone","doi":"10.1162/daed_a_02035","DOIUrl":"https://doi.org/10.1162/daed_a_02035","url":null,"abstract":"Abstract The well-being of American Indian and other Indigenous communities has long been compromised by ruthless processes of European colonial dispossession and subjugation. As a result, contemporary Indigenous communities contend with sometimes overwhelming degrees of demoralization, distress, and disability. The concept of Indigenous historical trauma has arisen during the past thirty years as an alternative mental health discourse that critically contests prevailing categories of psychological disability, psychiatric distress, and mental disorders (including addiction, trauma, and suicide). Indigenous adoption and promotion of historical trauma affords an explanatory account for community mental health inequities that designates the historical legacies of colonization as central for understanding contemporary Indigenous suffering. In so doing, Indigenous advocates of historical trauma creatively recast these problems as postcolonial pathologies, and ardently call for overdue advances in reconciliation, redress, and repair with respect to Indigenous Peoples. Ideally, such advances will be evidenced by societal transformations, structural reforms, and social justice that can enhance and ensure Indigenous futurity and well-being.","PeriodicalId":47980,"journal":{"name":"Daedalus","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139297143","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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