The Synapse Group at the University of Akron was formed to explore enlightened collaborations between art and science, and to probe the ideas, images, and mutual interests connecting art and science professionals and disciplines. This chapter presents selected artworks created by members of the group. A major theme of this chapter is visualizing water that is unseen, such as invisible underground water or imaginary virtual water. Also explained in the chapter are the inspiration processes by which those artworks were created. Yingcai Xiao University of Akron, USA
{"title":"Seeing the Unseen","authors":"Eric H. Holder","doi":"10.1162/daed_a_02045","DOIUrl":"https://doi.org/10.1162/daed_a_02045","url":null,"abstract":"The Synapse Group at the University of Akron was formed to explore enlightened collaborations between art and science, and to probe the ideas, images, and mutual interests connecting art and science professionals and disciplines. This chapter presents selected artworks created by members of the group. A major theme of this chapter is visualizing water that is unseen, such as invisible underground water or imaginary virtual water. Also explained in the chapter are the inspiration processes by which those artworks were created. Yingcai Xiao University of Akron, USA","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":"140269974","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 Beginning in the mid-1980s, scientific psychology underwent a revolution – the implicit revolution – that led to the development of methods to capture implicit bias: attitudes, stereotypes, and identities that operate without full conscious awareness or conscious control. This essay focuses on a single notable thread of discoveries from the Race Attitude Implicit Association Test (RA-IAT) by providing 1) the historical origins of the research, 2) signature and replicated empirical results for construct validation, 3) further validation from research in sociocognitive development, neuroscience, and computer science, 4) new validation from robust association between regional levels of race bias and socially significant outcomes, and 5) evidence for both short- and long-term attitude change. As such, the essay provides the first comprehensive repository of research on implicit race bias using the RA-IAT. Together, the evidence lays bare the hollowness of current-day actions to rectify disadvantage experienced by Black Americans at individual, institutional, and societal levels.
{"title":"The Science of Implicit Race Bias: Evidence from the Implicit Association Test","authors":"Kirsten N. Morehouse, M. Banaji","doi":"10.1162/daed_a_02047","DOIUrl":"https://doi.org/10.1162/daed_a_02047","url":null,"abstract":"Abstract Beginning in the mid-1980s, scientific psychology underwent a revolution – the implicit revolution – that led to the development of methods to capture implicit bias: attitudes, stereotypes, and identities that operate without full conscious awareness or conscious control. This essay focuses on a single notable thread of discoveries from the Race Attitude Implicit Association Test (RA-IAT) by providing 1) the historical origins of the research, 2) signature and replicated empirical results for construct validation, 3) further validation from research in sociocognitive development, neuroscience, and computer science, 4) new validation from robust association between regional levels of race bias and socially significant outcomes, and 5) evidence for both short- and long-term attitude change. As such, the essay provides the first comprehensive repository of research on implicit race bias using the RA-IAT. Together, the evidence lays bare the hollowness of current-day actions to rectify disadvantage experienced by Black Americans at individual, institutional, and societal levels.","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":"140082817","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}
{"title":"Preface: Recognizing Implicit Bias in the Scientific & Legal Communities","authors":"David Baltimore, David S. Tatel, A. Mazza","doi":"10.1162/daed_e_02043","DOIUrl":"https://doi.org/10.1162/daed_e_02043","url":null,"abstract":"","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":"140084096","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}
Rebecca C. Hetey, M. Hamedani, Hazel Rose Markus, Jennifer L Eberhardt
Abstract In this essay, we highlight the interplay between individuals' psychological processes and sociocultural systems in producing and maintaining racial bias. We use a conceptual tool we call the culture cycle to map these dynamics, and illustrate them with research and in-depth examples from our work reducing racial disparities in routine policing in Oakland, California. We feature the most common police encounter – the vehicle stop – and highlight evidence-based interventions we developed both to reduce the frequency of vehicle stops and mitigate racial disparities in stops. Throughout, we draw on our expertise in the social psychology of bias, culture, and inequality, as well as our experiences building research-driven partnerships with public- and private-sector leaders, to inform organizational and societal change.
{"title":"“When the Cruiser Lights Come On”: Using the Science of Bias & Culture to Combat Racial Disparities in Policing","authors":"Rebecca C. Hetey, M. Hamedani, Hazel Rose Markus, Jennifer L Eberhardt","doi":"10.1162/daed_a_02052","DOIUrl":"https://doi.org/10.1162/daed_a_02052","url":null,"abstract":"Abstract In this essay, we highlight the interplay between individuals' psychological processes and sociocultural systems in producing and maintaining racial bias. We use a conceptual tool we call the culture cycle to map these dynamics, and illustrate them with research and in-depth examples from our work reducing racial disparities in routine policing in Oakland, California. We feature the most common police encounter – the vehicle stop – and highlight evidence-based interventions we developed both to reduce the frequency of vehicle stops and mitigate racial disparities in stops. Throughout, we draw on our expertise in the social psychology of bias, culture, and inequality, as well as our experiences building research-driven partnerships with public- and private-sector leaders, to inform organizational and societal change.","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":"140085457","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}
{"title":"The Case for Data Visibility","authors":"Marcella Nunez-Smith","doi":"10.1162/daed_a_02046","DOIUrl":"https://doi.org/10.1162/daed_a_02046","url":null,"abstract":"","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":"140084155","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 Declining scholarly interest in intentional discrimination may be due to rapid growth of interest in systemic biases and implicit biases. Systemic biases are produced by organizational personnel doing their assigned jobs, but nevertheless causing adverse impacts to members of protected classes as identified in civil rights laws. Implicit biases are culturally formed stereotypes and attitudes that cause selective harms to protected classes while operating mostly outside of conscious awareness. Both are far more pervasive and responsible for much greater adversity than caused by overt, explicit bias, such as hate speech. Scientific developments may eventually influence jurisprudence to reduce effects of systemic and implicit biases, but likely not rapidly. We conclude by describing possibilities for executive leadership in both public and private sectors to ameliorate discrimination faster and more effectively than is presently likely via courts and legislation.
{"title":"Roles for Implicit Bias Science in Antidiscrimination Law","authors":"Anthony G. Greenwald, Tom Newkirk","doi":"10.1162/daed_a_02054","DOIUrl":"https://doi.org/10.1162/daed_a_02054","url":null,"abstract":"Abstract Declining scholarly interest in intentional discrimination may be due to rapid growth of interest in systemic biases and implicit biases. Systemic biases are produced by organizational personnel doing their assigned jobs, but nevertheless causing adverse impacts to members of protected classes as identified in civil rights laws. Implicit biases are culturally formed stereotypes and attitudes that cause selective harms to protected classes while operating mostly outside of conscious awareness. Both are far more pervasive and responsible for much greater adversity than caused by overt, explicit bias, such as hate speech. Scientific developments may eventually influence jurisprudence to reduce effects of systemic and implicit biases, but likely not rapidly. We conclude by describing possibilities for executive leadership in both public and private sectors to ameliorate discrimination faster and more effectively than is presently likely via courts and legislation.","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":"140086478","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}
{"title":"Introduction: Implicit Bias in the Context of Structural Racism","authors":"Goodwin Liu, Camara Phyllis Jones","doi":"10.1162/daed_a_02044","DOIUrl":"https://doi.org/10.1162/daed_a_02044","url":null,"abstract":"","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":"140086962","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 New technologies have fundamentally transformed the systems that govern modern life, from criminal justice to health care, housing, and beyond. Algorithmic advancements promise greater efficiency and purported objectivity, but they risk perpetuating dangerous biases. In response, the field of public interest technology has emerged to offer an interdisciplinary, human-centered, and equity-focused approach to technological innovation. This essay argues for the widespread adoption of public interest technology principles, including thinking critically about how and when technological solutions are deployed, adopting rigorous training to educate technologists on ethical and social context, and prioritizing the knowledge and experiences of communities facing the disproportionate harms or uneven benefits of technology. Tools being designed and deployed today will shape our collective future, and collaboration between philanthropy, government, storytellers, activists, and private-sector technologists is essential in ensuring that these new systems are as just as they are innovative.
{"title":"Deprogramming Implicit Bias: The Case for Public Interest Technology","authors":"Darren Walker","doi":"10.1162/daed_a_02059","DOIUrl":"https://doi.org/10.1162/daed_a_02059","url":null,"abstract":"Abstract New technologies have fundamentally transformed the systems that govern modern life, from criminal justice to health care, housing, and beyond. Algorithmic advancements promise greater efficiency and purported objectivity, but they risk perpetuating dangerous biases. In response, the field of public interest technology has emerged to offer an interdisciplinary, human-centered, and equity-focused approach to technological innovation. This essay argues for the widespread adoption of public interest technology principles, including thinking critically about how and when technological solutions are deployed, adopting rigorous training to educate technologists on ethical and social context, and prioritizing the knowledge and experiences of communities facing the disproportionate harms or uneven benefits of technology. Tools being designed and deployed today will shape our collective future, and collaboration between philanthropy, government, storytellers, activists, and private-sector technologists is essential in ensuring that these new systems are as just as they are innovative.","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":"140088330","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 Police departments tend to address operational challenges with training approaches, and implicit bias in policing is no exception. However, psychological scientists have found that implicit biases are very difficult to reduce in any lasting, meaningful way. Because they are difficult to change, and nearly impossible for the decision-maker to recognize, training to raise awareness or teach corrective strategies is unlikely to succeed. Recent empirical assessments of implicit bias trainings have shown, at best, no effect on racial disparities in officers' actions in the field. In the absence of effective training, a promising near-term approach for reducing racial disparities in policing is to reduce the frequency of actions most vulnerable to the influence of bias. Specifically, actions that allow relatively high discretion are most likely to be subject to bias-driven errors. Several cases across different policing domains reveal that when discretion is constrained in stop-and-search decisions, the impact of racial bias on searches markedly declines.
{"title":"Disrupting the Effects of Implicit Bias: The Case of Discretion & Policing","authors":"Jack Glaser","doi":"10.1162/daed_a_02053","DOIUrl":"https://doi.org/10.1162/daed_a_02053","url":null,"abstract":"Abstract Police departments tend to address operational challenges with training approaches, and implicit bias in policing is no exception. However, psychological scientists have found that implicit biases are very difficult to reduce in any lasting, meaningful way. Because they are difficult to change, and nearly impossible for the decision-maker to recognize, training to raise awareness or teach corrective strategies is unlikely to succeed. Recent empirical assessments of implicit bias trainings have shown, at best, no effect on racial disparities in officers' actions in the field. In the absence of effective training, a promising near-term approach for reducing racial disparities in policing is to reduce the frequency of actions most vulnerable to the influence of bias. Specifically, actions that allow relatively high discretion are most likely to be subject to bias-driven errors. Several cases across different policing domains reveal that when discretion is constrained in stop-and-search decisions, the impact of racial bias on searches markedly declines.","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":"140089894","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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