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Machine learning is a means to derive artificial intelligence by discovering patterns in existing data. Here, we show that applying machine learning to ordinary human language results in human-like semantic biases. We replicated a spectrum of known biases, as measured by the Implicit Association Test, using a widely used, purely statistical machine-learning model trained on a standard corpus of text from the World Wide Web. Our results indicate that text corpora contain recoverable and accurate imprints of our historic biases, whether morally neutral as toward insects or flowers, problematic as toward race or gender, or even simply veridical, reflecting the status quo distribution of gender with respect to careers or first names. Our methods hold promise for identifying and addressing sources of bias in culture, including technology.

Starting with a new formulation for the mutual information (MI) between a pair of events, this paper derives alternative upper bounds and extends those to the case of two discrete random variables. Normalized mutual information (NMI) measures are then obtained from those bounds, emphasizing the use of least upper bounds. Conditional NMI measures are also derived for three different events and three different random variables. Since the MI formulation for a pair of events is always nonnegative, it can properly be extended to include weighted MI and NMI measures for pairs of events or for random variables that are analogous to the well-known weighted entropy. This weighted MI is generalized to the case of continuous random variables. Such weighted measures have the advantage over previously proposed measures of always being nonnegative. A simple transformation is derived for the NMI, such that the transformed measures have the value-validity property necessary for making various appropriate comparisons between values of those measures. A numerical example is provided.

Objective. To analyze the conceptual foundations, calculation, and interpretation of key risk and impact measures used in nursing research, emphasizing their relevance for evidence-based clinical practice. Content synthesis. This article reviews measures of risk or association, including relative risk, odds ratio, and hazard ratio, as well as absolute impact measures, such as risk difference, absolute and relative risk reduction, absolute risk increase, and numbers needed to treat and harm. Using examples from recent primary studies conducted in clinically relevant nursing contexts, the manuscript illustrates step-by-step calculations and interpretations, highlighting the complementary roles of relative and absolute measures in clinical decision-making. Conclusion. An integrated understanding of risk and impact measures is essential for critical appraisal of nursing research and to assess the real clinical relevance of interventions. The combined use of these measures supports more informed, safe, and context-sensitive nursing care decisions, reinforcing evidence-based nursing practice.

On March 8, 2020, there was a 650% increase in Twitter retweets using the term “Chinese virus” and related terms. On March 9, there was an 800% increase in the use of these terms in conservative news media articles. Using data from non-Asian respondents of the Project Implicit “Asian Implicit Association Test” from 2007–2020 (n = 339,063), we sought to ascertain if this change in media tone increased bias against Asian Americans. Local polynomial regression and interrupted time-series analyses revealed that Implicit Americanness Bias—or the subconscious belief that European American individuals are more “American” than Asian American individuals—declined steadily from 2007 through early 2020 but reversed trend and began to increase on March 8, following the increase in stigmatizing language in conservative media outlets. The trend reversal in bias was more pronounced among conservative individuals. This research provides evidence that the use of stigmatizing language increased subconscious beliefs that Asian Americans are “perpetual foreigners.” Given research that perpetual foreigner bias can beget discriminatory behavior and that experiencing discrimination is associated with adverse mental and physical health outcomes, this research sounds an alarm about the effects of stigmatizing media on the health and welfare of Asian Americans.

Theory suggests that teachers’ implicit racial attitudes affect their students, but large-scale evidence on U.S. teachers’ implicit biases and their correlates is lacking. Using nationwide data from Project Implicit, we found that teachers’ implicit White/Black biases (as measured by the implicit association test) vary by teacher gender and race. Teachers’ adjusted bias levels are lower in counties with larger shares of Black students. In the aggregate, counties in which teachers hold higher levels of implicit and explicit racial bias have larger adjusted White/Black test score inequalities and White/Black suspension disparities.

Background Lesbian, gay, bisexual, transgender and questioning (LGBTQ) individuals experience higher rates of health disparities. These disparities may be driven, in part, by biases of medical providers encountered in health care settings. Little is known about how medical, nursing, or dental students are trained to identify and reduce the effects of their own biases toward LGBTQ individuals. Therefore, a systematic review was conducted to determine the effectiveness of programs to reduce health care student or provider bias towards these LGBTQ patients. Methods The authors performed searches of online databases (MEDLINE/PubMed, PsycINFO, Web of Science, Scopus, Ingenta, Science Direct, and Google Scholar) for original articles, published in English, between March 2005 and February 2017, describing intervention studies focused on reducing health care student or provider bias towards LGBTQ individuals. Data extracted included sample characteristics (i.e., medical, nursing, or dental students or providers), study design (i.e., pre-post intervention tests, qualitative), program format, program target (i.e., knowledge, comfort level, attitudes, implicit bias), and relevant outcomes. Study quality was assessed using a five-point scale. Results The search identified 639 abstracts addressing bias among medical, nursing, and dental students or providers; from these abstracts, 60 articles were identified as medical education programs to reduce bias; of these articles, 13 described programs to reduce bias towards LGBTQ patients. Bias-focused educational interventions were effective at increasing knowledge of LGBTQ health care issues. Experiential learning interventions were effective at increasing comfort levels working with LGBTQ patients. Intergroup contact was effective at promoting more tolerant attitudes toward LGBTQ patients. Despite promising support for bias education in increasing knowledge and comfort levels among medical, nursing, and dental students or providers towards LGBTQ persons, this systematic review did not identify any interventions that assessed changes in implicit bias among students or providers. Conclusions Strategies for assessing and mitigating implicit bias towards LGBTQ patients are discussed and recommendations for medical, nursing, and dental school curricula are presented.

Implicit bias is often viewed as a hidden force inside people that makes them perform inappropriate actions. This perspective can induce resistance against the idea that people are implicitly biased and complicates research on implicit bias. I put forward an alternative perspective that views implicit bias as a behavioral phenomenon. more specifically, it is seen as behavior that is automatically influenced by cues indicative of the social group to which others belong. This behavioral perspective is less likely to evoke resistance because implicit bias is seen as something that people do rather than possess and because it clearly separates the behavioral phenomenon from its normative implications. Moreover, performance on experimental tasks such as the Implicit Association Test is seen an instance of implicitly biased behavior rather than a proxy of hidden mental biases. Because these tasks allow for experimental control, they provide ideal tools for studying the automatic impact of social cues on behavior, for predicting other instances of biased behavior, and for educating people about implicitly biased behavior. The behavioral perspective not only changes the way we think about implicit bias but also shifts the aims of research on implicit bias and reveals links with other behavioral approaches such as network modeling.

Dual-process models are increasingly popular in sociology as a framework for theorizing the role of automatic cognition in shaping social behavior. However, empirical studies using dual-process models often rely on ad hoc measures such as forced-choice surveys, observation, and interviews whose relationships to underlying cognitive processes are not fully established. In this article, we advance dual-process research in sociology by (1) proposing criteria for measuring automatic cognition, and (2) assessing the empirical performance of two popular measures of automatic cognition developed by psychologists. We compare the ability of the Brief Implicit Association Test (BIAT), the Affect Misattribution Procedure (AMP), and traditional forced-choice measures to predict process-pure estimates of automatic influences on individuals’ behavior during a survey task. Results from three studies focusing on politics, morality, and racial attitudes suggest the AMP provides the most valid and consistent measure of automatic cognitive processes. We conclude by discussing the implications of our findings for sociological practice.

People with autism spectrum disorder (ASD) experience stigmatization rooted in negative attitudes or prejudice toward them due to social awkwardness. However, little is known about implicit attitudes toward ASD, especially differences in attitudes compared to those of more visible conditions; physical disabilities. In this study, we implemented implicit association tests (IATs) to assess implicit attitudes. Sixty-three university students participated in IATs and answered questionnaires that measured explicit attitudes, social desirability, knowledge about—and familiarity with—disorders. The results demonstrated that implicit attitude toward ASD was significantly less negative than toward physical disabilities. Regarding the discrepancy, not socially awkward behavior but appearance of people with ASD can be evaluated as ‘in-group’ members and lead to less negative attitude compared with physical disabilities.