Racial Bias Research Articles

A Path Toward Racial Justice in Education: Anti-Racist Policy Decision Making in School Districts

The U.S. education system has been a critical site in the nation’s ongoing fight for racial equity. Yet, despite many attempts to promote equity within and across schools, efforts fall short in a system designed to uphold norms rooted in whiteness and white supremacy. We need anti-racist educational leaders who can identify and push back at the racial bias embedded in educational policies. Through a research-practice partnership with a Midwestern high school, we sought to understand how an anti-racist policy decision-making protocol can be used to redress inequitable policies to be racially just. The anti-racist policy decision-making protocol promotes social justice by empowering school practitioners to become policy agents. Implications from our findings point to the need for school practitioners to be critically introspective and identify and directly address the politics of whiteness that can ensue when working in partnership to do anti-racist policy change.

Regression with race-modifiers: towards equity and interpretability.

The pervasive effects of structural racism and racial discrimination are well-established and offer strong evidence that the effects of many important variables on health and life outcomes vary by race. Alarmingly, standard practices for statistical regression analysis introduce racial biases into the estimation and presentation of these race-modified effects. We advocate abundance-based constraints (ABCs) to eliminate these racial biases. ABCs offer a remarkable invariance property: estimates and inference for main effects are nearly unchanged by the inclusion of race-modifiers. Thus, quantitative researchers can estimate race-specific effects "for free"-without sacrificing parameter interpretability, equitability, or statistical efficiency. The benefits extend to prominent statistical learning techniques, especially regularization and selection. We leverage these tools to estimate the joint effects of environmental, social, and other factors on 4th end-of-grade readings scores for students in North Carolina ( n = 27, 638) and identify race-modified effects for racial (residential) isolation, PM 2.5 exposure, and mother's age at birth.

"I'm here. We're here. My people and I exist": Exploring the psychopolitical experiences of Black immigrant college students attending predominantly White institutions.

Despite the ever-growing presence of Black immigrant college students in the United States, little is known about their unique campus experiences or their mental health outcomes as it relates to psychopolitical determinants of well-being. This qualitative study aimed to explore the unique psychopolitical experiences of 14, first- and second-generation Black immigrant college students attending predominately White institutions in the United States. Using the theoretical frameworks of critical race theory, intersectionality, and psychopolitical validity to guide our study, a phenomenological approach uncovered six themes and 11 subthemes: (1) collective well-being, (2) relational well-being, (3) individual well-being (purpose; health and wellness; critical consciousness), (4) collective suffering (neocolonialism, oppressive policies, and political violence; institutional oppression; barriers to mental health care), (5) relational suffering (implicit racial bias, stereotypes, and microaggressions; violence and brutality), and (6) individual suffering (ethno-racial trauma; isolation; school-related stress). We highlight implications for practitioners working with Black immigrant college students such as providing culturally responsive mental health care and advocating for decolonized and antiracist trainings at predominately White institutions. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Racial Bias in Performance Ratings – Unpacking the Source and Direction of Inaccuracy

Racial Bias in Performance Ratings – Unpacking the Source and Direction of Inaccuracy

Journal Digest: Racial Bias in Child Psychiatry, Money Woes and Dementia, and more

Journal Digest: Racial Bias in Child Psychiatry, Money Woes and Dementia, and more

Examining Prospective Jurors: Sensitivities of Probing for Racial Bias

Trial by jury remains the symbolic center of the U.S. justice system. Here, readers bear witness to the process of voir dire, or jury questioning, and efforts to identify racial bias. The problem? Racial prejudice itself is taboo: jurors are reluctant to admit it, and judges and attorneys are reluctant to ask about it.

Contributors to Underdiagnosis of ADHD among Asian Americans: A Narrative Review

Objective Rates of ADHD are lowest among Asian American children (1–6.1%) compared to all other major ethnic and racial groups in the US, but there is limited literature on reasons for the disparity in estimated prevalence rates. Method: We conducted a narrative review to integrate the literature on ADHD in children in Asian countries with that on ADHD among Asian American youth to highlight potential explanations for disparities in ADHD diagnosis and treatment among Asian American children relative to other racial and ethnic groups. Results: Factors possibly contributing to the low estimated prevalence rates of ADHD among Asian American children include: a higher proportion of Inattentive ADHD presentation among Chinese, Malaysian, and Indian children; racial bias and the influence of the Model Minority Myth; cultural differences in classroom identification; mental health stigma in Asian American communities; parent perception of ADHD as misbehavior rather than a neurodevelopmental disorder; and parent support for children’s academic activities that may mask impairment. Conclusion: We offer recommendations to inform individual and community-level psychoeducation, and new directions for research to address this health disparity.

Developing a fair and interpretable representation of the clock drawing test for mitigating low education and racial bias

The clock drawing test (CDT) is a neuropsychological assessment tool to screen an individual’s cognitive ability. In this study, we developed a Fair and Interpretable Representation of Clock drawing test (FaIRClocks) to evaluate and mitigate classification bias against people with less than 8 years of education, while screening their cognitive function using an array of neuropsychological measures. In this study, we represented clock drawings by a priorly published 10-dimensional deep learning feature set trained on publicly available data from the National Health and Aging Trends Study (NHATS). These embeddings were further fine-tuned with clocks from a preoperative cognitive screening program at the University of Florida to predict three cognitive scores: the Mini-Mental State Examination (MMSE) total score, an attention composite z-score (ATT-C), and a memory composite z-score (MEM-C). ATT-C and MEM-C scores were developed by averaging z-scores based on normative references. The cognitive screening classifiers were initially tested to see their relative performance in patients with low years of education (< = 8 years) versus patients with higher education (> 8 years) and race. Results indicated that the initial unweighted classifiers confounded lower education with cognitive compromise resulting in a 100% type I error rate for this group. Thereby, the samples were re-weighted using multiple fairness metrics to achieve sensitivity/specificity and positive/negative predictive value (PPV/NPV) balance across groups. In summary, we report the FaIRClocks model, with promise to help identify and mitigate bias against people with less than 8 years of education during preoperative cognitive screening.

Las dos caras de Lima: Discriminación racial en contexto de protesta social en Perú

Racial discrimination in Peru, especially during social protests, reflects structural inequalities and historical stigmatization of minority groups, where "terruqueo" and the media perpetuate stereotypes and justify repression, affecting social cohesion. In this sense, the intention to understand racial discrimination helped us identify and address the structural and cultural roots that perpetuate this phenomenon. The study adopted a hermeneutic qualitative approach, analyzing two contradictory videos during the protests of July 29, 2023, in the capital of Peru. Non-participant observation and qualitative coding were used to examine 208 comments, allowing for a detailed interpretation of the social and cultural dynamics involved. The findings evidenced that during a protest context, the prevalence of dehumanizing and pejorative adjectives, along with marked racial and class biases in public discourse, highlights a deep stigmatization of minority groups. Additionally, the influence of popular culture in perpetuating stereotypes, along with a clear tendency towards the criminalization and stigmatization of protesters, reflects how police and military repression is socially legitimized, justified, and favored. In this sense, we understand that racial discrimination acts as a "framework of racial delegitimization" that perpetuates inequalities and maintains social hierarchies through racially charged narratives and stereotypes, amplified by the media. Public discourse not only reflects but also exacerbates prejudices, legitimizing repressive responses and maintaining the "status quo" by the power groups that employ this tool as social control.

Mindful discipline: A pilot study exploring mindfulness and the reduction of punitive discipline for Black male students.

Racial disparities in school discipline are well known and widely studied. Studies find, for instance, that Black students in secondary grades experience the highest rates of exclusionary punishment compared with their peers (Losen, 2018). Despite what is known about the prevalence and causes of disparities, such as educator bias in discipline decisions, there is surprisingly little evidence about how to effectively shift these disparities in schools. This pilot study attempts to address this, exploring whether a brief mindfulness-based intervention for teachers may reduce the impacts of racial bias on their interpretations of behavior and subsequent recommendations for discipline after reviewing a vignette depicting a Black male adolescent. The effects of the mindfulness exercise on participants' interpretations and recommendations were examined through regression, and findings indicate a significant main effect of the treatment on both indicators. The preliminary findings of this exploratory study suggest that brief mindfulness training may help to reduce the impacts of racial bias on discipline decisions for Black students. Implications for both research and practice are discussed. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

“Somewhere along the line, your mask isn’t going to be fitting right”: institutional racism in Black narratives of traumatic brain injury rehabilitation across the practice continuum

BackgroundOver two decades of research about traumatic brain injury (TBI) rehabilitation emphasized the persistence of racial health disparities in functional outcomes that disproportionately impact Black populations without naming or addressing racism as the root problem. Further, the experiences of Black people with TBI have yet to be documented and accounted for in scientific scholarship from the perspectives of Black persons in Canada.PurposeThis study intended to examine the rehabilitation narratives of Black TBI survivors, family caregivers, and rehabilitation providers and use critical race theory as a conceptual framework to understand how anti-Black racism manifests in those experiences.MethodsThrough critical narrative inquiry informed by a critical constructivist paradigm and a critical race theory lens, in-depth narrative interviewing were conducted with seven survivors, three family caregivers, and four rehabilitation providers. Data were analyzed using reflexive thematic analysis within and across groups of participants to conceptualize themes and sub-themes.FindingsThemes captured how racism becomes institutionalized in TBI rehabilitation: (1) the institutional construction of deficient Black bodies, (2) the institutional construction of rehabilitation access, (3) the institutional investment in resisting and approximating whiteness in rehabilitation practice, and (4) the institutional construction of deficient Black futures.ConclusionStudy findings point to the dire need to ensure rehabilitation programs, services, and the delivery of care are not determined based on inequitable practices, racial biases and assumptions about Black people, which determine who deserves to get into rehabilitation and have opportunities to be supported in working towards living a full and meaningful life.

A Racial Model of Electoral Reform: The Relationship between Restrictive Voting Policies and Voter Confidence for Black and White Voters

Abstract In Crawford v. Marion County (2008), the Supreme Court ruled that voter identification requirements are lawful on the grounds that, even if they do not prevent fraud, they can instill confidence in voting. However, this reasoning ignores the historical and contemporary racial biases in the enactment and administration of restrictive voting policies in the United States. This legacy likely colors the extent to which Black and white voters see reforms as deterring fraud versus increasing suppression, and as such, how confident they are in election outcomes. We examine how increasing voter restrictions affects voter fraud beliefs and voter confidence among Black and white voters. We find that the enactment of restrictive policies decreases fraud beliefs among white voters but has no effect on fraud beliefs for Black voters; however, Black voters are less confident in elections when conducted with more restrictive policies while white voters are not. Finally, we find suggestive evidence that the racial gap in the effects of restrictive voting policies on voter confidence is partially undergirded by racial elite signaling.

The effect of academic outcomes, equity, and student demographics on parental preferences for schools: evidence from a survey experiment

Abstract How does competition for school resources, along with racial and socioeconomic biases, shape parental preferences for schools? In this article, I investigate how school attributes affect preferences and choice, which sheds light on the processes that maintain school segregation. To do so, I conduct a survey experiment that explores parental preferences and the tradeoffs inherent in the process of school selection using school profiles that resemble those available on widely used education data platforms. I find that parents hold the strongest positive preferences for learning opportunities and overall school achievement compared to other attributes, including school racial and socioeconomic composition. Additionally, though parents prefer schools that have higher equity rankings, highly equitable schools are less desirable to parents than schools with more status and learning opportunities. However, parents also hold independent racial and socioeconomic preferences and —on average—avoid schools with more students of color and low-income students. Furthermore, results suggest they are largely unwilling to make tradeoffs that would result in schools with higher fractions of students of color or low-income students. Taken together, this study links prior studies on the segregating effects of educational data with literatures on school segregation by illustrating the specific dimensions that drive school choice.

The Use of AI in Medicine: Health Data, Privacy Risks and More

In the era of advancements in artificial intelligence (AI) and machine learning, the healthcare industry has become one of the major areas where such technologies are being actively adopted and utilized. The global health care sector generated more than 2.3 zettabytes of data worldwide in 2020. Analysts estimate that the global market for artificial intelligence (AI) in medicine will grow to $13 billion by 2025, with a significant increase in newly established companies. Artificial intelligence in medicine is used to predict, detect and diagnose various diseases and pathologies. The sources of data can be various results of medical research (EEG, X-ray images, laboratory tests, e.g. tissues, etc.). At the same time, there are understandable concerns that AI will undermine the patient-provider relationship, contribute to the deskilling of providers, undermine transparency, misdiagnose or inappropriately treat because of errors within AI decision-making that are hard to detect, exacerbate existing racial or societal biases, or introduce algorithmic bias that will be hard to detect. Traditional research methods, general and special ones, with an emphasis on the comparative legal method, were chosen. For the AI to work it needs to be trained, and it’s learning from all sorts of information given to it. The main part of the information on which AI is trained is health data, which is sensitive personal data. The fact that personal data is qualified as sensitive personal data indicates the significance of the information contained, the high risks in case it’s leaking, and hence the need for stricter control and regulation. The article offers a detailed exploration of the legal implications of AI in medicine, highlighting existing challenges, the current state of regulation, and proposes future perspectives and recommendations for legislation adapted to the era of medical AI. Given the above, the study is divided into three parts: international framework, that will focus primarily on applicable WHO documents; risks and possible ways to minimize them, where the authors have tried to consider various issues related to the use of AI in medicine and find options to address them; and relevant case-study.

Provider Implicit Racial Bias in Pediatric Sickle Cell Disease.

This study is to (1) assess implicit racial bias among pediatric providers and (2) use virtual patient (VP) vignettes to determine the impact of implicit racial bias on clinical decision-making in pediatric sickle cell disease (SCD) pain care. This cross-sectional study was conducted at a mid-sized, freestanding children's hospital in the northeast. Participants (N = 52) were pediatric SCD providers (87% cisgender female, 90% White, M age = 38.78). Providers completed a demographic questionnaire, the race Implicit Association Test (IAT) with adult and child faces, and a measure of SCD explicit bias (5-point Likert scale). Providers also made clinical decisions for four VP vignettes depicting Black and White youth in the emergency department (ED) with either SCD or cancer pain. Frequency tables were calculated. On the race IAT, providers demonstrated a pro-White implicit bias for both adult (81%) and child (89%) faces. Responses to the explicit bias measure reflected low levels of agreement with negative stereotypes about SCD patients. No significant differences emerged in providers' pain treatment decisions for Black vs. White, or SCD vs. cancer VPs. Findings indicate pediatric providers harbor implicit racial bias similar to the general population. Findings from VP vignettes did not demonstrate that pain treatment decision-making differed based on race or diagnosis. This may be due to standardized protocols and procedures in the pediatric emergency setting. Future research is needed to clarify the role of implicit bias in clinical decision-making and the potential efficacy of treatment protocols in preventing biases from interfering with pediatric SCD pain care.

Investigating the Determinants of Nonaccidental Trauma Evaluation in Pediatric Femur Fractures.

Femoral fractures in children have the highest probability for abuse after skull fractures and humeral shaft fractures. However, the reported proportion of pediatric femur fractures that result from nonaccidental trauma (NAT) varies in the literature from 2% to 60%. The purpose of this study was to determine the patient characteristics associated with a physician's decision to conduct a NAT workup and challenge universal clinical practice guidelines stating that all children younger than 3 years with a diaphyseal femur fracture be evaluated for NAT. A retrospective review was conducted on patients aged 0 to 36 months diagnosed with a femur fracture between January 1, 2004, and April 9, 2019, at our institution. NAT was defined as a diagnosis of trauma that was nonaccidental in the medical chart after a positive workup by skeletal survey, ophthalmological examination, or other appropriate referral made by the institution's child abuse and neglect team. Patients with incomplete medical records were excluded. A total of 299 patients were identified, and 22 were excluded. 71% (197/277) were screened for NAT. Patients younger than 1 year were significantly more likely to undergo a NAT workup (P = 0.009; Odds Ratio [OR], 2.3; 95% CI, 1.2 to 4.4) and receive a positive result from the NAT workup (P < 0.0005; OR, 11.3; 95% CI, 4.6 to 27.4). Additional injuries were also a significant predictor of a positive NAT workup (P < 0.001; OR, 5.3; 95% CI, 2.4 to 11.9). Patient race/ethnicity, sex, and fracture type were not markedly associated with the physician's decision to conduct a NAT workup. Infants younger than 1 year with a femur fracture and children younger than 3 years presenting with a femur fracture and another injury are at increased risk of possible NAT. While clinical practice guidelines may minimize racial bias in NAT evaluation, this study demonstrates that universal NAT workups for walking-age children in the absence of other evidence of abuse may be unnecessary. This is a Level III retrospective cohort study.

Societal guardrails for AI? Perspectives on what we know about public opinion on artificial intelligence

Abstract Artificial intelligence (AI) not only holds immense potential for improving quality of life but also creates complex ethical, legal, and societal challenges. AI has gained significant attention recently, particularly by introducing ChatGPT and other emerging applications. This paper offers a comprehensive overview of public opinion trends on AI, drawing from fifteen surveys conducted in the USA over the past 4 years. The findings reveal a divided American public regarding AI’s applications. Americans generally call for more regulation and government oversight, expressing a need for increased measures before fully embracing AI. While specific AI applications, such as skin cancer screening, have been welcomed, most Americans emphasize the importance of addressing societal concerns such as racial bias and inequities before the widespread implementation of AI. The evolving landscape of AI necessitates ongoing monitoring of public sentiment and the consideration of societal implications.

Evaluating racial bias in clinical assessments

Evaluating racial bias in clinical assessments

Implicit bias and experience influence overall but not relative trustworthiness judgment of other-race faces

Impressions of trustworthiness are formed quickly from faces. To what extent are these impressions shared among observers of the same or different races? Although high consensus of trustworthiness evaluation has been consistently reported, recent studies suggested substantial individual differences. For instance, negative implicit racial bias and low contact experience towards individuals of the other race have been shown to be related to low trustworthiness judgments for other-race faces. This pre-registered study further examined the effects of implicit social bias and experience on trustworthiness judgments of other-race faces. A relatively large sample of White (N = 338) and Black (N = 299) participants completed three tasks: a trustworthiness rating task of faces, a race implicit association test, and a questionnaire of experience. Each participant rated trustworthiness of 100 White faces and 100 Black faces. We found that the overall trustworthiness ratings for other-race faces were influenced by both implicit bias and experience with individuals of the other-race. Nonetheless, when comparing to the own-race baseline ratings, high correlations were observed for the relative differences in trustworthiness ratings of other-race faces for participants with varied levels of implicit bias and experience. These results suggest differential impact of social concepts (e.g., implicit bias, experience) vs. instinct (e.g., decision of approach-vs-avoid) on trustworthiness impressions, as revealed by overall vs. relative ratings on other-race faces.

Fairness in Predicting Cancer Mortality Across Racial Subgroups

Machine learning has potential to transform cancer care by helping clinicians prioritize patients for serious illness conversations. However, models need to be evaluated for unequal performance across racial groups (ie, racial bias) so that existing racial disparities are not exacerbated. To evaluate whether racial bias exists in a predictive machine learning model that identifies 180-day cancer mortality risk among patients with solid malignant tumors. In this cohort study, a machine learning model to predict cancer mortality for patients aged 21 years or older diagnosed with cancer between January 2016 and December 2021 was developed with a random forest algorithm using retrospective data from the Mount Sinai Health System cancer registry, Social Security Death Index, and electronic health records up to the date when databases were accessed for cohort extraction (February 2022). Race category. The primary outcomes were model discriminatory performance (area under the receiver operating characteristic curve [AUROC], F1 score) among each race category (Asian, Black, Native American, White, and other or unknown) and fairness metrics (equal opportunity, equalized odds, and disparate impact) among each pairwise comparison of race categories. True-positive rate ratios represented equal opportunity; both true-positive and false-positive rate ratios, equalized odds; and the percentage of predictive positive rate ratios, disparate impact. All metrics were estimated as a proportion or ratio, with variability captured through 95% CIs. The prespecified criterion for the model's clinical use was a threshold of at least 80% for fairness metrics across different racial groups to ensure the model's prediction would not be biased against any specific race. The test validation dataset included 43 274 patients with balanced demographics. Mean (SD) age was 64.09 (14.26) years, with 49.6% older than 65 years. A total of 53.3% were female; 9.5%, Asian; 18.9%, Black; 0.1%, Native American; 52.2%, White; and 19.2%, other or unknown race; 0.1% had missing race data. A total of 88.9% of patients were alive, and 11.1% were dead. The AUROCs, F1 scores, and fairness metrics maintained reasonable concordance among the racial subgroups: the AUROCs ranged from 0.75 (95% CI, 0.72-0.78) for Asian patients and 0.75 (95% CI, 0.73-0.77) for Black patients to 0.77 (95% CI, 0.75-0.79) for patients with other or unknown race; F1 scores, from 0.32 (95% CI, 0.32-0.33) for White patients to 0.40 (95% CI, 0.39-0.42) for Black patients; equal opportunity ratios, from 0.96 (95% CI, 0.95-0.98) for Black patients compared with White patients to 1.02 (95% CI, 1.00-1.04) for Black patients compared with patients with other or unknown race; equalized odds ratios, from 0.87 (95% CI, 0.85-0.92) for Black patients compared with White patients to 1.16 (1.10-1.21) for Black patients compared with patients with other or unknown race; and disparate impact ratios, from 0.86 (95% CI, 0.82-0.89) for Black patients compared with White patients to 1.17 (95% CI, 1.12-1.22) for Black patients compared with patients with other or unknown race. In this cohort study, the lack of significant variation in performance or fairness metrics indicated an absence of racial bias, suggesting that the model fairly identified cancer mortality risk across racial groups. It remains essential to consistently review the model's application in clinical settings to ensure equitable patient care.