Subtle Biases Research Articles

Facing Microaggressions Head-On: Effective Strategies for Addressing Subtle Bias in the Workplace

This article discusses strategies for constructively addressing microaggressions, which are subtle verbal, nonverbal, or environmental slights that communicate hostile or derogatory messages to marginalized groups in the workplace. Microaggressions undermine diversity and inclusion efforts and have negative consequences for targets' well-being and companies' productivity and legal liability. The article defines common types of microaggressions and explains why they are important to address. It then outlines factors targets should consider when deciding whether and how to respond, and provides research-backed strategies such as respectfully educating the aggressor, describing personal impact, inquiring about intent, addressing systemic issues, setting boundaries, or removing oneself from the situation. Examples are given of how different strategies could be applied in healthcare, technology, education, non-profit and government contexts. The conclusion emphasizes that addressing microaggressions requires both individual interpersonal skills and organizational support through policies and training.

Subtle Biases Towards a New Colleague Depend on Sexual Orientation and Relationship Status

Abstract Background Allonormative beliefs entail the idea that all people desire sexual activity with other people. Single and/or asexual individuals may face social disapproval or discrimination for not being in line with allonormative beliefs. The present study investigated whether individuals offered help or were likely to befriend a new co-worker differently depending on the co-worker’s sexual orientation (i.e., heterosexual vs. asexual) and relationship status (i.e., in a relationship vs. single). Methods The online study included 1,028 participants (50.0% women and 50.0% men; Mage = 29.2, SD = 8.8) from German-speaking countries. Each participant read one of twelve short descriptions of a working situation in which they got to know a new co-worker. The description of the new colleague’s gender, sexual orientation, and relationship status varied. Participants had to indicate how likely they would share information with, befriend, gossip about, or have other-interests in the new colleague. A multivariate analysis of co-variance was calculated. Results Female participants had lower intentions to share knowledge with persons who were singles by circumstance than with persons in a relationship and male participants were more likely to befriend a single gay man by choice than a gay man in a relationship. Conclusions Bias against single persons or prejudice resulting from heteronormative beliefs might lead to subtle differences in behavior towards a co-worker. Such subtle “micro-practices” can be harmful because micro-practices accumulate over time and situations. Interventions at organizations need to help employees realize their own biases that might be based on prejudices against people with different sexual orientations and relationship statuses. Key messages • Overall women are more pressured by allonormative ideas and might therefore have lower intentions to share knowledge with persons who were singles by circumstance than with persons in a relationship. • The differing treatment of single gay men by choice and gay men in relationships may not be solely due to adherence to allonormativity, but rather could stem from heterosexist beliefs.

De-Escalation With Low-Intensity Antecedent Strategies: Implications for Disproportionate Disciplinary Outcomes

Research suggests that a cycle of misbehavior can escalate quickly from corrections over minor rule infractions to threats of serious disciplinary consequences. There is also evidence that Black youth may be especially at risk for these escalations due to subtle or implicit racial bias, or cultural mismatches that may lead teachers to believe that minor rule violations, especially those that require subjective judgments, convey willful defiance or disrespect. In this article, we provide a brief overview of data confirming that Black students experience disproportionate disciplinary outcomes, explore the idea that one contributor to disproportionality may be cultural disconnects between adults and the student population, and consider the idea that many punitive disciplinary actions evolve from relatively minor classroom conflict, disruption, or rule violations. We argue that fairly simple, low-intensity antecedent strategies that show promise in defusing problems rather than exacerbating them may offer mitigation against disproportionate disciplinary outcomes.

A Tale of Two Frontlines: Critically Assessing the Dynamics of Interracial Service Encounters

Minority frontline workers are often required to engage in interracial service encounters, which often involve additional complexities due to the subtle biases and stereotypes held by customers. Improving service delivery in interracial service encounters requires understanding the experiences of minority frontline workers. We begin by explaining the challenges faced by minority frontline workers and then offer an analytical framework that will allow operations management research to better account for these experiences. Next, we discuss opportunities for operations management researchers to explore how interracial service encounters impact employees, service operations, and service supply chains.

Methylphenidate reduces spatial attentional bias by modulating fronto-striatal connectivity.

Spatial attention bias reflects tendency to direct attention to specific side in space. This bias reflects asymmetric dopamine (DA) signaling in the striatum. Administration of DA agonists reduces spatial bias, yet the underlying mechanism is not yet clear. To address this, the current study tested whether methylphenidate (MPH; an indirect DA agonist) reduces orienting bias by modulating fronto-striatal connectivity. 54 adults with consistent bias completed the greyscales task which detects subtle biases during fMRI scanning under MPH (20mg) or placebo, in a double-blind design. As hypothesized, MPH reduced bias by increasing orienting towards non-preferred hemispace, regardless of whether the initial bias was left or right. MPH-induced increases were found in activation of the medial superior frontal gyrus (mSFG: F[1;53] = 4.632, cluster-defining threshold of P< 0.05, minimal cluster size = 0, p_FWE = 0.036, η2= 0.08) and its functional connectivity with the caudate (left caudate: F[1;53] = 12.664, p_FWE = 0.001, η2= 0.192; right caudate: F[1;53] = 11.069, p_FWE = 0.002, η2= 0.172), when orienting towards the non-preferred hemispace. MPH also reduced mSFG activation and fronto-striatal connectivity for the preferred hemispace. Results suggest modulation of frontal excitability due to increased caudate-mSFG functional connectivity. This mechanism may underlie the positive effect of dopaminergic agonists on abnormal patterns of directing attention in space.

Advancing the Knowledge of Spaniards’ Attitudes Towards Immigration

This study aims to assist Spanish authorities in enhancing the prevention of negative attitudes towards immigrants by addressing two key questions derived from prior research by the authors: (1) Which variables consistently influence attitudes towards migration over time? (2) What are the distinct attitudinal differences among various groups? Thus, the study delves into the evolution of attitudes towards migration in Spain from 2015 to 2017. It employs a dynamic perspective and advanced machine learning (ML) techniques recognized for their superior predictive capacity in social sciences. It identifies key variables influencing attitudes within diverse groups, including Multicultural and Non-Multicultural, and traces their changes over time. The research addresses the persistence of specific variables tied to subtle biases and distinctions among attitudinal segments. Despite an overall positive trend towards welcoming differences, the study unveils enduring ambivalence, elements of aversive racism, and subtle prejudice, highlighting the intricate factors contributing to the non-acceptance of immigration. Notably, self-perception emerges as a determinant, with optimism and professional attributes correlating with heightened tolerance. In conclusion, the study provides nuanced insights into the multifaceted nature of attitudes towards migration, offering valuable perspectives for more informed policy development.

Bias and ethics of AI systems applied in auditing - A systematic review

The integration of artificial intelligence into auditing shows great potential in enhancing automation and gaining insights from complex data. However, it also presents significant ethical challenges, including algorithmic biases, transparency, accountability, and fairness. This study aimed to investigate the sources of bias and risks posed by AI systems applied in auditing and the complex downstream interactions and effects they have. The study also explored the technical and ethical guardrails proposed and recommendations for translating principles into auditing practice. A systematic methodology was employed to acquire relevant studies across scientific databases. This involved a three-step process, including a targeted search query using Boolean operators and snowballing to yield 310 preliminary publications. A systematic review process was then conducted to identify 123 relevant articles focused on AI's implications for auditing, accounting, finance, or assurance contexts. Finally, screening and filtering on research quality distilled 83 high-quality publications from the year 2018 to 2023 spanning computer science, accounting, management science, and ethics disciplines. The analysis revealed five primary sources driving technical and human biases: data deficiencies, demographic homogeneity, spurious correlations, improper comparators, and cognitive biases. It also highlighted wider issues, such as trade-offs between efficiency and diligence, erosion of human skills and judgement, data dependence risks, and privacy violations from uncontrolled personal data exploitation. The study found promising remedies, including causal modeling to enable auditors to uncover subtle biases, representative algorithmic testing to evaluate fairness, periodic auditing of AI systems, human oversight alongside automation, and embedding ethical values like fairness and accountability into system design. The study concludes that auditors play a crucial role in assessing and ensuring AI's reliable and socially beneficial integration. It recommends governance, risk assessment before deployment, ongoing performance monitoring, and policies fostering trust and collaboration to responsibly translate principles into auditing practice.

Unveiling Gender Discrimination in the Workplace: A Qualitative Analysis of Vietnamese Women's Narratives

This study explores the dynamics of gender discrimination in the workplace through the qualitative narratives of 30 Vietnamese women across various professional sectors. Using a purposive sampling method, the research employs semi-structured interviews to gather in-depth insights into the participants' experiences, which were analyzed through thematic analysis. Key findings reveal prevalent workplace gender discrimination, characterized by subtle biases, traditional gender role enforcement, and challenges linked to maternity and career advancement. These issues manifest through both overt marginalization and covert devaluation of women's professional contributions, highlighting the resilience and coping strategies women employ to navigate such environments. While the study focuses on formal employment sectors and may not capture all employment types in Vietnam, it provides valuable perspectives for policymakers and organizational leaders aiming to mitigate gender disparities. Recommendations include enhanced diversity training, transparent career advancement policies, and improved work-life balance support. This research contributes original insights into the complexities of gender discrimination, emphasizing cultural specifics that influence workplace dynamics in Vietnam.

Beyond Binary: Revealing Variations in Islamophobic Content with Hierarchical Multi-Class Classification

In the digital age, the rise of Islamophobia-marked by an irrational fear or discrimination against Islam and Muslims-has emerged as a pressing issue, especially on social media platforms. In this paper we employs a multi-class classification system, moving beyond traditional binary models. We categorize Islamophobic content into three main classes and various subclasses, covering a range from subtle biases to explicit incitement. Comparative analysis of data from Reddit and Twitter illuminates the distinct prevalence and types of Islamophobic content specific to each platform. This paper deepens our understanding of digital Islamophobia and provides insights for crafting targeted online counter strategies. Additionally, it highlights the role of machineand deep learning in detecting and addressing Islamophobic content, emphasizing their significance in resolving complex social issues in the digital sphere.

Generative Bayesian modeling to nowcast the effective reproduction number from line list data with missing symptom onset dates.

The time-varying effective reproduction number Rt is a widely used indicator of transmission dynamics during infectious disease outbreaks. Timely estimates of Rt can be obtained from reported cases counted by their date of symptom onset, which is generally closer to the time of infection than the date of report. Case counts by date of symptom onset are typically obtained from line list data, however these data can have missing information and are subject to right truncation. Previous methods have addressed these problems independently by first imputing missing onset dates, then adjusting truncated case counts, and finally estimating the effective reproduction number. This stepwise approach makes it difficult to propagate uncertainty and can introduce subtle biases during real-time estimation due to the continued impact of assumptions made in previous steps. In this work, we integrate imputation, truncation adjustment, and Rt estimation into a single generative Bayesian model, allowing direct joint inference of case counts and Rt from line list data with missing symptom onset dates. We then use this framework to compare the performance of nowcasting approaches with different stepwise and generative components on synthetic line list data for multiple outbreak scenarios and across different epidemic phases. We find that under reporting delays realistic for hospitalization data (50% of reports delayed by more than a week), intermediate smoothing, as is common practice in stepwise approaches, can bias nowcasts of case counts and Rt, which is avoided in a joint generative approach due to shared regularization of all model components. On incomplete line list data, a fully generative approach enables the quantification of uncertainty due to missing onset dates without the need for an initial multiple imputation step. In a real-world comparison using hospitalization line list data from the COVID-19 pandemic in Switzerland, we observe the same qualitative differences between approaches. The generative modeling components developed in this work have been integrated and further extended in the R package epinowcast, providing a flexible and interpretable tool for real-time surveillance.

Prior probability cues bias sensory encoding with increasing task exposure

When observers have prior knowledge about the likely outcome of their perceptual decisions, they exhibit robust behavioural biases in reaction time and choice accuracy. Computational modelling typically attributes these effects to strategic adjustments in the criterion amount of evidence required to commit to a choice alternative - usually implemented by a starting point shift - but recent work suggests that expectations may also fundamentally bias the encoding of the sensory evidence itself. Here, we recorded neural activity with EEG while participants performed a contrast discrimination task with valid, invalid, or neutral probabilistic cues across multiple testing sessions. We measured sensory evidence encoding via contrast-dependent steady-state visual-evoked potentials (SSVEP), while a read-out of criterion adjustments was provided by effector-selective mu-beta band activity over motor cortex. In keeping with prior modelling and neural recording studies, cues evoked substantial biases in motor preparation consistent with criterion adjustments, but we additionally found that the cues produced a significant modulation of the SSVEP during evidence presentation. While motor preparation adjustments were observed in the earliest trials, the sensory-level effects only emerged with extended task exposure. Our results suggest that, in addition to strategic adjustments to the decision process, probabilistic information can also induce subtle biases in the encoding of the evidence itself.

Prior probability cues bias sensory encoding with increasing task exposure.

When observers have prior knowledge about the likely outcome of their perceptual decisions, they exhibit robust behavioural biases in reaction time and choice accuracy. Computational modelling typically attributes these effects to strategic adjustments in the criterion amount of evidence required to commit to a choice alternative - usually implemented by a starting point shift - but recent work suggests that expectations may also fundamentally bias the encoding of the sensory evidence itself. Here, we recorded neural activity with EEG while participants performed a contrast discrimination task with valid, invalid, or neutral probabilistic cues across multiple testing sessions. We measured sensory evidence encoding via contrast-dependent steady-state visual-evoked potentials (SSVEP), while a read-out of criterion adjustments was provided by effector-selective mu-beta band activity over motor cortex. In keeping with prior modelling and neural recording studies, cues evoked substantial biases in motor preparation consistent with criterion adjustments, but we additionally found that the cues produced a significant modulation of the SSVEP during evidence presentation. While motor preparation adjustments were observed in the earliest trials, the sensory-level effects only emerged with extended task exposure. Our results suggest that, in addition to strategic adjustments to the decision process, probabilistic information can also induce subtle biases in the encoding of the evidence itself.

SocialStigmaQA: A Benchmark to Uncover Stigma Amplification in Generative Language Models

Current datasets for unwanted social bias auditing are limited to studying protected demographic features such as race and gender. In this work, we introduce a comprehensive benchmark that is meant to capture the amplification of social bias, via stigmas, in generative language models. Taking inspiration from social science research, we start with a documented list of 93 US-centric stigmas and curate a question-answering (QA) dataset which involves simple social situations. Our benchmark, SocialStigmaQA, contains roughly 10K prompts, with a variety of prompt styles, carefully constructed to systematically test for both social bias and model robustness. We present results for SocialStigmaQA with two open source generative language models and we find that the proportion of socially biased output ranges from 45% to 59% across a variety of decoding strategies and prompting styles. We demonstrate that the deliberate design of the templates in our benchmark (e.g., adding biasing text to the prompt or using different verbs that change the answer that indicates bias) impacts the model tendencies to generate socially biased output. Additionally, through manual evaluation, we discover problematic patterns in the generated chain-of-thought output that range from subtle bias to lack of reasoning. Warning: This paper contains examples of text which are toxic, biased, and potentially harmful.

Multicenter integrated analysis of noncoding CRISPRi screens

The ENCODE Consortium’s efforts to annotate noncoding cis-regulatory elements (CREs) have advanced our understanding of gene regulatory landscapes. Pooled, noncoding CRISPR screens offer a systematic approach to investigate cis-regulatory mechanisms. The ENCODE4 Functional Characterization Centers conducted 108 screens in human cell lines, comprising >540,000 perturbations across 24.85 megabases of the genome. Using 332 functionally confirmed CRE–gene links in K562 cells, we established guidelines for screening endogenous noncoding elements with CRISPR interference (CRISPRi), including accurate detection of CREs that exhibit variable, often low, transcriptional effects. Benchmarking five screen analysis tools, we find that CASA produces the most conservative CRE calls and is robust to artifacts of low-specificity single guide RNAs. We uncover a subtle DNA strand bias for CRISPRi in transcribed regions with implications for screen design and analysis. Together, we provide an accessible data resource, predesigned single guide RNAs for targeting 3,275,697 ENCODE SCREEN candidate CREs with CRISPRi and screening guidelines to accelerate functional characterization of the noncoding genome.

Gendered subtle bias in Danish TV election debates

Abstract Trust in society is related to a perception of fairness and lack of bias. But bias has many faces. This article presents a conversation analytic study of the initial introduction of the debaters in so-called ‘presidential’ TV debates during the final stages of the general election campaigns in Denmark. The data represents a rare possibility to compare almost identical debate contexts: two different elections, but same TV channel, host, presidential debate setup and campaign contexts. The analyses show how male party leaders were given a chance to construct themselves as experienced, engaged, and hardworking politicians, while a female party leader was merely positioned with regard to her gender and age and as an underdog meeting a strong opponent. This allows us to explore how bias is not just about what is actually said and done but also about what could (based on the comparison) have been said and done.

What are we really decoding? Unveiling biases in EEG-based decoding of the spatial focus of auditory attention

Objective. Spatial auditory attention decoding (Sp-AAD) refers to the task of identifying the direction of the speaker to which a person is attending in a multi-talker setting, based on the listener’s neural recordings, e.g. electroencephalography (EEG). The goal of this study is to thoroughly investigate potential biases when training such Sp-AAD decoders on EEG data, particularly eye-gaze biases and latent trial-dependent confounds, which may result in Sp-AAD models that decode eye-gaze or trial-specific fingerprints rather than spatial auditory attention. Approach. We designed a two-speaker audiovisual Sp-AAD protocol in which the spatial auditory and visual attention were enforced to be either congruent or incongruent, and we recorded EEG data from sixteen participants undergoing several trials recorded at distinct timepoints. We trained a simple linear model for Sp-AAD based on common spatial patterns filters in combination with either linear discriminant analysis (LDA) or k-means clustering, and evaluated them both across- and within-trial. Main results. We found that even a simple linear Sp-AAD model is susceptible to overfitting to confounding signal patterns such as eye-gaze and trial fingerprints (e.g. due to feature shifts across trials), resulting in artificially high decoding accuracies. Furthermore, we found that changes in the EEG signal statistics across trials deteriorate the trial generalization of the classifier, even when the latter is retrained on the test trial with an unsupervised algorithm. Significance. Collectively, our findings confirm that there exist subtle biases and confounds that can strongly interfere with the decoding of spatial auditory attention from EEG. It is expected that more complicated non-linear models based on deep neural networks, which are often used for Sp-AAD, are even more vulnerable to such biases. Future work should perform experiments and model evaluations that avoid and/or control for such biases in Sp-AAD tasks.

Believing is seeing - the deceptive influence of bias in quantitative microscopy.

The visual allure of microscopy makes it an intuitively powerful research tool. Intuition, however, can easily obscure or distort the reality of the information contained in an image. Common cognitive biases, combined with institutional pressures that reward positive research results, can quickly skew a microscopy project towards upholding, rather than rigorously challenging, a hypothesis. The impact of these biases on a variety of research topics is well known. What might be less appreciated are the many forms in which bias can permeate a microscopy experiment. Even well-intentioned researchers are susceptible to bias, which must therefore be actively recognized to be mitigated. Importantly, although image quantification has increasingly become an expectation, ostensibly to confront subtle biases, it is not a guarantee against bias and cannot alone shield an experiment from cognitive distortions. Here, we provide illustrative examples of the insidiously pervasive nature of bias in microscopy experiments - from initial experimental design to image acquisition, analysis and data interpretation. We then provide suggestions that can serve as guard rails against bias.

Are older workers a saviour for recruitment problems in the hospitality industry?

This opinion piece discusses the potential for older workers to help alieviate recruitment problems in the hospitality industry. These problems have been made worse by the Covid-19 Pandemic and, in the UK, by Brexit and changes to immigration rules. There are many advantages of employing older workers in the hospitality industry but there is a persistent paradigm that older workers are resistent to change, have higher levels of absenteeism and possess poor IT skills. As many of the issues associated with barriers to the employment of older workers are due to negative stereotying and subtle bias, the author advocates the application of Implicit Bias Theory to uncover and subsequetly help address discrimination against older workers.

Teaching strategies for cultivating preschoolers’ sharing behavior based on gender roles in Chinese culture

&lt;span lang="EN-US"&gt;Chinese boys and girls manifest distinct sharing behavior compared with children of other nationalities following different cross-national pathways. Undoubtedly, the Chinese traditional culture projects distinct expectations of female and male roles. Kindergarten teachers are responsible for delivering cultural norm knowledge to children and shaping their behaviors and beliefs. This study aims to provide an insight into how kindergarten teachers cultivate preschoolers’ sharing behavior considering the traditional gender-role culture in China. This study adopted the multiple case study approach and involved six participants working in a Chinese public provincial kindergarten in Hebei Province. The data collection techniques used comprise semi-structured interviews and document analysis. This study’s findings showed teachers develop both boys’ and girls’ sharing awareness through an other-oriented strategy. They also elicit sharing practices through benefits-oriented and habits-forming strategies. However, different treatments of boys and girls could be observed in some respects. These findings demonstrate that children’s sharing behaviors are shaped through the same strategy with a subtle gender bias. They further imply that the gender-role knowledge of Chinese kindergarten teachers impacts how they cultivate preschoolers’ sharing behavior.&lt;/span&gt;

Leveraging Artificial Intelligence to Uncover Hidden Biases in Survival Disparities of Patients with Multiple Myeloma

In 2000, the Institute of Medicine highlighted significant evidence supporting a positive association between higher surgical oncologic procedure volume and improved clinical outcomes. Over nearly two decades since that report, research on the volume-outcome relationship in hematologic malignancies remains somewhat unclear. Most of the research on the impact of procedural volume in Multiple Myeloma (MM) management has relied on the National Cancer Database (NCDB), which consistently indicated that MM patients treated at higher-volume facilities experienced lower mortality rates compared to those treated at lower-volume facilities. Still, the underlying reasons for this disparity have remained elusive. In our pursuit of understanding the underlying factors contributing to the survival disparities in MM, we delved into the data from the NCDB studies. Notably, we observed a significant separation in the Kaplan-Meier (KM) curve for overall survival (OS) between patients treated in high-volume and low-volume facilities during the first 2-3 years. However, intriguingly, the divergence in survival rates tended to plateau after this initial period, with similar event rates observed between the two cohorts in subsequent years. This observation led us to question whether hidden biases or confounding factors might influence the outcomes beyond the scope of traditional analyses. Artificial Intelligence (AI) and machine learning algorithms offer powerful tools for unraveling complex patterns and identifying subtle biases in large datasets. By leveraging these advanced techniques, we aim to dig deeper into the NCDB data to uncover potential hidden factors influencing the survival disparities among MM patients. Objective: To assess the early separation of KM curves, and potential violation of proportional hazard assumptions, we used image analysis software to extract survival numerical value from the KM graph of major three studies of NCDB. We hypothesized that majority of significance in the survival between high vs low volumes occurs in the first 3 years and after that the difference in death rate remains stable. Methods: We included three main analyses of NCDB with KM curve assessment of high vs. low volume facilities. The Computer Vision 2 module on Python programming was used to convert the KM graph image to a gray-scale version. Then the X and Y coordinates of both lines were extracted. The vertical distance between two Y values for each value on X-axis was calculated as the difference between event rates in two cohorts. Further, we created a cumulative hazard plot to visualize the cumulative hazard function for each group over time. This plot can provide insights into whether the proportional hazard assumption holds. Kernel Density Estimation (KDE) was utilized for estimating probability densities from the underlying distribution. Results: We successfully reproduced the KM curve using the coordinates' numerical value (Figure-1).Kernel Density Estimation (KDE) plot visualizes the density of survival difference values over time. The color intensity indicates the density of points, with darker regions representing higher densities. The plot shows the prominent increase in the difference between survival rates occurring relatively early (roughly 30-36 months). After this initial increase, the plot's color intensity becomes lighter, indicating that the survival rate difference tends to plateau, i.e., it remains relatively constant. A large portion of the difference between the two curves occurs early in the course and, after that, stays stable, illustrating early separation in the KM curve. Conclusion: Our findings illustrate that the main survival difference between high and low-volume facilities for MM treatment occurs early in the disease course, potentially violating the proportional hazard assumption. This divergence from expectation may be attributed to patient migration from lower to higher-volume facilities over time, influencing survival data. In light of this, we recommend using alternative approaches, such as cumulative hazard plots and time-varying coefficient Cox models, to assess the Proportional Hazard assumption for patient volume in future research.