Social Bias Research Articles

Islamic Viewpoints on Opportunistic Sex Selection of IVF Embryos upon doing Preimplantation Genetic Testing for Preventing Genetic Diseases.

In recent years, preimplantation genetic testing (PGT) of IVF embryos have gained much traction in clinical assisted reproduction for preventing various genetic defects, including Down syndrome. However, such genetic tests inevitably reveal the sex of IVF embryos by identifying the sex (X and Y) chromosomes. In many countries with less stringent IVF regulations, information on the sex of embryos that are tested to be genetically normal is readily shared with patients. This would thus present Muslim patients with unintended opportunities for sex selection based on personal or social biases without any pressing need or valid medical reason. Additionally, there are other patients who claim using PGT for preventing genetic defects as a pretext or "convenient excuse," with a secret intention to do sex selection when it is banned in their home country. Currently, non-medicalsex selection is a highly-controversial and hotly debated issue in Islam, because there is generally a strong preference for having sons over daughters due to widespread cultural norms of elderly parents depending on their sons for financial support, as well as the need for male heirs to continue the family lineage within the backdrop of local patriarchal cultures. There is a risk of gender imbalance and social disequilibrium occurring in Islamic societies due to prevalent sex selection. Hence, the question is whether opportunistic sex selection with PGT would contravene Islamic ethics and principles, which will thus be discussed here.

Unraveling market mysteries: a comprehensive review of financial anomalies and puzzles

This comprehensive literature review consolidates various market anomalies and puzzles, providing an aggregated perspective to understand these complex dynamics that challenge the traditional Efficient Market Hypothesis. We examined numerous academic works to reveal insights into long-term return irregularities, earnings management influence on equity offerings, and information uncertainty’s impact on stock returns. The review delves into unique phenomena like persistent mutual fund performance, the day-of-the-week returns, the January effect, weather-induced mood shifts on the market, and the dynamics of multiple anomalies. International asset pricing and weekend anomalies were also discussed, with a particular focus on cryptocurrency efficiency. Incorporating behavioral finance perspectives, we explored social transmission bias, emotional finance, biased beliefs, investor optimism, sentiment, and global market inefficiencies. The influence of unique events and seasonal factors, such as the Super Bowl, daylight saving time, and the Halloween effect, were also analyzed. The review concludes by highlighting the evolving landscape of market anomalies, discussing ma- chine learning approaches to anomaly research, investor behavior challenges, and the disappearing anomalies in country and industry returns. It sets the groundwork for holistic comprehension of market anomalies, suggesting future research directions such as exploring new data sources, comprehensive theoretical modeling, and the role of technology, market regulations, and environmental changes on market anomalies.

The structure of segregation in co-authorship networks and its impact on scientific production

Co-authorship networks, where nodes represent authors and edges represent co-authorship relations, are key to understanding the production and diffusion of knowledge in academia. Social constructs, biases (implicit and explicit), and constraints (e.g. spatial, temporal) affect who works with whom and cause co-authorship networks to organise into tight communities with different levels of segregation. We aim to examine aspects of the co-authorship network structure that lead to segregation and its impact on scientific production. We measure segregation using the Spectral Segregation Index (SSI) and find four ordered categories: completely segregated, highly segregated, moderately segregated and non-segregated communities. We direct our attention to the non-segregated and highly segregated communities, quantifying and comparing their structural topologies and k-core positions. When considering communities of both categories (controlling for size), our results show no differences in density and clustering but substantial variability in the core position. Larger non-segregated communities are more likely to occupy cores near the network nucleus, while the highly segregated ones tend to be closer to the network periphery. Finally, we analyse differences in citations gained by researchers within communities of different segregation categories. Researchers in highly segregated communities get more citations from their community members in middle cores and gain more citations per publication in middle/periphery cores. Those in non-segregated communities get more citations per publication in the nucleus. To our knowledge, this work is the first to characterise community segregation in co-authorship networks and investigate the relationship between community segregation and author citations. Our results help study highly segregated communities of scientific co-authors and can pave the way for intervention strategies to improve the growth and dissemination of scientific knowledge.

How to Make “AI” Intelligent; or, The Question of Epistemic Equality

Abstract Critics have identified a set of operational flaws in the machine language and deep learning systems now discussed under the “AI” banner. Five of the most discussed are social biases, particularly racism; opacity, such that users cannot assess how results were generated; coercion, in that architectures, datasets, algorithms, and the like are controlled by designers and platforms rather than users; systemic privacy violations; and the absence of academic freedom covering corporation-based research, such that results can be hyped in accordance with business objectives or suppressed and distorted if not. This article focuses on a sixth problem with AI, which is that the term intelligence misstates the actual status and effects of the technologies in question. To help fill the gap in rigorous uses of “intelligence” in public discussion, it analyzes Brian Cantwell Smith's The Promise of Artificial Intelligence (2019), noting humanities disciplines routinely operate with Smith's demanding notion of “genuine intelligence.” To get this notion into circulation among technologists, the article calls for replacement of the Two Cultures hierarchy codified by C. P. Snow in the 1950s with a system in which humanities scholars participate from the start in the construction and evaluation of “AI” research programs on a basis of epistemic equality between qualitative and quantitative disciplines.

Does Environmental, Social and Governance Performance Lessen Analyst Optimistic Bias: Evidence from China*

AbstractWe examine how analysts respond to non‐financial information about environmental, social responsibility, and corporate governance (ESG) performance and find good ESG performance lessens analysts' optimistic bias. We verify the mediating role of financial transparency that superior ESG performance decreases the level of information asymmetry between firms and analysts and improves financial transparency, thus weakening optimistic bias. Analysts' optimistic bias rises with increased institutional holdings and diminishes with heightened economic policy uncertainty. Social responsibility makes a larger reducing effect on analyst optimistic bias than corporate governance and environmental protection. Our findings have important implications for encouraging firms to emphasize ESG performance and improving stock market efficiency.

‘Horn Please’: The evolution and regulation of traffic in twentieth-century India

The article explores the changes in behaviours and norms that followed the arrival of modern transport: trams, motor cars, buses and motor lorries. The article examines how new forms of behaviours such as the regulation of speed of motor cars, use of traffic lights, or newer methods of using streets such as walking on footpaths impacted the interaction between the authorities and people living within cities. The article shows that ideas of modern behaviour, centred on maintaining discipline and order on the streets were endorsed by the government but were contested and reluctantly adapted by the people. It also highlights the existence of social biases in administrative decisions connected to street use. This is evident in the analysis of accidents when the Indian driver and his lack of physical strength is equated with his incapability to drive a tram. This essentialisation takes place at both ends. Such debates occurred on the lines of race and class. For instance, the industrial workforce which migrated from rural areas had to learn new skills to navigate different spaces in the city. Apart from the factory, their activities on the streets were also scrutinised, such as when their inability to understand traffic rules was attributed to their rural background. Thus, in certain cases, transport mobility is incompatible with social mobility.

Corporate social responsibility (CSR) and cognitive bias: A systematic review and research direction

Corporate Social Responsibility's (CSR) competitive advantage continues to disappear rapidly due to embedded gaps in practice. Thus, managers and scholars are beginning to shift their focus from the business case of CSR toward how firms address weaknesses within CSR practices. This study reveals the prevalence and influence of different cognitive bias types in CSR formulation, implementation, and communication. In this paper, CSR formulation emerged as the domain more predisposed to cognitive biases, followed by CSR implementation and CSR communication with 12, 9, and 5 cognitive bias types, respectively. Based on a systematic review, we provide a conceptual framework that discusses CSR cognitive bias' antecedents, indicator taxonomy, context, and consequences on partnerships. Following that, we identify the major implications for strategy theory and discuss possible future research areas to address.

Preference for native-accented peers in monolingual and bilingual children acquiring Czech

ABSTRACT The way a talker speaks influences how they are socially perceived. Social preferences based on language are present already in early childhood: children generally prefer to interact with those who speak the child's native language or native accent. Prior findings are inconclusive as to whether the accent-based social biases could be reduced by more varied language experience, including bilingual upbringing. This study reports a friendship-choice task administered to 49 5- to 6-year-old children acquiring Czech (26 monolinguals and 23 bilinguals). The results showed that both monolingual and bilingual children had a preference for native-accented peers. In bilingual children, however, this native-speaker preference was reliably smaller than in monolingual children. These findings demonstrate that foreign accent is a strong cue for friendship choice even in the linguistically homogeneous, Czech-speaking society, and that the effect of foreign accent is attenuated by a child's bilingual language experience.

Hypodescent or ingroup overexclusion?: Children's and adults' racial categorization of ambiguous black/white biracial faces.

Two processes describe racially ambiguous Black/White Biracial categorization-the one-drop rule, or hypodescent, whereby racially ambiguous people are categorized as members of their socially subordinated racial group (i.e., Black/White Biracial faces categorized as Black) and the ingroup overexclusion effect, whereby racially ambiguous people are categorized as members of a salient outgroup, regardless of the group's status. Without developmental research with racially diverse samples, it is unclear when these categorization patterns emerge. Study 1 included White, Black, and racially diverse Biracial children (aged 3- to 7-years) and their parents to test how racial group membership and social context influence face categorization biases. To provide the clearest test of hypodescent and ingroup overexclusion, White participants came from majority White neighborhoods and Black participants from majority Black neighborhoods (with Biracial participants from more racially diverse neighborhoods)-two samples with prominent racial ingroups. Study 2 aimed to replicate the parent findings with a separate sample of White, Black, Black/White Biracial, and Asian adults. Results suggest the ingroup overexclusion effect is present across populations early in development and persists into adulthood. Additionally, categorization was meaningfully related to parental context, pinpointing a pathway that potentially contributes to ingroup overexclusion. RESEARCH HIGHLIGHTS: White, Black, and racially diverse Biracial children and adults tended to categorize racially ambiguous Black/White Biracial faces as racial outgroup members, even if the outgroup was White. This contradicts most work arguing Black/White Biracial racially ambiguous people are more often seen as Black. Children and parents' categorizations were related, though children's categorizations were not related to socialization above and beyond parents' categorizations. Children showed similar categorization patterns across dichotomous and continuous measures.

Partisan Media Sentiment Toward Artificial Intelligence

Artificial intelligence (AI) is becoming pervasive across society. However, its deployment appears to be a divisive issue. This research examines aversion to AI across the partisan divide. We analyze partisan media sentiment toward AI, a powerful driver of public opinion toward social issues. We conduct a text analysis of media articles on AI (N = 7,840) from several liberal-leaning and conservative-leaning media outlets. The results demonstrate that liberal-leaning media show a greater aversion to AI than conservative-leaning media. Furthermore, a mediation analysis suggests that liberal-leaning media are more concerned with AI magnifying social biases in society than conservative-leaning media, which drives the partisan media differences. Moreover, the results also show that media sentiment toward AI became more negative after George Floyd’s death, an event that heightened sensitivity about social biases in society. Implications for how these partisan media differences can polarize public opinion and policymaker support toward AI are discussed.

Preference for Normative Information Over Social Information: A Vignette Experiment Testing Content Bias at Three Phases of Transmission

This study aimed to investigate norm bias, a novel type of content bias, in cultural transmission. Using online vignettes with 106 participants, we investigated whether participants preferred normative information over social information. Following the method of Stubbersfield et al. (2015), we examined norm bias in three transmission phases: choose-to-receive, encode-and-retrieve, and choose-to-transmit. The results showed that normative bias was preferred over social bias in the choose-to-retrieve and choose-to-transit phases, but not in the encode-and-retrieve phase, suggesting that normative information may be more likely to be transmitted over social information.

Los pronombres interrogativos complejos del españolel quéylo qué

AbstractThe neuter interrogative pronounsel quéandlo quéalternate withqué‘what’ in conversational Spanish in certain contexts and types of wh-questions (e.g., –No me lo puedo creer – ¿No te puedes creer qué/el qué/lo qué?). This paper describes geographical, register and social variation in the use ofel quéandlo quéand analyses the interpretive and distributional properties of these complex forms. Building on corpus data and speakers’ judgements, it is shown that the formel quéis common to all Spanish dialects and is used in all registers and at all social levels, whereaslo quéis only found in Spain, Argentina and Uruguay: in Spain and Argentina, this form belongs to lower class people with little education and speakers in rural areas; in Uruguay, it does not have a social bias and occurs in both formal and informal situations. It is also argued thatel quéandlo quéare used to indicate that the target of the question is identifiable in the context of utterance, and it is thus claimed thatelandloinel quéandlo quéare overt definiteness markers.

Blinding to Circumvent Human Biases: Deliberate Ignorance in Humans, Institutions, and Machines

Inequalities and injustices are thorny issues in liberal societies, manifesting in forms such as the gender–pay gap; sentencing discrepancies among Black, Hispanic, and White defendants; and unequal medical-resource distribution across ethnicities. One cause of these inequalities is implicit social bias—unconsciously formed associations between social groups and attributions such as “nurturing,” “lazy,” or “uneducated.” One strategy to counteract implicit and explicit human biases is delegating crucial decisions, such as how to allocate benefits, resources, or opportunities, to algorithms. Algorithms, however, are not necessarily impartial and objective. Although they can detect and mitigate human biases, they can also perpetuate and even amplify existing inequalities and injustices. We explore how a philosophical thought experiment, Rawls’s “veil of ignorance,” and a psychological phenomenon, deliberate ignorance, can help shield individuals, institutions, and algorithms from biases. We discuss the benefits and drawbacks of methods for shielding human and artificial decision makers from potentially biasing information. We then broaden our discussion beyond the issues of bias and fairness and turn to a research agenda aimed at improving human judgment accuracy with the assistance of algorithms that conceal information that has the potential to undermine performance. Finally, we propose interdisciplinary research questions.

Brain mediators of biased social learning of self-perception in social anxiety disorder

Social anxiety disorder (SAD) is characterized by an excessive fear of social evaluation and a persistently negative view of the self. Here we test the hypothesis that negative biases in brain responses and in social learning of self-related information contribute to the negative self-image and low self-esteem characteristic of SAD. Adult participants diagnosed with social anxiety (N = 21) and matched controls (N = 23) rated their performance and received social feedback following a stressful public speaking task. We investigated how positive versus negative social feedback altered self-evaluation and state self-esteem and used functional Magnetic Resonance Imaging (fMRI) to characterize brain responses to positive versus negative feedback. Compared to controls, participants with SAD updated their self-evaluation and state self-esteem significantly more based on negative compared to positive social feedback. Responses in the frontoparietal network correlated with and mirrored these behavioral effects, with greater responses to positive than negative feedback in non-anxious controls but not in participants with SAD. Responses to social feedback in the anterior insula and other areas mediated the effects of negative versus positive feedback on changes in self-evaluation. In non-anxious participants, frontoparietal brain areas may contribute to a positive social learning bias. In SAD, frontoparietal areas are less recruited overall and less attuned to positive feedback, possibly reflecting differences in attention allocation and cognitive regulation. More negatively biased brain responses and social learning could contribute to maintaining a negative self-image in SAD and other internalizing disorders, thereby offering important new targets for interventions.

Bipol: A novel multi-axes bias evaluation metric with explainability for NLP

We introduce bipol, a new metric with explainability, for estimating social bias in text data. Harmful bias is prevalent in many online sources of data that are used for training machine learning (ML) models. In a step to address this challenge we create a novel metric that involves a two-step process: corpus-level evaluation based on model classification and sentence-level evaluation based on (sensitive) term frequency (TF). After creating new models to classify bias using SotA architectures, we evaluate two popular NLP datasets (COPA and SQuADv2) and the WinoBias dataset. As additional contribution, we created a large English dataset (with almost 2 million labeled samples) for training models in bias classification and make it publicly available. We also make public our codes.

Expanding Nature's storytelling: extended reality and debiasing strategies for an eco-agency.

Communication in sustainability and environmental sciences is primed to be substantially changed with extended reality technology, as the emergent Metaverse gives momentum to building an urgent pro-environmental mindset. Our work focuses on immersive econarratives, supported by virtual and augmented realities, and their potential to favor an improved relationship with the environment. Considering social aggregation dynamics and cognitive bias, this article intends to (1) make the case for a new environmental narrative; (2) position extended reality as privileged settings to sustain this narrative; and (3) suggest that this storytelling should be informed by Nature's empirical evidence, i.e., ecosystem data. We see this as a chance to think this Metaverse with an embedded environmental consciousness, informed by behavior-change research.

Exploring the Interplay of Social Capital, Cognitive Biases, and Environmental Factors on Entrepreneurial Behavior in Albania

Purpose: This research explores how social capital, cognitive biases, and environmental factors influence entrepreneurial behavior in Albania.
 
 Theoretical Framework: Grounded in the Albanian entrepreneurial context, the study examines the combined impact of social capital, cognitive biases, and environmental dynamics on entrepreneurial behavior.
 
 Design/Methodology/Approach: Utilizing surveys and interviews, this mixed-methods study employs statistical techniques including correlation, regression, and ANOVA to uncover intricate relationships.
 
 Findings: The study reveals a 25% engagement rate in entrepreneurial activities. Social capital, notably professional networks (60%), significantly supports entrepreneurship. Cognitive biases, particularly overconfidence, influence decision-making and entrepreneurial actions. Business environment perceptions are varied, revealing bureaucratic hurdles and limited funding as primary challenges. Positive influences include motivational factors like incentives and training. Cultural, social, and economic elements locally shape entrepreneurial behavior.
 
 Research, Practical & Social Implications: The research underscores the need to nurture social capital, address cognitive biases, and refine the business ecosystem for a stronger Albanian entrepreneurial landscape. Valuable for policymakers and entrepreneurs, these insights contribute to economic growth and innovation.
 
 Originality/Value: This study provides unique insights into Albania's entrepreneurial behavior nexus, contributing to both academic discourse and practical applications within the Albanian context.

Female Leadership in Muslim Societies

For centuries, Muslim women have been considered legally unqualified for judicial, political, and other highest leadership positions in the public domain. The contemporary debates range from constructing and reconstructing theological contestation to identifying and analysing various socio-cultural realities around Muslim women’s lives which made it more challenging for them to claim leadership. Today, in one hand, a growing number of literatures are challenging conservative religious interpretations that disfavour female leadership and calling to reread religious source texts. On the other hand, many literatures are raising voices on social gender perceptions and cultural biases which create barriers for women leadership. The recent inauguration of hijab-clad Muslim female presidents in Tanzania and Singapore also manifests a positive paradigm shift in collective Muslim consciousness to female leadership. To understand this change, this paper looks into both theological and socio-cultural aspects to outline the different positions and new developments in contemporary literature in identifying the burdens on Muslim women’s shoulders that curtail their aspirations for leadership participation. This study argues that contemporary literature reveals a complex and formidable development in the debate that demands more careful attention to accommodate the changing environment and attitudes in Muslim societies regarding the permissibility and plausibility of female leadership.

The cost of social influence: Own-gender and gender-stereotype social learning biases in adolescents and adults.

Pervasive gender gaps in academic subject and career choices are likely to be underpinned by social influences, including gender stereotypes of competence in academic and career domains (e.g., men excel at engineering, women excel at care), and model-based social learning biases (i.e., selective copying of particular individuals). Here, we explore the influence of gender stereotypes on social learning decisions in adolescent and adult males and females. Participants (Exp 1: N = 69 adolescents; Exp 2: N = 265 adults) were presented with 16 difficult multiple-choice questions from stereotypically feminine (e.g., care) and masculine (e.g., engineering) domains. The answer choices included the correct response and three incorrect responses paired with a male model, a female model, or no model. Participants' gender stereotype knowledge and endorsement were measured, and adolescents (Exp. 1) listed their academic subject choices. As predicted, there was a bias towards copying answers paired with a model (Exp.1: 74%, Exp. 2: 65% ps < .001). This resulted in less success than would be expected by chance (Exp. 1: 12%, Exp. 2: 16% ps < .001), demonstrating a negative consequence of social information. Adults (Exp 2) showed gender stereotyped social learning biases; they were more likely to copy a male model in masculine questions and a female model in feminine questions (p = .012). However, adolescents (Exp 1) showed no evidence of this stereotype bias; rather, there was a tendency for male adolescents to copy male models regardless of domain (p = .004). This own-gender bias was not apparent in female adolescents. In Exp 1, endorsement of masculine stereotypes was positively associated with selecting more own-gender typical academic subjects at school and copying significantly more male models in the male questions. The current study provides evidence for the first time that decision-making in both adolescence and adulthood is impacted by gender biases.

Changes in Learning From Social Feedback After Web-Based Interpretation Bias Modification: Secondary Analysis of a Digital Mental Health Intervention Among Individuals With High Social Anxiety Symptoms.

Biases in social reinforcement learning, or the process of learning to predict and optimize behavior based on rewards and punishments in the social environment, may underlie and maintain some negative cognitive biases that are characteristic of social anxiety. However, little is known about how cognitive and behavioral interventions may change social reinforcement learning in individuals who are anxious. This study assessed whether a scalable, web-based cognitive bias modification for interpretations (CBM-I) intervention changed social reinforcement learning biases in participants with high social anxiety symptoms. This study focused on 2 types of social reinforcement learning relevant to social anxiety: learning about other people and learning about one's own social performance. Participants (N=106) completed 2 laboratory sessions, separated by 5 weeks of ecological momentary assessment tracking emotion regulation strategy use and affect. Approximately half (n=51, 48.1%) of the participants completed up to 6 brief daily sessions of CBM-I in week 3. Participants completed a task that assessed social reinforcement learning about other people in both laboratory sessions and a task that assessed social reinforcement learning about one's own social performance in the second session. Behavioral data from these tasks were computationally modeled using Q-learning and analyzed using mixed effects models. After the CBM-I intervention, participants updated their beliefs about others more slowly (P=.04; Cohen d=-0.29) but used what they learned to make more accurate decisions (P=.005; Cohen d=0.20), choosing rewarding faces more frequently. These effects were not observed among participants who did not complete the CBM-I intervention. Participants who completed the CBM-I intervention also showed less-biased updating about their social performance than participants who did not complete the CBM-I intervention, learning similarly from positive and negative feedback and from feedback on items related to poor versus good social performance. Regardless of the intervention condition, participants at session 2 versus session 1 updated their expectancies about others more from rewarding (P=.003; Cohen d=0.43) and less from punishing outcomes (P=.001; Cohen d=-0.47), and they became more accurate at learning to avoid punishing faces (P=.001; Cohen d=0.20). Taken together, our results provide initial evidence that there may be some beneficial effects of both the CBM-I intervention and self-tracking of emotion regulation on social reinforcement learning in individuals who are socially anxious, although replication will be important.