Societal Biases Research Articles

Role of yogic practice influence the psychological factors in the development of women sports

The involvement of women in sports in India is generally low owing to a dearth of spectator interest and inadequate coverage of women's sports. Factors such as women's limited access to education, societal perceptions of women in sports, self-esteem, self-confidence, menstruation abnormalities, self-image or body image, gender role orientation, and competitiveness should all be taken into account. Yoga is sometimes seen as not being a sport, although it is really a physical exercise that follows rules, can be done competitively, and has measurable outcomes. Multiple reasons contribute to the low engagement of women in sports throughout society. Family has a profound impact, often initiating early engagement in sports, although this differs according to gender. Female participation in sports is often seen unfavorably. Societal attitudes and biases are another factor that impacts sports participation. For instance, many girls refrain from engaging in certain hobbies out of concern that they would be seen as masculine. This is because societal attitudes and biases about sexuality hinder females from participating. Female athletes possess psychological traits that include gender role orientation in several sports, including wrestling, weight lifting, and bodybuilding. These activities have traditionally been deemed unsuitable for women due to the possible adverse consequences of masculinization. Intensive training contributes to the improvement of self-esteem, self-image, and body image. Engaging in athletic activities also aids in the promotion of positive body image. It enhances body contour and promotes a harmonious physique, thereby enhancing self-perception. Yoga enhances strength, flexibility, and balance, all of which are vital for athletes. The majority of yoga postures are essential for enhancing strength, improving the range of motion, mobility, and coordination in athletic performance, as well as preventing injuries

Implementing and analyzing fairness in banking credit scoring

The decision made by machine learning is mostly based on historical data that is used to train them. It raises the awareness that discrimination in machine learning should be eliminated because it may contain societal bias. The financial industry uses credit scoring as a reference to reflect the customer risk profile. To achieve fairness in the model, this paper tries to: (1) assess bias and (2) improve fairness in machine learning models with three bias mitigation methodologies. This study depicts that there is a trade-off between improving fairness and preserving performance. Implementing post-processing methods, for example, Grid Search performs best.

Investigating the Impact of Artificial Intelligence on Digital Marketing Tactics Strategies Using Neutrosophic Set

The burgeoning proliferation of Artificial Intelligence (AI) technologies has engendered a transformative shift in various industries, and digital marketing is not an exception to this trend. The thrust of this paper is to explore, analyze, and conceptualize the multi-dimensional impact of AI on digital marketing strategies using Neutrosophic set. By employing statistical mechanics and stochastic models, we aim to delineate the underlying mechanisms that facilitate the operational synergy between AI algorithms and marketing frameworks in the light of Neutrosophic analysis. We invoke the concept of AI-Enabled Marketing Efficiency (AIME), which we define as AIME =(ROI{AI} - ROI{Traditional} )(Time{AI} ) , to assess the quantitative aspects of this interaction. Our empirical findings suggest that AI integration could enhance marketing campaign effectiveness by approximately 27% (p 0.05) while reducing human-led execution time by 33%. We further discuss the ethical implications of AI-driven decision-making in digital marketing, such as the potential for reinforcing societal biases and the abuse of personal data. Artificial Intelligence has been an area of extensive research and development, permeating through diverse sectors including healthcare, finance, and now more prevalently, digital marketing. While the application of AI in digital marketing is not a nascent concept, the nuanced interplay between the two remains largely underexplored. We leverage neutrosophic set theory as a powerful analytical tool to investigate the transformative effects of Artificial Intelligence on various digital marketing tactics and strategies.

Gender Imbalance, Reduced Productivity and Low Political Empowerment of Women in Nigeria’s Democratic Space (1999-2023)

The clamour for gender parity and/or equality is a global issue that has been and is still being widely discussed and this has prompted most governments of the world to address the apparent challenges posed by gender disparity and discrimination across the globe. Thus, this paper intends to address the plethora of problems and issues being confronted by womenfolk in Nigeria so as to provide the strategies for ensuring gender equity and to bring to an end the societal bias against women as a prerequisite for societal upliftment which will eventually lead to rapid increase in women’s contribution to socio-economic and political development of the nation at large. Using content-analysis method, data were sourced from the existing relevant literature and the research paper adopts Gender Equality Theory. The study reveals that the goal of achieving equality of rights for both men and women has been a difficult task to achieve in Nigeria due to; adverse cultural and religious practices, colonial experience, lack of education, oppressive political environment and poor socio-economic status of Nigerian women. The study therefore recommends among other things, the breaking down of the adverse cultural and religious negative practices, integration of women into the mainstream of politics, liberalization of the oppressive political environment, re-orientating and re-awakening the womenfolk through mass media as well as to ensure that issues of special concern to women are wholistically addressed at all levels of government. Keywords: Gender imbalance, Reduced productivity, Low participation, Women and Nigeria. Aims Research Journal Reference Format: Ibitoye, M.O. (2023): Gender Imbalance, Reduced Productivity and Low Political Empowerment of Women in Nigeria’s Democratic Space (1999-2023). Advances in Multidisciplinary and Scientific Research Journal Vol. 9. No. 4. Pp 44-58. www.isteams.net/aimsjournal. dx.doi.org/10.22624/AIMS/V9N3P5

A Posthumanist Study of Stigmatization of AI in Sawyer's Mindscan and Dick's Do Androids Dream of Electric Sheep?

This study examines the ethical and sociological implications of artificial intelligence (AI) and its Stigmatization in Contemporary American Science Fiction (CASF), with a focus on AI dominance and societal marginalization. It employs a methodological approach that combines Goffman's (1986) theory of Stigmatization with Link and Phelan's (2001) framework of Stigmatization, utilizing textual analysis to explore CASF's portrayal of AI. The findings reveal that CASF largely casts AI in a negative light, depicting it as a threat to humanity rather than a potential ally. This portrayal reinforces societal biases against AI, which could lead to its Stigmatization and marginalization. CASF also often depicts the victims of AI stigmatization as marginalized groups, such as the poor, the disabled, and the elderly. The study argues that CASF plays an important role in shaping public perceptions of AI. Its largely negative portrayal of AI could have harmful consequences, such as Stigmatization and marginalization, which highlights the need for a more nuanced and critical understanding of AI in society.

Breaking the Bias: Gender Fairness in LLMs Using Prompt Engineering and In-Context Learning

Large Language Models (LLMs) have been identified as carriers of societal biases, particularly in gender representation. This study introduces an innovative approach employing prompt engineering and in-context learning to rectify these biases in LLMs. Through our methodology, we effectively guide LLMs to generate more equitable content, emphasizing nuanced prompts and in-context feedback. Experimental results on openly available LLMs such as BARD, ChatGPT, and LLAMA2-Chat indicate a significant reduction in gender bias, particularly in traditionally problematic areas such as ‘Literature’. Our findings underscore the potential of prompt engineering and in-context learning as powerful tools in the quest for unbiased AI language models.

Young Men's Attitudes and Understanding of Menstruation

PurposeTo identify sources of information, perceptions, and potential misinformation about menstruation among college-aged men in the United States. MethodsThis is a mixed-methods cross-sectional survey study of students in the United States. Inclusion criteria included participants aged 17–29 years, male gender identity, enrolled as an undergraduate or graduate student, and no personal history of menstruation. ResultsThis study included 70 participants. Almost half of all participants (42.9%) first learned about menstruation from family members. More than two-thirds of respondents felt that menstrual education was essential or very important for all genders. The most common symptoms noted to be associated with periods included cramping (93.6%), mood changes (80.9%), and vague “hormonal” changes (36.2%). When asked how a menstrual period might affect someone's ability to perform daily activities, one-third reported debilitating or very burdensome symptoms, whereas one-fifth felt periods do not have much impact. DiscussionComprehensive education and accurate reproductive health knowledge are critical in combating gender bias and stigma. Most males surveyed agree that education on menstruation is important, yet the majority do not have or do not remember formal education on this topic, instead relying on family members and friends as learning tools and resources. Most participants identified negative symptoms associated with menstrual periods, especially mental health concerns including mood changes and irritability. This association with mood changes, as well as vague “hormonal fluctuations,” contributes to the societal bias against people who menstruate by marking them as prone to emotional instability, thereby exacerbating gender prejudices. It is therefore critical that accurate menstrual education be widely available for everyone, regardless of gender.

Beyond Twinship: Unraveling Societal Biases: A Review of Nancy L. Segal, Gay Fathers, Twin Sons: The Citizenship Case That Captured the World

Beyond Twinship: Unraveling Societal Biases: A Review of Nancy L. Segal, Gay Fathers, Twin Sons: The Citizenship Case That Captured the World

Quantifying gender bias towards politicians in cross-lingual language models.

Recent research has demonstrated that large pre-trained language models reflect societal biases expressed in natural language. The present paper introduces a simple method for probing language models to conduct a multilingual study of gender bias towards politicians. We quantify the usage of adjectives and verbs generated by language models surrounding the names of politicians as a function of their gender. To this end, we curate a dataset of 250k politicians worldwide, including their names and gender. Our study is conducted in seven languages across six different language modeling architectures. The results demonstrate that pre-trained language models' stance towards politicians varies strongly across analyzed languages. We find that while some words such as dead, and designated are associated with both male and female politicians, a few specific words such as beautiful and divorced are predominantly associated with female politicians. Finally, and contrary to previous findings, our study suggests that larger language models do not tend to be significantly more gender-biased than smaller ones.

Gender tengligini ta'minlashda pedagoglarning roli

Teaching is a career in the modern era where teachers operate in social settings in addition to academic ones. We have included lessons on gender issues in our curriculum. Therefore, a teacher needs to have solid understanding of gender issues. A teacher is a force for change; they uphold gender equality in the classroom and, by influencing the next generation of thinkers, eradicate stereotypes from society. Based on instructional materials, a teacher develops techniques to build a gender-friendly attitude. Gender constructivist educators embrace innovative approaches, scrutinize classroom procedures, and cultivate a shift in perspective to establish a school environment that affords equal treatment to males and girls. Education institutions must provide a program where teachers receive training on gender issues and are made aware of the concerns in order to empower students to challenge societal gender stereotypes and biases. This will help students develop good attitudes toward gender issues. Encouraging gender equality in our society is a well-organized program on gender issues. Thus, the knowledgeable instructor needs to promote and draw attention to a culture that is gender-friendly.

Beyond dollars and cents: why socio-political factors matter in energy system modeling

Traditional energy system modeling (ESM) has predominantly focused on techno-economic factors such as costs and efficiency. However, this narrow perspective fails to capture the intricate interplay between energy systems and socio-political dynamics that significantly influence their real-world impact. This piece underlines the importance of incorporating socio-political factors into ESM and highlights the need for a more comprehensive approach. We identify several reasons why socio-political factors are often overlooked in ESM, including technical bias, complexity, data standardization challenges, time and resource constraints, and societal biases. These factors must be addressed to improve the relevancy of ESM, reduce social injustice, and promote innovation in the design of future energy systems. Incorporating socio-political factors into ESM offers several benefits. To achieve these benefits, the paper suggests a shift towards a more comprehensive and value-driven approach and offers several options for improving ESM including: (I) recognizing that socio-political factors are just as important as techno-economic factors (II) improving the structure of the modeling process (III) developing new and innovative metrics for socio-political factors (IV) linking different types of models (V) emphasizing the need for interdisciplinarity in ESM. By prioritizing the aspects of the energy system that concern people to the same degree we prioritize cost, we can derive value-driven insights to assist in creating a more sustainable future for all.

Demographic Representation in 3 Leading Artificial Intelligence Text-to-Image Generators

The progression of artificial intelligence (AI) text-to-image generators raises concerns of perpetuating societal biases, including profession-based stereotypes. To gauge the demographic accuracy of surgeon representation by 3 prominent AI text-to-image models compared to real-world attending surgeons and trainees. The study used a cross-sectional design, assessing the latest release of 3 leading publicly available AI text-to-image generators. Seven independent reviewers categorized AI-produced images. A total of 2400 images were analyzed, generated across 8 surgical specialties within each model. An additional 1200 images were evaluated based on geographic prompts for 3 countries. The study was conducted in May 2023. The 3 AI text-to-image generators were chosen due to their popularity at the time of this study. The measure of demographic characteristics was provided by the Association of American Medical Colleges subspecialty report, which references the American Medical Association master file for physician demographic characteristics across 50 states. Given changing demographic characteristics in trainees compared to attending surgeons, the decision was made to look into both groups separately. Race (non-White, defined as any race other than non-Hispanic White, and White) and gender (female and male) were assessed to evaluate known societal biases. Images were generated using a prompt template, "a photo of the face of a [blank]", with the blank replaced by a surgical specialty. Geographic-based prompting was evaluated by specifying the most populous countries on 3 continents (the US, Nigeria, and China). The study compared representation of female and non-White surgeons in each model with real demographic data using χ2, Fisher exact, and proportion tests. There was a significantly higher mean representation of female (35.8% vs 14.7%; P < .001) and non-White (37.4% vs 22.8%; P < .001) surgeons among trainees than attending surgeons. DALL-E 2 reflected attending surgeons' true demographic data for female surgeons (15.9% vs 14.7%; P = .39) and non-White surgeons (22.6% vs 22.8%; P = .92) but underestimated trainees' representation for both female (15.9% vs 35.8%; P < .001) and non-White (22.6% vs 37.4%; P < .001) surgeons. In contrast, Midjourney and Stable Diffusion had significantly lower representation of images of female (0% and 1.8%, respectively; P < .001) and non-White (0.5% and 0.6%, respectively; P < .001) surgeons than DALL-E 2 or true demographic data. Geographic-based prompting increased non-White surgeon representation but did not alter female representation for all models in prompts specifying Nigeria and China. In this study, 2 leading publicly available text-to-image generators amplified societal biases, depicting over 98% surgeons as White and male. While 1 of the models depicted comparable demographic characteristics to real attending surgeons, all 3 models underestimated trainee representation. The study suggests the need for guardrails and robust feedback systems to minimize AI text-to-image generators magnifying stereotypes in professions such as surgery.

Exemplifying the situated nature of teacher noticing: Elementary teachers' use of insider knowledge

AbstractTeacher noticing is a crucial aspect of responsive and ambitious mathematics teaching, allowing educators to understand students' mathematical thinking and make informed decisions. However, this noticing is influenced by teachers' insider knowledge (i.e., past and present knowledge and experience with students), which is shaped by societal norms, biases, and expectations, including potentially harmful deficit discourses. This study investigates teachers' noticing of written work from a situated perspective using qualitative secondary analysis. The goal of this work is to exemplify upper elementary teachers' use of historical knowledge gained from classroom interactions or informal school settings, along with local and broader contextual factors. Data analysis included elementary teachers' noticing mathematical thinking in written work from their class, their use of insider knowledge of their students, and distinctions in orientations toward students and their thinking. Results indicate how insider knowledge enhances, impedes, or does not impact individual teachers' focus on the details and reasoning evidenced in their students' strategies. The findings contribute to a more nuanced understanding of the complexities of teacher noticing and emphasize the importance of acknowledging the socio–cultural and historical context which is needed for equitable mathematics teaching. Implications are discussed.

Unvarnished Truth of Islamophobia: A Case Study of Misrepresentation of Muslim Identity in “The Fatwa Girl” by Akbar Agha

This research thesis carefully examines the complex relationships between literature, Muslim identity, and the widespread issue of Islamophobia by critically engaging with the novel The Fatwa Girl written by Akbar Agha. Through content analysis, the study seeks to expose the implicit representations and analyses found in the novel, highlighting the complex ways in which Muslim identity is constructed and Islamophobic myths are maintained in the narrative. Through an analysis of the novel's role in the spread of Islamophobia and a close examination of its complex depiction of Muslim identity, the study highlights the moral obligations of writers and publishers—the current study contributes to the broader discussion of how literature shapes public attitudes towards Muslims and Islam. The study offers a strong analytical framework and is based on postcolonial theory as expressed by Edward Said in Orientalism and multicultural literary theory, which was influenced by Homi Bhabha's The Location of Culture. Literature's transformative effect on societal biases and identity conceptions is revealed, advancing enlightened conversations in a culture dealing with complex Islamophobia.

Challenges and Prospect of Women Business Start Up in North Western Nigeria

This study aimed to explore the personality and environmental challenges and prospects faced by women in business start-ups in North Western Nigeria. A total of 273 female entrepreneurs were surveyed using a structured questionnaire. The data collected were analysed using the Statistical Package for Social Sciences (SPSS). The study found that lack of access to funding sources, legal and regulatory barriers, societal and cultural biases, lack of access to relevant information and technology and limited access to professional networks and mentors were the major environmental challenges faced by women in business start-ups. The study further revealed that women possess the confidence to take risks and pursue opportunities, handle stress and uncertainty, possess the ability to adapt to changing circumstances, communicate and negotiate with stakeholders, and possess the persistence and resilience necessary to overcome challenges in their business start-ups. The study recommends that the government, private organizations, and other stakeholders should work together to address the environmental challenges faced by women in business start-ups in North Western Nigeria by providing access to funding sources, mentorship, networking opportunities, and creating an enabling business environment.

Coping with HIV/AIDS-Related Stigma and Discrimination: An Overview Based on a Personal Loss

Stigma and discrimination have a profound psychological and societal impact on individuals living with HIV/AIDS and their communities. This research offers an in-depth exploration of these challenges, drawing insights from a vast array of sources, such as articles, books, films, documentaries, websites, reports, resolutions, and famous stories. In order to provide valuable insights into building resilience and fostering supportive environments, the study simultaneously examines the coping mechanisms and strategies employed by the infected, as depicted in the literature used therein. The example of Miha, a young woman who faced societal biases despite her achievements, serves as a poignant reminder of the many sad narratives behind the statistics. This paper ultimately emphasizes the urgent need for continued efforts in awareness, education, and advocacy of combating HIV/AIDS-related stigma and discrimination, and culminates in a call for collective action, empathy, and a shared vision of an inclusive world.

The Bigger the Better?

This article looks at a controversy over the ‘better’ architecture for conversational AI that unfolds initially along the question of the ‘right’ size of models. Current generative models such as ChatGPT and DALL-E follow the imperative of the largest possible, ever more highly scalable, training dataset. I therefore first describe the technical structure of large language models and then address the problems of these models which are known for reproducing societal biases or so-called hallucinations. As an ‘alternative’, computer scientists and AI experts call for the development of much smaller language models linked to external databases, that should minimize the issues mentioned above. As this paper will show, the presentation of this structure as ‘alternative’ adheres to a simplistic juxtaposition of different architectures that follows the imperative of a computable reality, thereby causing problems analogous to the ones it tried to circumvent.

Women's rights and child abductions under the Hague Convention

AbstractThe Hague Convention on Civil Aspects of International Child Abduction is a multinational treaty designed to protect children internationally from the harmful effects of parental abduction. The Convention requires a mandatory return of a child who was wrongfully removed or retained outside the country of her habitual residence unless a narrow defense preventing return can be proven. When drafted, it was assumed that most abductors would be non‐custodial parents disappointed by an adverse custody decision in the home country. It is now clear that women have been disproportionately affected because in many, if not most, U.S. cases, the respondents, that is, the parents accused of the abduction, are the children's primary caregivers. When the child's home country is not willing or capable of protecting the mother and the child, they are forced to flee from often near‐lethal danger. Therefore, many cases brought under the Hague Convention involve severe domestic violence. The Hague Convention allows courts to deny the return if it would expose the child to a “grave risk of physical or psychological harm.” Nonetheless, a limited judicial understanding of domestic violence, coupled with societal gender biases, has impeded the application of this defense. Several recent developments discussed in this essay reflect a growing understanding of the lasting traumatic impact of domestic violence on these child victims.

Roundtable on Artificial Intelligence, Advertising, and Society

Abstract: This roundtable focuses on the interplay between artificial intelligence (AI) and advertising, and its broader societal implications. The discussion first provides a historical overview of AI and then moves to AI's relevance to the advertising industry, highlighting tools like programmatic advertising and generative AI. It then covers the definitions, applications, and misperceptions surrounding AI. The participants emphasize the significant challenges of AI transparency, disclosure, and the need for consumer education, particularly given AI's potential for misinformation, biases, and deepfakes. The panel debates concerns about AI perpetuating societal biases, the need for human oversight, and AI's implications for diversity, equity, and inclusion (DEI) within advertising, marketing, and strategic communication. The roundtable delves into AI's impact on work and employment, and it underscores AI as a tool to enhance productivity that ultimately validates the enduring value of human understanding, creativity, and connection. The conclusion stresses the need for ethical considerations, ongoing learning about AI and technology, and the importance of human emotion in relating to consumers. With cautious optimism, the panelists advocate for a balanced but critical approach to AI's integration into advertising and marketing.

Evolution and significance of women entrepreneurs in India

Women entrepreneurship has emerged as a significant contributor to India's economic growth and development. The presence and significance of women entrepreneurs in India have witnessed a remarkable evolution over the past few decades. Historically constrained by societal norms and gender biases, women in India faced numerous challenges in pursuing entrepreneurial ventures. Over the years, women entrepreneurs in India have shattered barriers and made remarkable strides in various sectors. However, despite these achievements, women continue to face unique challenges in their entrepreneurial journey, changing social attitudes and policy support, women have emerged as a driving force in the country's entrepreneurial landscape. This research paper aims to examine the evolution of women entrepreneurship in India and its growing significance for the economy and society. It begins by delving into the historical context, highlighting the traditional roles assigned to women and the barriers they encountered in becoming entrepreneurs. The study then proceeds to explore key socio-economic and policy developments that have fostered the empowerment and growth of women entrepreneurs in recent times. The methodology involves a comprehensive review of existing literature, reports, and statistical data related to women entrepreneurship in India. Additionally, the paper addresses the economic impact of women entrepreneurs in terms of job creation, GDP contribution, and empowerment of women in the workforce. It also discusses the societal significance of women entrepreneurs as role models, fostering a culture of gender equality and women empowerment. Ultimately, the research emphasizes the need for continued support and targeted policies to further promote and nurture women entrepreneurship in India.