Related papers: Beyond Stereotypes: Exploring How Minority College…

Context: Gender inequality is a widely discussed issue across various sectors, including Information Technology and Communication (ICT). In Brazil, women represent less than 18% of ICT students in higher education. Prior studies highlight…

An analysis of institutional data to understand the outcome of the many obstacles faced by students from historically disadvantaged backgrounds is important in order to work towards promoting equity and inclusion for all students. We use 10…

Toxic comment classification models are often found biased toward identity terms which are terms characterizing a specific group of people such as "Muslim" and "black". Such bias is commonly reflected in false-positive predictions, i.e.…

Gender bias represents a form of systematic negative treatment that targets individuals based on their gender. This discrimination can range from subtle sexist remarks and gendered stereotypes to outright hate speech. Prior research has…

Despite attempts to increase gender parity in politics, global efforts have struggled to ensure equal female representation. This is likely tied to implicit gender biases against women in authority. In this work, we present a comprehensive…

Online communities can serve as meaningful sources of social support, particularly for marginalized and vulnerable groups. Disclosure of personal information facilitates integration into online communities but may also expose individuals to…

Underrepresented minority (URM) students are subjected to historically rooted inequities when pursuing an education, especially in STEM disciplines with little diversity. In order to make STEM education equitable and inclusive, evidence for…

Sexism in online content is a pervasive issue that necessitates effective classification techniques to mitigate its harmful impact. Online platforms often have sexist comments and posts that create a hostile environment, especially for…

Stereotypes influence social perceptions and can escalate into discrimination and violence. While NLP research has extensively addressed gender bias and hate speech, stereotype detection remains an emerging field with significant societal…

Moral outrage has become synonymous with social media in recent years. However, the preponderance of academic analysis on social media websites has focused on hate speech and misinformation. This paper focuses on analyzing moral judgements…

The Stereotype Content model (SCM) states that we tend to perceive minority groups as cold, incompetent or both. In this paper we adapt existing work to demonstrate that the Stereotype Content model holds for contextualised word embeddings,…

This article investigates the critical issue of dataset bias in medical imaging, with a particular emphasis on racial disparities caused by uneven population distribution in dataset collection. Our analysis reveals that medical segmentation…

Cyberbullying is of extreme prevalence today. Online-hate comments, toxicity, cyberbullying amongst children and other vulnerable groups are only growing over online classes, and increased access to social platforms, especially post…

Due to massive adoption of social media, detection of users' depression through social media analytics bears significant importance, particularly for underrepresented languages, such as Bangla. This study introduces a well-grounded approach…

Selective exposure is the main driver for the economy of attention when consuming online content. We select information adhering to our system of beliefs and ignore dissenting information. However, even personal interest is likely to play a…

Despite decades of anti-caste efforts, sociocultural practices that marginalize lower-caste groups in India remain prevalent and have even proliferated with the use of social media. This paper examines how groups engaged in caste-based…

In this work, we examine the extent to which embeddings may encode marginalized populations differently, and how this may lead to a perpetuation of biases and worsened performance on clinical tasks. We pretrain deep embedding models (BERT)…

Biases in automated clinical decision-making using Electronic Healthcare Records (EHR) impose significant disparities in patient care and treatment outcomes. Conventional approaches have primarily focused on bias mitigation strategies…

The purpose of the paper is to examine the perceptions of entrepreneurship of graduate students enrolled in a digital-oriented entrepreneurship course, focusing on the challenges and opportunities related to starting a business. In today's…

Psychological stress detection is an important task for mental healthcare research, but there has been little prior work investigating the effectiveness of psychological stress models on minority individuals, who are especially vulnerable…