Related papers: Implicit Gender Bias in Computer Science -- A Qual…

The growing prominence of large language models (LLMs) in daily life has heightened concerns that LLMs exhibit many of the same gender-related biases as their creators. In the context of hiring decisions, we quantify the degree to which…

Data science can offer answers to a wide range of social science questions. Here we turn attention to the portrayal of women in movies, an industry that has a significant influence on society, impacting such aspects of life as self-esteem…

Scientific attention is unevenly distributed, creating inequities in recognition and distorting access to opportunities. Using citations as a proxy, we quantify disparities in attention by gender and institutional prestige. We find that…

Cyberbullying is a widespread adverse phenomenon among online social interactions in today's digital society. While numerous computational studies focus on enhancing the cyberbullying detection performance of machine learning algorithms,…

During the past decades the importance of soft skills for labour market outcomes has grown substantially. This carries implications for labour market inequality, since previous research shows that soft skills are not valued equally across…

Background. Women bring unique problem-solving skills to software development, often favoring a holistic approach and attention to detail. In software testing, precision and attention to detail are essential as professionals explore system…

In recent years, there have been significant advancements in computer vision which have led to the widespread deployment of image recognition and generation systems in socially relevant applications, from hiring to security screening.…

Conversations around diversity and inclusion in software engineering often focus on gender and racial disparities. However, the State of Devs 2025 survey with 8,717 participants revealed that other forms of discrimination are similarly…

Automated computer vision systems have been applied in many domains including security, law enforcement, and personal devices, but recent reports suggest that these systems may produce biased results, discriminating against people in…

To mitigate gender bias in contextualized language models, different intrinsic mitigation strategies have been proposed, alongside many bias metrics. Considering that the end use of these language models is for downstream tasks like text…

The issue of gender bias in scientific publications has been a topic of ongoing debate. One aspect of this debate concerns whether women receive equal credit for their contributions compared to men. Conventional wisdom suggests that women…

Collaboration is crucial in Software Engineering (SE), yet factors like gender bias can shape team dynamics and behaviours. This study examines an eight-week project involving 39 SE students across eight teams contributing to GitHub…

Currently, there is a surge of interest in fair Artificial Intelligence (AI) and Machine Learning (ML) research which aims to mitigate discriminatory bias in AI algorithms, e.g. along lines of gender, age, and race. While most research in…

Data science research into fake news has gathered much momentum in recent years, arguably facilitated by the emergence of large public benchmark datasets. While it has been well-established within media studies that gender bias is an issue…

Large language models (LLMs) are rapidly being adopted as research assistants, particularly for literature review and reference recommendation, yet little is known about whether they introduce demographic bias into citation workflows. This…

Gender bias in grant allocation is a deviation from the principle that scientific merit should guide grant decisions. However, most studies on gender bias in grant allocation focus on gender differences in success rates, without including…

The recent paper by AlShebli et al. (2020) investigates the impact of mentorship in young scientists. Among their conclusions, they state that female prot\'eg\'es benefit more from male than female mentorship. We herein expose a critical…

We address the critical issue of biased algorithms and unfair rankings, which have permeated various sectors, including search engines, recommendation systems, and workforce management. These biases can lead to discriminatory outcomes in a…

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…

Underrepresentation of women in the academic system is a problem common to many countries, often associated with gender discrimination. In the Italian academic context in particular, favoritism is recognized as a diffuse phenomenon…