A Multifaceted View on Discrimination in Software Development Careers

• URL: http://arxiv.org/abs/2510.22457v2
• Date: Fri, 31 Oct 2025 02:57:02 GMT
• Title: A Multifaceted View on Discrimination in Software Development Careers
• Authors: Shalini Chakraborty, Sebastian Baltes,
• Abstract summary: The State of Devs 2025 survey with 8,717 participants revealed that other forms of discrimination are similarly prevalent but receive less attention.<n>This includes discrimination based on age, political perspective, disabilities, or cognitive differences such as neurodivergence.<n>Our study covers multiple identity facets, including age, gender, race, and disability.
• Score: 3.1006429989273054
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 prevalent but receive considerably less attention. This includes discrimination based on age, political perspective, disabilities, or cognitive differences such as neurodivergence. We conducted a secondary analysis of 800 open-ended survey responses to examine patterns of perceived discrimination, as well as related challenges and negative impacts. Our study covers multiple identity facets, including age, gender, race, and disability. We found that age- and gender-related discrimination was the most frequently reported workplace issue, but discrimination based on political and religious views emerged as further notable concerns. Most of the participants who identified as female cited gender as the primary source of discrimination, often accompanied by intersectional factors such as race, political views, age, or sexual orientation. Discrimination related to caregiving responsibilities was reported by all gender identities. Regarding the negative impacts of workplace issues, many participants described modifying their appearance or behavior in response to gender biases. Gender also appeared to influence broader career challenges, as women and non-binary respondents reported experiencing almost all workplace issues at higher rates, particularly discrimination (35%) and mental health challenges (62%). Our goal is to raise awareness in the research community that discrimination in software development is multifaceted, and to encourage researchers to select and assess relevant facets beyond age and gender when designing software engineering studies.

Related papers

• The Role of Social Identity in Shaping Biases Against Minorities in Software Organizations [5.704788741312527]
  This study investigates four distinct forms of bias: lack of career development, stereotyped task selection, unwelcoming environments, and identity attacks.<n>Women were more than three times as likely as men to face career development bias, task selection bias, and an unwelcoming environment.<n>In parallel, individuals from marginalized ethnic backgrounds were disproportionately targeted by identity attacks.
  arXiv  Detail & Related papers  (2026-01-29T04:39:32Z)
• Overcoming Obstacles: Challenges of Gender Inequality in Undergraduate ICT Programs [0.34865180646161636]
  In Brazil, women represent less than 18% of ICT students in higher education.<n>This study explores the perceptions of women undergraduate students in ICT regarding gender inequality.
  arXiv  Detail & Related papers  (2025-05-02T18:28:48Z)
• Beauty and the Bias: Exploring the Impact of Attractiveness on Multimodal Large Language Models [51.590283139444814]
  Physical attractiveness has been shown to influence human perception and decision-making.<n>The role that attractiveness plays in the assessments and decisions made by multimodal large language models (MLLMs) is unknown.<n>We conduct an empirical study with 7 diverse open-source MLLMs evaluated on 91 socially relevant scenarios and a diverse dataset of 924 face images.
  arXiv  Detail & Related papers  (2025-04-16T16:02:55Z)
• Perceptions of Entrepreneurship Among Graduate Students: Challenges, Opportunities, and Cultural Biases [0.0]
  The paper focuses on the perceptions of entrepreneurship of graduate students enrolled in a digital-oriented entrepreneurship course. The main issues raised by the students were internal traits and external obstacles, such as lack of resources and support. Gender discrimination and cultural biases persist, limiting opportunities and equality for women.
  arXiv  Detail & Related papers  (2024-05-10T08:35:18Z)
• A Multilingual Perspective on Probing Gender Bias [0.0]
  Gender bias is a form of systematic negative treatment that targets individuals based on their gender. This thesis investigates the nuances of how gender bias is expressed through language and within language technologies.
  arXiv  Detail & Related papers  (2024-03-15T21:35:21Z)
• Exposing Algorithmic Discrimination and Its Consequences in Modern Society: Insights from a Scoping Study [0.0]
  This study delves into various studies published over the years reporting algorithmic discrimination. We aim to support software engineering researchers and practitioners in addressing this issue.
  arXiv  Detail & Related papers  (2023-12-08T05:09:00Z)
• "I'm fully who I am": Towards Centering Transgender and Non-Binary Voices to Measure Biases in Open Language Generation [69.25368160338043]
  Transgender and non-binary (TGNB) individuals disproportionately experience discrimination and exclusion from daily life. We assess how the social reality surrounding experienced marginalization of TGNB persons contributes to and persists within Open Language Generation. We introduce TANGO, a dataset of template-based real-world text curated from a TGNB-oriented community.
  arXiv  Detail & Related papers  (2023-05-17T04:21:45Z)
• Towards Understanding Gender-Seniority Compound Bias in Natural Language Generation [64.65911758042914]
  We investigate how seniority impacts the degree of gender bias exhibited in pretrained neural generation models. Our results show that GPT-2 amplifies bias by considering women as junior and men as senior more often than the ground truth in both domains. These results suggest that NLP applications built using GPT-2 may harm women in professional capacities.
  arXiv  Detail & Related papers  (2022-05-19T20:05:02Z)
• A Deep Dive into Dataset Imbalance and Bias in Face Identification [49.210042420757894]
  Media portrayals often center imbalance as the main source of bias in automated face recognition systems. Previous studies of data imbalance in FR have focused exclusively on the face verification setting. This work thoroughly explores the effects of each kind of imbalance possible in face identification, and discuss other factors which may impact bias in this setting.
  arXiv  Detail & Related papers  (2022-03-15T20:23:13Z)
• Are Commercial Face Detection Models as Biased as Academic Models? [64.71318433419636]
  We compare academic and commercial face detection systems, specifically examining robustness to noise. We find that state-of-the-art academic face detection models exhibit demographic disparities in their noise robustness. We conclude that commercial models are always as biased or more biased than an academic model.
  arXiv  Detail & Related papers  (2022-01-25T02:21:42Z)
• Anatomizing Bias in Facial Analysis [86.79402670904338]
  Existing facial analysis systems have been shown to yield biased results against certain demographic subgroups. It has become imperative to ensure that these systems do not discriminate based on gender, identity, or skin tone of individuals. This has led to research in the identification and mitigation of bias in AI systems.
  arXiv  Detail & Related papers  (2021-12-13T09:51:13Z)
• Feature Representation in Deep Metric Embeddings [0.0]
  This study takes embeddings trained to discriminate faces (identities) and uses unsupervised clustering to identify the features involved in facial identity discrimination. In the intra class scenario, the inference process distinguishes common attributes between single identities, achieving 90.0% and 76.0% accuracy for beards and glasses, respectively. The system can also perform extra class sub-discrimination with a high accuracy rate, notably 99.3%, 99.3% and 94.1% for gender, skin tone, and age, respectively.
  arXiv  Detail & Related papers  (2021-02-05T13:53:10Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.