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The Role of Social Identity in Shaping Biases Against Minorities in Software Organizations

Sayma Sultana, London Cavaletto, Bianca Trinkenreich, Amiangshu Bosu

TL;DR

The paper addresses how social identity shapes biases against minorities in software organizations using Social Identity Theory (SIT). It uses a vignette-based online survey to quantify four bias types—Career Development Bias, Stereotyped Task Selection Bias, Unwelcoming Environments, and Identity Attacks—and analyzes how demographics predict victimization, consequences, and perpetrators' motivations. Logistics regression and thematic analysis reveal that CDB and TSB are most prevalent, women face markedly higher risk across several biases, and ethnic minorities are disproportionately targeted by identity attacks, with age and experience modulating risk. The study contributes an intersectional SIT framework for bias in computing, provides actionable recommendations for individuals and managers, and underscores the need for targeted, context-aware interventions to improve inclusion and retention in software engineering.

Abstract

While systemic workplace bias is well-documented in non-computing fields, its specific impact on software engineers remains poorly understood. This study addresses that gap by applying Social Identity Theory (SIT) to investigate four distinct forms of bias: lack of career development, stereotyped task selection, unwelcoming environments, and identity attacks. Using a vignette-based survey, we quantified the prevalence of these biases, identified the demographics most affected, assessed their consequences, and explored the motivations behind biased actions. Our results show that career development and task selection biases are the most prevalent forms, with over two-thirds of victims experiencing them multiple times. Women were more than three times as likely as men to face career development bias, task selection bias, and an unwelcoming environment. In parallel, individuals from marginalized ethnic backgrounds were disproportionately targeted by identity attacks. Our analysis also confirms that, beyond gender and race, factors such as age, years of experience, organization size, and geographic location are significant predictors of bias victimization.

The Role of Social Identity in Shaping Biases Against Minorities in Software Organizations

TL;DR

The paper addresses how social identity shapes biases against minorities in software organizations using Social Identity Theory (SIT). It uses a vignette-based online survey to quantify four bias types—Career Development Bias, Stereotyped Task Selection Bias, Unwelcoming Environments, and Identity Attacks—and analyzes how demographics predict victimization, consequences, and perpetrators' motivations. Logistics regression and thematic analysis reveal that CDB and TSB are most prevalent, women face markedly higher risk across several biases, and ethnic minorities are disproportionately targeted by identity attacks, with age and experience modulating risk. The study contributes an intersectional SIT framework for bias in computing, provides actionable recommendations for individuals and managers, and underscores the need for targeted, context-aware interventions to improve inclusion and retention in software engineering.

Abstract

While systemic workplace bias is well-documented in non-computing fields, its specific impact on software engineers remains poorly understood. This study addresses that gap by applying Social Identity Theory (SIT) to investigate four distinct forms of bias: lack of career development, stereotyped task selection, unwelcoming environments, and identity attacks. Using a vignette-based survey, we quantified the prevalence of these biases, identified the demographics most affected, assessed their consequences, and explored the motivations behind biased actions. Our results show that career development and task selection biases are the most prevalent forms, with over two-thirds of victims experiencing them multiple times. Women were more than three times as likely as men to face career development bias, task selection bias, and an unwelcoming environment. In parallel, individuals from marginalized ethnic backgrounds were disproportionately targeted by identity attacks. Our analysis also confirms that, beyond gender and race, factors such as age, years of experience, organization size, and geographic location are significant predictors of bias victimization.
Paper Structure (20 sections, 2 figures, 7 tables)

This paper contains 20 sections, 2 figures, 7 tables.

Table of Contents

  1. Introduction
  2. Research Questions
  3. Related Works
  4. Research Methodology
  5. Biases through the lens of SIT
  6. Questionnaire Design
  7. Vignettes design
  8. Questions on a vignette
  9. Demographic questions
  10. Survey validation
  11. Data collection
  12. Data analysis
  13. Results
  14. Respondent demographics
  15. RQ1: Associations between demographic factors and being victims
  16. ...and 5 more sections

Figures (2)

  • Figure 1: An overview of our research method
  • Figure 2: The associations between bias types and categories of bias consequences