Invisible Load: Uncovering the Challenges of Neurodivergent Women in Software Engineering

2025-12-04

AI Summary

This study systematically investigates, for the first time, the intersectional challenges faced by neurodivergent women in software engineering—arising from the compounding effects of gender bias and neurocognitive differences (e.g., autism, ADHD)—manifesting as cognitive, social, organizational, and career-development barriers. It identifies how misdiagnosis, camouflaging behaviors, and male-centric workplace cultures exacerbate stress, burnout, and attrition. Employing a mixed-methods approach integrating the InclusiveMag framework and GenderMag gender-inclusive heuristic evaluation, the research comprises three phases: systematic literature review, persona development, and collaborative participatory workshops. Five core challenge categories are derived. The findings establish a theoretical foundation and actionable pathways for designing assessment tools and evidence-based interventions tailored to neurodivergent women, thereby advancing the development of genuinely inclusive technical workplaces.

Technology Category

• Application Domains: Software Engineering
• Humans and AI: AI for Accessibility
• Computer Vision: Bias, Fairness & Privacy

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Fairness, privacy, and diversity in economic environments
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

Neurodivergent women in Software Engineering (SE) encounter distinctive challenges at the intersection of gender bias and neurological differences. To the best of our knowledge, no prior work in SE research has systematically examined this group, despite increasing recognition of neurodiversity in the workplace. Underdiagnosis, masking, and male-centric workplace cultures continue to exacerbate barriers that contribute to stress, burnout, and attrition. In response, we propose a hybrid methodological approach that integrates InclusiveMag's inclusivity framework with the GenderMag walkthrough process, tailored to the context of neurodivergent women in SE. The overarching design unfolds across three stages, scoping through literature review, deriving personas and analytic processes, and applying the method in collaborative workshops. We present a targeted literature review that synthesize challenges into cognitive, social, organizational, structural and career progression challenges neurodivergent women face in SE, including how under/late diagnosis and masking intensify exclusion. These findings lay the groundwork for subsequent stages that will develop and apply inclusive analytic methods to support actionable change.

Problem

• Addressing underrepresentation of neurodivergent women in software engineering research
• Identifying cognitive, social, and organizational barriers they face
• Developing inclusive methods to reduce stress, burnout, and attrition

Innovation

• Hybrid methodology combining InclusiveMag and GenderMag
• Three-stage design: literature review, personas, workshops
• Tailored analytic methods for neurodivergent women in SE