About the Workshop

This second workshop focusing on intersectionality and software engineering in education, industry, research, product, policy, and practice. The workshop will be held in Montreal, Canada as a part of the 2026 Foundations of Software Engineering conference.

For questions, email the Program Committee Chair: Alicia JW Takaoka, Send email

Motivation

Software and smart devices are becoming more embedded in our daily lives. Ethical concerns about omissions of groups of people and the biases of developers have come under rightful scrutiny by researchers and the general public. Our increased dependence on the built environment and those who make it require critical examination and reflection. In this second edition of the workshop, we examine intersectionality, which is the recognition that individual categorizations like race and gender create interdependent systems of discrimination or systemic disadvantage, as a requirement in software engineering education and in software engineering. Submissions examining the team composition, management practices, user perspectives, products in development or in use, processes that guide hiring practices or workplace culture, and policies that govern aspects of software development are encouraged. This workshop explores the importance of intersectionality as a concept in software engineering.

The use of networked technologies to highlight the state of academia and work in recent years has led to intense polarization and fragmentation across cultural, political, and geographic boundaries. In response, empirical software engineering researchers have produced results that indicate the need for changes to software engineering practices, policies, and team composition, hiring, and education through sustainability and inclusion. This work impacts teams, users, and organizations software and applications are designed and deployed. An example of this is seen in the EU AI Act and the call to create AI, algorithmic, and autonomous systems auditing methods and impartial auditors.

Call for Papers

This workshop theme is "Intersectional in SE: People, Process, Policy, and Product." This theme offers an opportunity to focus on the social, cultural, political, and economic examination and shaping of software engineering practices, policies, and their consequences as well as critical reflection on personal experience. This emphasis invites a range of scholarly inquiries, such as how to uncover bias in algorithms, question the western-centric development of AI, and evaluate accessible learning software. Submissions for the workshop may include empirical, critical, reflection, experienced--based research and theoretical work, as well as richly described practice cases and demonstrations. The topics of interest include, but are not limited to:

Submit your papers to https://fse26-intersectionality.hotcrp.com/

Author Guidelines

All submissions must be in English and in PDF format. Submission Format: Follow the guidelines on "How to Submit (Companion proceedings)" on the main FSE 2026 website https://conf.researchr.org/track/fse-2026/fse-2026-how-to-submit. Please note the \booktitle and two column document requirements.

The timeline for papers is:

Starting January 1, 2026, ACM will fully transition to Open Access. All ACM publications, including those from ACM-sponsored conferences, will be 100% Open Access. Authors will have two primary options for publishing Open Access articles with ACM: the ACM Open institutional model or by paying Article Processing Charges (APCs). With over 1,800 institutions already part of ACM Open, the majority of ACM-sponsored conference papers will not require APCs from authors or conferences (currently, around 70-75%). However, the corresponding author must be from an ACM institution. Note that not any author is considered for the free submission. Only the correspoonding author is considered for this publication scheme. Authors from institutions not participating in ACM Open will need to pay an APC to publish their papers, unless they qualify for a financial or discretionary waiver. To find out whether an APC applies to your article, please consult the list of participating institutions in ACM Open and review the APC Waivers and Discounts Policy. Keep in mind that waivers are rare and are granted based on specific criteria set by ACM. Understanding that this change could present financial challenges, ACM has approved a temporary subsidy for 2026 to ease the transition and allow more time for institutions to join ACM Open. The subsidy will offer: $250 APC for ACM/SIG members $350 for non-members This represents a 65% discount, funded directly by ACM. Authors are encouraged to help advocate for their institutions to join ACM Open during this transition period. This temporary subsidized pricing will apply to all conferences scheduled for 2026. “Extended abstracts” (limited to five pages or less) are free of APC charges. Please note that “short papers” are charged, but “extended abstracts” are not (see https://libraries.acm.org/acmopen/article-types).

Workshop Schedule

Coming Soon

Accepted Papers

The Emotional Cost of Technical Debt: Quantifying Developer Frustration Across Debt-Related Concern Themes

Authors: Mohamad Kassab, Izah Sohail, João Paulo Fernandes

Technical debt affects not only code quality but also the developers who must continuously manage its consequences. This paper introduces the Negative Emotion Index (NEI), a scalable metric for quantifying developers’ emotional responses to technical debt–related concerns. Using over 73,000 issue tracker comments from three large Apache projects (Hadoop, Kafka, and Flink), we combine keyword-based debt mining, topic modeling, and transformer-based emotion classification to characterize frustration across seven latent debt-related concern themes discovered via LDA. We find that operational and infrastructure-oriented concerns—such as flaky continuous integration, configuration complexity, and deployment instability—are associated with systematically higher negative emotional intensity than implementation-level and API usability concerns. These results highlight emotional burden as an underexplored dimension of software maintainability, complementing established technical debt cost models. We further discuss how NEI can be integrated into prioritization frameworks and engineering dashboards to support more human-centered technical debt management.

 

Team Diversity Promotes Software Fairness: An Experiment on Fairness-Aware Requirements Prioritization

Cleyton Magalhães, Ronnie de Souza Santos, Bimpe Ayoola, Brody Stuart-Verner, Italo Santos

Background: Fairness and diversity are receiving growing attention in software engineering, particularly as AI and machine learning systems increasingly influence decision making processes. While fairness is often examined at the algorithmic or data level, there is limited understanding of how it is addressed during the early stages of software development. Moreover, little is known about how team diversity affects fairness related decisions in software projects. Aims: This study investigates how diversity in software teams influences fairness aware behavior during requirements prioritization. Method: A controlled experiment was conducted with 27 pairs of software engineering students, including 13 LGBTQ diverse pairs and 14 non diverse pairs. Each pair prioritized user stories with varying fairness implications. Descriptive statistics were used to analyze attitudes and prioritization outcomes, and thematic analysis was applied to examine the reasoning behind participants’ decisions. Results: Both groups demonstrated general alignment with fairness principles, prioritizing features that promoted equitable treatment and rejecting those that posed fairness risks. However, LGBTQ diverse pairs were more consistent in rejecting fairness risking stories and made fewer fairness related misprioritization errors. Their reasoning emphasized inclusion, non discrimination, and ethical responsibility, whereas non diverse pairs adopted a more pragmatic, goal oriented perspective. Conclusions: The findings indicate that fairness should be considered from the earliest stages of software development. Team diversity can enhance the identification and interpretation of fairness issues during requirements analysis, fostering more reflective and inclusive decision making. Preprint

 

Gender bias and propagation of stereotypes in GenAI-assisted recruitment

Martina Ullasci, Marco Rondina, Riccardo Coppola, Antonio Vetro'

In recent years, generative artificial intelligence (GenAI) systems have assumed increasingly crucial roles in personnel recruitment and candidate profiles analysis. However, using large language models introduces the risk of perpetuating and exacerbating existing gender stereotypes in the labour market. This research aims to evaluate this phenomenon, analysing how a state-of-the-art generative model (GPT-5) suggests occupations and represents ideal candidates based on their gender, focusing on under 35 years old Italian graduates. The study consists of two complementary experiments. In the Candidate-driven experiment, the model is prompted to provide job suggestions for 24 synthetic candidate profiles, balanced by gender, age, experience, and professional field. Results show that, although no significant differences emerged in job titles, gendered linguistic patterns exist in the adjectives attributed to female and male candidates, indicating a tendency of the model to associate women with emotional and empathetic traits, while men with strategic and analytical ones. The Job-driven experiment employed 114 LinkedIn job advertisements as prompts to generate textual and visual representations of ideal candidates. The analysis of the outputs revealed a clear gender polarisation: the model assigned 71% of profiles to male and 29% to female gender. The strongest association emerged in HR & People Operations occupations, assigned exclusively to female candidates, and Operations, Technical & Manufacturing jobs, assigned exclusively to male candidates. Visual analysis confirms the perpetuation of gender stereotypes, depicting women in more approachable postures and men in assertive roles. These results suggest that, in the recruitment domain and under the experimental settings of this study, GenAI models do not simply reflect the gender biases of the training data, but also amplify them. The research raises an ethical question regarding the use of these models in HR decision support, highlighting the need for transparency and bias mitigation strategies to ensure fairness and inclusive representation. Preprint

 

Analysis Of Linguistic Stereotypes in Single and Multi-Agent Generative AI Architectures

Martina Ullasci, Marco Rondina, Riccardo Coppola, Flavio Giobergia, Riccardo Bellanca, Gabriele Mancari Pasi, Luca Prato, Federico Spinoso, Silvia Tagliente

Many works in the literature show that LLM outputs exhibit discriminatory behaviour, triggering stereotype-based inferences based on the dialect in which the inputs are written. This bias has been shown to be particularly pronounced when the same inputs are provided to LLMs in Standard American English (SAE) and African-American English (AAE). In this paper, we replicate existing analyses of dialect-sensitive stereotype generation in LLM outputs and investigate the effects of mitigation strategies, including prompt engineering (role-based and Chain-Of-Thought prompting) and multi-agent architectures composed of generate-critique-revise models. We define eight prompt templates to analyse different ways in which dialect bias can manifest, such as suggested names, jobs, and adjectives for SAE or AAE speakers. We use an LLM-as-judge approach to evaluate the bias in the results, using a 1-10 scale. Our results show that stereotype-bearing differences emerge between SAE- and AAE-related outputs across all template categories, with the strongest effects observed in adjective and job attribution. Baseline disparities vary substantially by model, with the largest SAE–AAE differential observed in Claude Haiku and the smallest in Phi-4 Mini. Chain-Of-Thought prompting proved to be an effective mitigation strategy for Claude Haiku, whereas the use of a multi-agent architecture ensured consistent mitigation across all the models. These findings suggest that for intersectionality-informed software engineering, fairness evaluation should include model-specific validation of mitigation strategies, and workflow-level controls (e.g., agentic architectures involving critique models) in high-impact LLM deployments. The current results are exploratory in nature and limited in scope, but can lead to extensions and replications by increasing the dataset size and applying the procedure to different languages or dialects. Preprint

 

An Intersectional Approach to Walking Navigation Systems for Women’s Urban Safety

Giulio Attenni, Novella Bartolini

Technological solutions such as urban safety applications and safe path planning algorithms have emerged to address gender-based violence and harassment in public spaces. Through an intersectional lens, this paper critically analyzes existing safe path planning systems, synthesizing literature from feminist geography, humancomputer interaction, and computer science to examine algorithmic approaches and underlying data infrastructures. We propose intersectional design principles that interrogate data sources, design for diverse identities, center structural change, and value embodied community knowledge. This work call on the field to shift from overtechnical optimization to equitable safety design that focuses on the real experiences of women with intersecting identities.

Keynote

Speaker photo

Title: The Software We Build, and the People We Are

Abstract: Software is not only built with code. It is built from people to people. Every system reflects the perspectives, assumptions, and blind spots of those who plan, design, implement, and test it. When teams lack diversity, these limitations can become embedded in products, processes, and policies. In the current AI era, where systems increasingly influence opportunities and everyday life, these risks are amplified. In this keynote, I invite you to reflect on how team composition shapes software engineering practice: how software problems are framed, how risks in software projects are recognized, and how technical decisions are justified. Together, we will navigate concrete cases from software development contexts in which diverse teams identified potential harms, questioned taken for granted assumptions, and redirected software projects away from discriminatory or exclusionary outcomes. From these cases emerges a different understanding of diversity in software engineering. Diversity is not only about representation. It is about expanding collective reasoning within software teams and strengthening our capacity to anticipate harm before it becomes embedded in software systems.

Bio: Ronnie de Souza Santos is a Tenure Track Assistant Professor of Software Engineering at the University of Calgary, Canada, and Director of the PLURISE Lab. His research investigates socio-technical dimensions of software engineering, with particular attention to fairness in AI systems, human aspects of software development, and equity, diversity, and inclusion in technology. His work appears in journals such as ACM Transactions on Software Engineering and Methodology, ACM Computing Surveys, and IEEE Software, as well as international conferences including ICSE, FSE, ASE, ICSME, ESEM, and CHASE. His contributions have been recognized with a Best Paper Award at CHASE, multiple Distinguished Reviewer Awards at ICSE, CHASE, EASE, and CSEE&T, and the SSE Excellence Award for fostering equity, diversity, inclusion, and accessibility in engineering at the University of Calgary. More information here

About the Organizers

Claudia Maria Cutrupi is a PhD Candidate at the Department of Computer Science of the Norwegian University of Science and Technology (NTNU) conducting her doctoral research on Gender Diversity and Software Engineering, specifically on the experiences of women in the software engineering pipeline and on designing impactful intervention in the field. Send email

Javier Gomez is Associate Professor in the Department of Computer Engineering of the Universidad Autonoma de Madrid (UAM), Madrid, Spain researching technologies to support people with special needs. Send email

Alicia Julia Wilson Takaoka is a postdoctoral researcher at Erasmus University Rotterdam focusing on AI, data, and digitalization for an inclusive and fair energy transition. Send email

 

Past Workshops

Accepted Papers and Abstracts of the Inagural Workshop

A Preliminary Framework for Intersectionality in ML Pipelines Machine learning (ML) has become a go-to solution for improving how we use, experience, and interact with technology (and the world around us). Unfortunately, studies have shown time and again that machine learning technologies may not be providing adequate support for the range of societal identities and experiences. Intersectionality is a sociological framework that provides a mechanism for the explicit consideration of complex social identities, with a focus on social justice and power. While the framework of intersectionality can support the development of technologies that acknowledge and support all members of society, over the years it has been adopted and adapted in ways that are not always true to its foundations, thereby weakening its potential for impact. To support the appropriate adoption and use of intersectionality for more equitable technological outcomes, we conducted a critical analysis of three existing efforts to incorporate intersectionality into machine learning methodologies. We report on the alignments and misalignments we discovered and how future efforts can properly make use of this socially-relevant framework in the development of their machine learning solutions. https://dl.acm.org/doi/abs/10.1145/3696630.3728692

AI for Empowering Women in AI Women remain significantly underrepresented in computing and artificial intelligence (AI), facing barriers such as limited access to tools, training, and opportunities. As AI becomes increasingly integral to daily life, it has the potential to address these disparities and foster greater inclusion. Realizing this potential requires fair access, inclusive design, and strategies that actively promote confidence, participation, and representation. This study explores these dimensions, identifying pathways for AI to serve as a catalyst for change. The results show that there is still scarce research explicitly connected to AI artifacts designed for empowerment, but there is a growing recognition of its importance. The SLR also reveals various challenges and success factors related to the role of AI in fostering gender inclusivity and empowerment, which calls for further attention. As further work, the authors will conduct empirical research to validate the findings and gather new insights, while co-designing an artifact to empower women in AI. https://dl.acm.org/doi/abs/10.1145/3696630.3728693

Rethinking Entry Requirements for Gender Diversity in CS Education: A Case Study of Student Performance in an Introductory Programming Course The gender distribution in computer science (CS) education remains uneven, with a persistent underrepresentation of women. One challenge in improving diversity is that many women choose non-technical tracks in high school, and therefore often lack the required mathematics qualifications for STEM educations. The purpose of this study was to investigate gender differences in performance and challenges in an introductory programming course and examining the impact of prior experience. The course is part of a CS program with a balanced gender ratio and relatively low mathematical prerequisites compared to similar programs in Sweden, allowing students with backgrounds in social sciences, economics, or humanities to apply. Bringing in students from diverse academic backgrounds may offer valuable perspectives and contribute to a broader understanding of computer science. The study was based on observations, a survey, and longitudinal statistics of student performance. Our analysis showed that women and men passed the course at equal rates, and struggled with similar learning barriers, regardless of their prior knowledge in mathematics. However, prior experience in programming may have played a role in grade differences, with men tending to achieve higher average grades. These findings raise interesting questions about whether lowering entry requirements, particularly in mathematics, could be a viable approach to improving gender balance in computer science education. https://dl.acm.org/doi/abs/10.1145/3696630.3728694

The Tech DEI Backlash - The Changing Landscape of Diversity, Equity, and Inclusion in Software Engineering Not long ago, Diversity, Equity, and Inclusion (DEI) initiatives were a top priority for leading software companies. However, in a short period, a wave of backlash has led many firms to re-assess their DEI strategies. Responding to this DEI backlash is crucial in academic research, especially because, currently, little scholarly research has been done on it. In this paper, therefore, we have set forth the following research question (RQ): "How have leading software companies changed their DEI strategies in recent years?" Given the novelty of the RQ and, consequently, the lack of scholarly research on it, we are conducting a grey literature study, examining the current state of DEI initiatives in 10 leading software companies. Based on our analysis, we have classified companies into categories based on their shift in commitment to DEI. We can identify that companies are indeed responding to the backlash by rethinking their strategy, either by reducing, increasing, or renaming their DEI initiatives. In contrast, some companies keep on with their DEI strategy, at least so far, despite the challenging political climate. To illustrate these changes, we introduce the DEI Universe Map, a visual representation of software industry trends in DEI commitment and actions. https://dl.acm.org/doi/abs/10.1145/3696630.3728695

The Impact of Team Diversity in Agile Development Education Software Engineering is mostly a male-dominated sector, where gender diversity is a key feature for improving equality of opportunities, productivity, and innovation. Other diversity aspects, including but not limited to nationality and ethnicity, are often understudied. In this work we aim to assess the impact of team diversity, focusing mainly on gender and nationality, in the context of an agile software development project-based course. We analyzed 51 teams over three academic years, measuring three different Diversity indexes -- regarding Gender, Nationality and their co-presence -- to examine how different aspects of diversity impact the quality of team project outcomes. Statistical analysis revealed a moderate, statistically significant correlation between gender diversity and project success, aligning with existing literature. Diversity in nationality showed a negative but negligible effect on project results, indicating that promoting these aspects does not harm students' performance. Analyzing their co-presence within a team, gender and nationality combined had a negative impact, likely due to increased communication barriers and differing cultural norms. This study underscores the importance of considering multiple diversity dimensions and their interactions in educational settings. Our findings, overall, show that promoting diversity in teams does not negatively impact their performance and achievement of educational goals. https://dl.acm.org/doi/abs/10.1145/3696630.3728696