Intersectionality and Software Engineering Workshop

About the Workshop

This inagural workshop focusing on intersectionality and software engineering, both in education and in practice, will be held in Trondheim, Norway on June 27th as a part of the 2025 Foundations of Software Engineering conference.

All of the program committee members are part of the COST Action CA19122 EUGAIN.

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 inaugural 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 “Intersectionality, diversity, and inclusion in software engineering.” It offers an opportunity to focus scholarly attention on the social, cultural, political, and economic examination and shaping of software engineering practices, policies, and their consequences. 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 and theoretical work, as well as richly described practice cases and demonstrations. The topics of interest include, but are not limited to:

• Best practices for promoting intersectionality, diversity, and inclusion
• Digital equity and digital divide
• Intersectional issues in generative AI
• Feminist methodologies in empirical software engineering
• Inclusive and identity-aware technology design, implementation, and use
• Software engineering policy interventions to promote equity and justice
• Tool development and testing for diversity, equity, and inclusion
• Implications and socio-political influence of ICTs
• Re-conceptualizing SE concepts, methods, frameworks, and theories through a critical lens

Author Guidelines

• Research Track: (max 10 pages excluding references) Papers describing original research, including novel approaches, tools, datasets, and studies related to the workshop topics.
• Industry Track: (max 4 pages excluding references) Papers that discuss industry challenges and lessons learned from practice.

• Workshop papers submission: Tuesday Feb 25th, 2025
• Workshop papers notification Tuesday Mar 25th, 2025
• Workshop papers camera-ready (hard) Thursday Apr 24th, 2025

Workshop Schedule

Below is the schedule of the workshop:

Accepted Papers and Abstracts

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.

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. A key finding is the lack of tools explicitly designed to support women’s participation in AI. Beyond reducing bias, fostering diversity in AI development requires designing artifacts that actively promote knowledge, skills, and confidence. Empowerment in this context refers to equipping individuals to engage with and influence AI, whether as users or developers. Women’s participation in AI can thus be understood as a cyclical process, where increased engagement leads to further inclusion and representation in the field. The research objective of this study is to provide information on the relation between empowerment and women in AI, using the Systematic Literature Review (SLR) methodology. The SLR proposes and analyzes 14 studies that serve as a basis to understand the relationship between women, empowerment, and AI. 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.

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.

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.

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.