Owning Your Career Paths: Storytelling to Engage Women in Computer Science
Kapitel i bok, 2023

Motivation & challenge: Computer Science suffers from a lack of diversity that gets perpetuated by the most dominant and
visible role models. The community is doing itself a disservice by upholding techno-solutionism, short-term efficiency, and
busyness as central values. Those models are created and consolidated over time through social and cultural interactions
that increase the perpetration of gender stereotypes. Exposing people to diverse types of role models and stories can
contribute to making them more aware of the complexity of reality and inspire them taking better informed decisionsmaking
on their career paths. Likewise, showing different role models to stakeholders in society and industry can contribute
to increase the workforce diversity in the profession of computing as well as to make a shift towards the consolidation
of different role models. This, in turn, may contribute to strengthen resilience and adequacy for solving issues related to
diversity, equality and inclusion in Computer Science and more importantly allowing women take the ownership of their
career path.
Goal: To encourage the dissemination, sharing and creation of stories that show diverse career pathways to address
gender stereotypes created by dominant stories in Computer Science. We tackle this issue by developing a framework for
storytelling around female scientists and professionals to show a diversity of possibilities for women in pursuing an academic
career based on the ownership of their pathways.
Method: We apply a qualitative approach to analyse stories collected using the auto-ethnography and use thematic
analysis to unpack the components of what in these stories contribute to building the academic path in the field of Computer
Science. Authors used their own professional histories and experiences as input. They highlighted the central values of
their research visions and approaches to life and emphasised how they have helped to take decisions that shaped their
professional paths.
Results: We present a framework made of the nine macro-themes emerging from the autoethnography analysis and two
dimensions that we pick from the literature (interactions and practices). The framework aims to be a reflecting storytelling
tool that could support women in Computer Sciences to create their own paths. Specifically, the framework addresses
issues related to communication, dissemination to the public, community engagement, education, and outreach to increase
the diversity within Computer Science, AI and STEM in general.
Impact: The framework can help building narratives to showcase the variety of values supported by Computer Science.
These stories have the power of showing the diversity of people as well as highlighting the uniqueness of their research
visions in contributing to transformation of our global society into a supportive, inclusive and equitable community. Our
work aims to support practitioners who design outreach activities for increasing diversity and inclusion, and will help other
stakeholders to reflect on their own reality, values and priorities. Additionally, the outcomes are useful for those who are
working in improving the gender gap in Computer Science in academia and industry. Finally, they are meant for women
who are willing to proceed into an academic career in this area by offering a spur for reflection and concrete actions that
could support them in their path from PhD to professorship.

Career Paths: Storytelling to Engage Women in Computer Science


Elisa Rubegni

Lancaster University

Birgit Penzenstadler

Chalmers, Data- och informationsteknik, Interaktionsdesign och Software Engineering

Monica Landoni

Scuola Universitaria Professionale della Svizzera Italiana (SUPSI)

Letizia Jaccheri

Norges teknisk-naturvitenskapelige universitet

Gordana Dodig Crnkovic

Chalmers, Data- och informationsteknik, Interaktionsdesign och Software Engineering

Gender in AI and Robotics: The Gender Challenges from an Interdisciplinary Perspective




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