Data-Driven Analysis of Gender Fairness in the Software Engineering Academic Landscape

• URL: http://arxiv.org/abs/2309.11239v1
• Date: Wed, 20 Sep 2023 12:04:56 GMT
• Title: Data-Driven Analysis of Gender Fairness in the Software Engineering Academic Landscape
• Authors: Giordano d'Aloisio, Andrea D'Angelo, Francesca Marzi, Diana Di Marco, Giovanni Stilo, and Antinisca Di Marco
• Abstract summary: We study the problem of gender bias in academic promotions in the informatics (INF) and software engineering (SE) Italian communities. We first conduct a literature review to assess how the problem of gender bias in academia has been addressed so far. Next, we describe a process to collect and preprocess the INF and SE data needed to analyse gender bias in Italian academic promotions. From the conducted analysis, we observe how the SE community presents a higher bias in promotions to Associate Professors and a smaller bias in promotions to Full Professors compared to the overall INF community.
• Score: 4.580653005421453
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Gender bias in education gained considerable relevance in the literature over the years. However, while the problem of gender bias in education has been widely addressed from a student perspective, it is still not fully analysed from an academic point of view. In this work, we study the problem of gender bias in academic promotions (i.e., from Researcher to Associated Professor and from Associated to Full Professor) in the informatics (INF) and software engineering (SE) Italian communities. In particular, we first conduct a literature review to assess how the problem of gender bias in academia has been addressed so far. Next, we describe a process to collect and preprocess the INF and SE data needed to analyse gender bias in Italian academic promotions. Subsequently, we apply a formal bias metric to these data to assess the amount of bias and look at its variation over time. From the conducted analysis, we observe how the SE community presents a higher bias in promotions to Associate Professors and a smaller bias in promotions to Full Professors compared to the overall INF community.

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