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Gender bias in education gained considerable relevance in the literature overthe years. However, while the problem of gender bias in education has beenwidely addressed from a student perspective, it is still not fully analysedfrom an academic point of view. In this work, we study the problem of genderbias in academic promotions (i.e., from Researcher to Associated Professor andfrom Associated to Full Professor) in the informatics (INF) and softwareengineering (SE) Italian communities. In particular, we first conduct aliterature review to assess how the problem of gender bias in academia has beenaddressed so far. Next, we describe a process to collect and preprocess the INFand SE data needed to analyse gender bias in Italian academic promotions.Subsequently, we apply a formal bias metric to these data to assess the amountof bias and look at its variation over time. From the conducted analysis, weobserve how the SE community presents a higher bias in promotions to AssociateProfessors and a smaller bias in promotions to Full Professors compared to theoverall INF community.

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