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

TL;DR

This paper investigates gender bias in Italian software engineering academia, analyzing promotion data over time to quantify bias levels and compare them with the broader informatics community.

Contribution

It introduces a data collection and analysis process for assessing gender bias in academic promotions within Italian SE and INF communities, highlighting differences across career stages.

Findings

SE shows higher bias in promotions to Associate Professor

Bias is smaller in promotions to Full Professor in SE

SE community exhibits different bias patterns compared to INF

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.