Gendered behavior as a disadvantage in open source software development
Women’s disadvantage: because of who they are, or what they do?
Women often find themselves strongly disadvantaged in the field of software development, in particular when it comes to open source. In a study recently published in EPJ Data Science, Orsolya Vasarhelyi and Balazs Vedres argue that this disadvantage stems from gendered behavior rather than categorical discrimination: women are at a disadvantage because of what they do, rather than because of who they are.
In our recent article published in EPJ Data Science (1st August 2019) we analyzed a large dataset of open source software developers to answer the question: are women at a disadvantage because of who they are, or because of what they do? Using data on entire careers of users from GitHub.com, we developed a model to capture the gendered pattern of behavior: the probability of being female given behavior.
Our measure takes into account variables that the individual can control in principle, such as professional specialization (e.g., data science or front-end development) or the gender of followed individuals—but we do not take into account who follows that person. We are, of course, acutely aware that women may not freely choose their activities, but can rather be channelled to expected, socially acceptable (that is, often male majority acceptable) specializations.
We found that gendered behavior is a significant source of disadvantage in open source software development. Our models show negative effect for female-typical behavior, and only weak support for categorical discrimination: about 85% of women’s disadvantage in success comes from what they do, and only 15% of their disadvantage stems from who they are.
An important finding is that the disadvantage stemming from female-typical behavior is not exclusive to women: men are hurt just the same, should they choose specialties and behaviors typical of women. What is more, we found that users with unidentifiable gender are just as disadvantaged along this dimension. Hiding your gender on an online platform does not help, as disadvantage comes overwhelmingly from the gendered nature of activities.
Our results have important consequences for policy and interventions in gender inequalities in STEM and possibly in other fields. In the short term, attempts to set higher quotas for women will not address the component of inequality that is related to gendered behavior. We can only hope that increased proportion of women eventually will erase striking inequalities associated with gender typical behavior. A higher proportion of women can lead to questioning stereotypes. We would see female success stories in conventionally male types of behavior, and eventually firms and communities would learn to remove associations of masculinity–femininity from professional specializations.
As the use of AI systems in human resources management advances, the high salience of gendered behavior in disadvantage means an increased risk of algorithmic discrimination. Algorithms can be policed to exclude manifest gender information from their decision making, but they can perpetuate discrimination based on behavioral typicality. It will be difficult to hold such algorithms accountable, as the particular behavioral specializations that drive inequality can be shifting constantly. Today, activists target the front-end/back-end dichotomy at Google, but tomorrow they might need to target D3 and Hadoop.
Orsolya Vasarhelyi spent 6 years as a data analyst at various startups, taking part in network science, data mining and NLP projects. Her main interest is to transfer academic findings into everyday business solutions. She writes articles about startups, data analytics and traveling. She is an active member and ambassador of the Bridge Budapest Association, organizer of Django Girls Budapest and mentor at the KONNEKT Organization.
Balazs Vedres’s research furthers the agenda of developing network science with social theoretical insight. His recent research follows entrepreneurs, video game developers, jazz musicians, programmers, and graphic designers as they weave collaborative networks through their projects and recording sessions, analyzing questions of the sources of creativity, gender inequality, and the historical sustainability of innovation systems.