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Understanding Fairness in Software Engineering: Insights from Stack Exchange

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arxiv 2402.19038 v3 pith:UFIVKF33 submitted 2024-02-29 cs.SE

classification cs.SE
keywords fairnesssoftwarediscussionspractitionerspostsexchangeproblemsstack
verification ladder T0 review T1 audit T2 compute T3 formal
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Software practitioners discuss problems at work with peers, in-person and online. These discussions can be technical (e.g., how to fix a bug?) and social (e.g., how to assign work fairly?). While there is a growing body of knowledge exploring fairness problems and solutions in the human and social factors of software engineering, most focus has been on specific problems. This study provides fairness discussions by software practitioners on Stack Exchange sites. We present an exploratory study presenting the fairness experience of software practitioners and fairness expectations in software teams. We also want to identify the fairness aspects software practitioners talk about the most. For example, do they care more about fairness in income or how they are treated in the workplace? Our investigation of fairness discussions on eight Stack Exchange sites resulted in a list of 136 posts (28 questions and 108 answers) manually curated from 4,178 candidate posts. The study reveals that the majority of fairness discussions (24 posts) revolve around the topic of income suggesting that many software practitioners are highly interested in matters related to their pay and how it is fairly distributed. Further, we noted that while not discussed as often, discussions on fairness in recruitment tend to receive the highest number of views and scores. Interestingly, the study shows that unfairness experiences extend beyond the protected attributes. In this study, only 25 out of 136 posts mention protected attributes, with gender mainly being discussed.

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  1. From Expectation to Habit: Why Do Software Practitioners Adopt Fairness Toolkits?

    cs.SE 2024-12 conditional novelty 4.0 of 10

    A UTAUT2-based survey of 181 practitioners finds performance expectancy and habit drive fairness toolkit adoption.

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