Start Date
11-12-2016 12:00 AM
Description
Increasingly, information generated by open collaboration communities is being trusted and used by individuals to make decisions and carry out work tasks. Little is known about the quality of this information or the bias it may contain. In this study we address the question: How is gender bias embedded in information about organizational leaders in an open collaboration community? To answer this question, we use the bias framework developed by Miranda and colleagues (2016) to study bias stemming from structural constraints and content restrictions in the open collaboration community Wikipedia. Comparison of Wikipedia profiles of Fortune 1000 CEOs reveals that selection, source, and influence bias stemming from structural constraints on Wikipedia advantage women and disadvantage men. This finding suggests that information developed by open collaboration communities may contain unexpected forms of bias.
Recommended Citation
Young, Amber; Wigdor, Ari D.; and Kane, Gerald, "It’s Not What You Think: Gender Bias in Information about Fortune 1000 CEOs on Wikipedia" (2016). ICIS 2016 Proceedings. 15.
https://aisel.aisnet.org/icis2016/SocialMedia/Presentations/15
It’s Not What You Think: Gender Bias in Information about Fortune 1000 CEOs on Wikipedia
Increasingly, information generated by open collaboration communities is being trusted and used by individuals to make decisions and carry out work tasks. Little is known about the quality of this information or the bias it may contain. In this study we address the question: How is gender bias embedded in information about organizational leaders in an open collaboration community? To answer this question, we use the bias framework developed by Miranda and colleagues (2016) to study bias stemming from structural constraints and content restrictions in the open collaboration community Wikipedia. Comparison of Wikipedia profiles of Fortune 1000 CEOs reveals that selection, source, and influence bias stemming from structural constraints on Wikipedia advantage women and disadvantage men. This finding suggests that information developed by open collaboration communities may contain unexpected forms of bias.