  Human activities can be seen as sequences of events, which are crucial to
understanding societies. Disproportional event distribution for different
demographic groups can manifest and amplify social stereotypes, and potentially
jeopardize the ability of members in some groups to pursue certain goals. In
this paper, we present the first event-centric study of gender biases in a
Wikipedia corpus. To facilitate the study, we curate a corpus of career and
personal life descriptions with demographic information consisting of 7,854
fragments from 10,412 celebrities. Then we detect events with a
state-of-the-art event detection model, calibrate the results using
strategically generated templates, and extract events that have asymmetric
associations with genders. Our study discovers that the Wikipedia pages tend to
intermingle personal life events with professional events for females but not
for males, which calls for the awareness of the Wikipedia community to
formalize guidelines and train the editors to mind the implicit biases that
contributors carry. Our work also lays the foundation for future works on
quantifying and discovering event biases at the corpus level.
