Men ask most of the questions at scientific conferences; we can choose to change that

"When women are 70% of a room, they still asked only about 40% of the questions," says Natalie Telis, then a graduate student at Stanford University. "At that rate an audience would need to be 80% or 90% women before question asking would be split evenly between men and women."

Telis was inspired to study Q&A participation after a conference she attended as an undergraduate. She asked one question, but every other question she heard that day was asked by a man. To determine whether the ratio of questions asked was representative of the gender makeup of the audience, Telis and co-author Emily Glassberg recorded data about who asked questions after presentations they attended at seven conferences.

When Telis shared some of their findings online during a conference in 2015, it immediately sparked conversation and a policy change: the first question during every Q&A would come from a trainee. The following year, the conversation had quieted, but the policy remained, providing a classic experimental setup.

At the 2017 meeting of the American Society of Human Genetics, data were collected through a crowdsourced approach. Any conference attendee could record data about who was asking questions after the presentations he or she attended. Recorders' data overlapped but didn't exactly match -- some recorders may have left mid-Q&A or missed a question while recording data for the previous one, for example. Telis and Glassberg had to reconstruct the series of questions and found a solution in the tools of computational biology.

"We thought, we're trying to figure out a sequence, and we have some broken up chunks of that sequence. That sounds exactly like sequence alignment," says Telis. "We ended up creating this modified approach where we would align evidence for a question by time."

Telis and Glassberg used both the program genderizeR and U.S. census data to infer the gender makeup of the audience and presenters. Although this work focuses on representation across a simple binary, the authors acknowledge that future work should analyze effects from self-reported gender identity, race, ethnicity, and other demographics.

Telis has already seen the positive impact of her work. "I had tons of people tell me or tweet at me that they asked their first questions after hearing about this work," she says. Their study may provide a framework for future research about representation in the scientific community. "Making a choice and then evaluating its contribution to change is a critical part of experiment design. I hope that this is the start of a longer trend of us asking questions about the genetics community we want to create and how we create it."