Solving The Pipeline Problem
Here’s what resulted from that call for speakers: from the pool of applicants whom we chose for the December 3 conference program, more than half were women. From the pool of applicants whom we chose for the December 2 Ignite: Lean Startup program, more than a third were people of color. And we had to make some heartbreaking decisions to pass on a number of speakers just because we didn’t have enough time on stage for everyone. Taken together, this shows that when you have a broader group from which to draw, there’s a good distribution of speakers; not all—not even most—of the good speakers are white men. The common conference organizer’s argument that we don’t know any black people in tech or that women didn’t apply to speak just doesn’t hold up.
Did employing a transparent process make a difference? The August call for proposals described above was actually our second attempt to change the makeup of our applicant pool; the first attempt failed. In June, we posted our first call for speakers, asking people to nominate others they thought would be good speakers. We noted that we were particularly keen on learning about white women and people of color who might be great speakers but weren’t on our radar yet, but we didn’t say anything about what had happened in the past or how we were trying to change it. We received about 35 nominations. Although some were very good, just about 10% were women and almost none were people of color. Every piece of data we have been able to gather on conferences says that 10% is the standard rate at which women will apply or be nominated and that very few people of color will be among these pools.
Let’s dwell on this for a moment. By using principles of meritocratic selection—i.e., being explicit about our desire to find great speakers whom we didn’t know, and by being honest about the process we’d used in the past—we created an atmosphere in which a much broader range of strong speakers felt invited to participate.
It’s also worth mentioning that we knew we’d hit on something important not just because we got such different results in the second round, but also because applicants told us so. We got a lot of comments like this:
“I LAUGH when you say, ‘under-represented at a tech conference,’ because had you not presented such a compelling invitation, I would have never even dreamed of applying for a position of a speaker.”
“The main reason I’m applying is because I have a huge amount of respect and admiration for your efforts to reach out to a new circle of contributors. Anything I can do to support you, whether it’s speaking or just behind the scenes, is personally very worthwhile to me.”
“Thanks for opening this up to the non-famous. I think there are lots of great stories out there to be told.”
Blind review. It’s well-documented that when people know the gender or race of a job applicant or the main character in a story, they generally assess the person’s qualifications and performance more favorably if the person is male or white. Because we all have internal biases, everybody does it–including white women and people of color. We wanted to eliminate that bias as best we could. So we asked applicants to submit some written information, along with a video, and we made the first cut based on the write-ups, which we read without checking names or other identifying info.
Did using blind review make a difference? By reading the applications, we quickly eliminated people who weren’t a fit for the conference because their topic clearly wasn’t on point for our audience. That was a small group of people, but the distribution was broad. Beyond that, blind review didn’t make a big difference. Why? We asked for relatively little info in writing, both because we wanted to hear directly from speakers (not their PR people) and because video much better represents the medium we’re trying to assess (often, people who can write a nice description of their talk can’t deliver the presentation well, and vice versa). So video was much more important to us than writing, but we couldn’t assess it blind. It’s worth considering whether there’s more we can do with this tool in the future.
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