GHC16 / What Are Tech Tools Doing That The Best Diversity Initiatives Aren't?

How can software help companies recruit and hire more diversely? Erica Joy Baker, Laura I. Gomez, Stephanie Lampkin, Liz Kofman, and Aline Lerner tackled this question on a panel at Grace Hopper this year. Most came from the perspective of creating the tools or working in tech, and one came as a social scientist studying the problem. It turns out that technology can do a lot, from removing biases to helping employees find good matches in prospective employers.

Here are my live notes from the session, edited slightly since I took them.

- we'd like to have the tech to kill the resume and allow for anonymous processes where everyone is evaluated the same
- what drives behaviour change? show candidates what's really going on in companies
- "I don't believe in unconscious bias training. I believe in results."

- compelling to see results of a fairer, more competitive process
- many challenges in academic research: one group, no change, another group, huge difference (why?); the more data we have, the more we can figure out what's really going on
- early feedback is that demystifying 'the pipeline' idea has been valuable

- technical interviewing it totally broken; interviewing as a process is as effective as putting names on a dartboard and throwing the dart (this is especially true of unstructured interviews, which have no correlation to success outcomes; structured interviews have a tiny amount of correlation)
- competency-based interviewing helps structure interviews as well as check later whether the interview ended up being a good predictor of future performance; issue is that managers don't know what competencies matter, so hand-holding in that regard is needed
- big companies need to have the same vocabulary and awareness of where the issues are

- want companies to dissect what makes a high performing employee, then capture that about a candidate; again, because traditional interviewing sourcing process is broken; need chances to capture data in soft skills, behaviour science, neuroscience...

- hiring processes are antiquated; why haven't we seen much innovation in this space?
- change is hard partly because those responsible for letting folks into the pipeline don't have the skills they're recruiting for; they have the wrong incentives

- anonymizing applicants: is this the same as that Wall Street Journal author's suggestion, which made many women and other feminists upset?
- even when we remove the name, there are other indicators; how you write can identify you, even when what you said doesn't change
- anonymization doesn't take away identity; it lets folks look at us differently

- audience question: should companies be aiming to improve diversity? how will anonymization help them identify those candidates?
- yes, there are companies actively sourcing; mixed evidence on blind identity (may not help companies that were already trying); have to understand the context of the companies, each of which are so complex
- not as simple as sourcing underrepresented groups; need efforts to improve the process and give resources to those that don't have them
- recognize that we do have biases, and give tools to interrupt them

- audience question: has bias affected how much funding your companies have received?
- bias toward funding previously successful founders, even though data shows this isn't a good indicator of success
- needs to be more examples of success because there's a lot of pattern matching going on
- Stephanie: need to set the example as a young gay black woman, farthest from an old white man as you can get
- having a personal brand ended up helping some of the panellists (though not deliberate); e.g doing a lot of writing on the broken hiring process and sharing data