Laura Mather

The technology industry has long struggled to create inclusive corporate cultures.

Tech leaders would have us believe they are making great strides, but they aren’t. For example, Google’s latest diversity report shows just 20% of the technology organization is female. One of the main reasons companies struggle so much to address diversity is due to a major misunderstanding: People think the best way to address unconscious bias is to train it away.

And yet, decades of research shows it is virtually impossible to use
training to remove the unconscious bias permeating society. Silicon
Valley claims to be data driven. But these companies ignore the data that shows that even though they’ve deployed unconscious bias training, their diversity numbers have barely budged.

There is good news. Research has shown companies can impact hiring decisions in a way that makes for truly fair outcomes: change the process. Use identity-blind resume reviews. Conduct structured interviews in a way that only allows interviewers to score candidates on the skills and values that are relevant to a job and not on things like how much they ‘like’ someone.

What’s critical is to interact with hiring teams at the moment they make a decision, like when they are reviewing a resume or scoring a candidate’s interview responses.

This sounds hard. Fortunately, technology can do this for us. It can
remove identifying information from resumes. It can enforce scoring
guidelines for interviews that focus the hiring decisions only on what’s relevant.

It’s time companies stop relying on unconscious bias training and start doing things that have been proven to be successful – change the hiring process – and use technology to make it scalable.

Imagine the results a company can achieve when it hires the very best person for the job regardless of gender or race.

That’s not just good for business. That’s good for everyone.

With a Perspective, I’m Laura Mather.

Laura Mather is a Silicon Valley-based entrepreneur dedicated to removing unconscious bias from the hiring process.