Hi everyone. Welcome to the A6Z podcast. I'm Sonal and I'm here today with Michael and we are talking to

Kieran Snyder, who is a CEO and co-founder of Textio, a company that analyzes job listings

to predict how well they're going to perform and can help optimize them to get more qualified

diverse candidates. And interestingly, they've been able to figure out, besides what doesn't work

very well in job descriptions, words like synergize. They've been able to figure out what does work well.

Language like in tech, people love to talk about hard problems and tough challenges. But it's a lot

bigger than just about jobs. The ability to understand the words we use and how we use them is pretty important because even though we're completely immersed in a world of tech, where a lot of the conversation is around big data as numbers, a lot of the data that we produce, the output of our work is actually taking place in the form of words. And those words matter.

Sometimes how you say things is more influential than what you're actually saying, right?

And it's counterintuitive to any of us who've built products before because you like to think

you're leading with a strong vision. Clearly words matter. And another place that that plays out

is with hidden biases that are often revealed in words. For example, Kieran examined a number of resumes to see the differences between how women and men describes themselves as well as in performance reviews to see the ways that women and men were described differently.

The word abrasive, which has been talked about

since then, ended up, you know, being used in 17 out of a couple hundred women's reviews and

zero times in men's reviews, right? The sort of stereotypical, like aggressive was used in a man's

review with an exhortation to be more of it,

and in women's reviews as a term of some judgment. Okay, let's get started. Kieran, welcome.

So the reason we actually invited you to the ACNC podcast today is because you've been writing a lot

of interesting work based on the outcomes of your product where you've been analyzing

people's use of language

in certain contexts as a way to surface insights. And I think that's really fascinating because I think

we have a tendency in our world to focus on big data as if it's just numbers and not other forms

of data because you're really describing. I mean, what you describe your work is doing is applying

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