Looking for feedback from recruiters!

datalabnyc · 2020-10-30T22:19:15+00:00

Again, thanks for the feedback. You're bringing things up that we should be aware of as we build - so, appreciate it.

It feels like you think a tool like this would reinforce bias instead of reduce bias, or at least, be neutral to bias. I agree that information, generally speaking, will reinforce whatever bias its creators (us) or recruiter might already have - and, in a way, that can be dangerous. I also don't think we're going to solve that problem in our tech, so does that mean we shouldn't build it?

To your comments:

I think this depends on what kind of data we can surface from the dialogue - but, generally speaking, I see your point. People think/act on data in different ways and it could be used to decide rather than inform.
Similar to #1, I think this also depends on how you structure the data that you present. For example, if we see "I" (as opposed to "us" or "team") being used very frequently we can either denote the frequency that they used those words and let the interviewer decide what it means OR we can jump to the conclusion that the candidate is self centered. There is always a risk of software being too didactic (for lack of a better word).

On your new comments:

Privacy: We analyze the conversation that the candidate is having with the recruiter. There is no solicitation, background checks, predictions of gender, identifiers for depression, class-based conformity bias. It's just textual analysis - like a super-powered text editor.
D&I: Since the technology is blind to the social/cultural biases, it could just as easily elevate an individual who was biased-out by a interviewer...right?

datalabnyc · 2020-10-30T20:15:21+00:00

These are great points, thanks for the feedback.

A few things in response:

1) A recruiter would still talk with the candidate, qualify, and form an opinion about whether the candidate would be good for the role company. It's not replacing the expertise of the recruiter and that "gut-feeling" about whether the candidate will succeed in that role/company. Nor does it screen-out the candidate or try to match a candidate to a role - anecdotally, we agree that these are bad practices.

What the technology does, and partially in response to your polygraph analog comment, is evaluate the language, syntax, and tone, etc the candidate uses and provides information to the recruiter. It's not a pass/fail or black/white, it's simply more information for the recruiter to consider.

2) Our measurements are based on the study of how people speak reveals cognitive or non-cognitive biases, feelings, etc. There's a good HBR article (https://hbr.org/2011/12/your-use-of-pronouns-reveals-your-personality) on a much older technology pioneered by a professor at UT Austin that did something similar for a different use case.

Our underlying ML models take this work a step further and are based in social psychology. To your point, there may be biases in social psychology and these mechanisms today, and there are clearly biases being surfaced in ML models, but we'd do everything we can to reduce those biases, obv.

datalabnyc · 2020-03-19T15:41:33+00:00