Start-Ups Use Technology to Redesign the Hiring Process - New York Times

MS. GLAZEBROOK We ran a large-scale experiment last year to test whether we could beat a traditional résumé-sifting process, and found that on every measure, we were driving better outcomes. Indeed, over half of the people that were hired wouldn’t have been were it not for the platform.

Are you concerned about algorithmic bias?

MS. GLAZEBROOK The risk of algorithmic bias is real: The data you use to train the algorithm is critical, and naïveté in design can be disastrous. If you train a computer to optimize outcomes using biased past decisions, it’ll do a fantastic job of replicating that bias.

MS. POLLI I’m also very concerned about algorithmic bias because A.I. is so powerful that, unless checked, it can magnify the bias in hiring, and very few technologies test to see if their tools have bias. However, Pymetrics does. Algorithms can be fairer than people, which, although true, can be a tough sell!

What is next in the advancement of women?

MS. GLAZEBROOK Going beyond awareness and caring about problems of biased assessments to working on practical, day-to-day solutions that promote the right behaviors and make fairness the norm. I’m hugely optimistic about the possibilities of more data-driven decision-making. The steady stream of data scientists moving into H.R. functions is a sign that people are starting to see the value in better measuring and experimenting on talent management.

MS. POLLI Using technology in the work force to create a truly “blind audition” that will hire and promote based on ability, not gender. I’m also very optimistic about the promise of technology to assist the migration of women and men whose jobs are being lost to A.I. into the jobs of the future.

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