Stanford
Engineering

Jennifer Chayes: Fairness, transparency and ethics in data science

The managing director of Microsoft Research discusses her path to the company and the work her team is doing to remove bias from data and algorithms.
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​Bill Snyder
This article is part of the series: 
The Women in Data Science (WiDS) podcast
What can be done to identify, audit and amend biases in data and algorithms? | Illustration by Kevin Craft

What can be done to identify, audit and amend biases in data and algorithms? | Illustration by Kevin Craft

Attaining tenured status at a major university is often the culmination of an academic’s career; giving it up is unthinkable for most.

But after 10 years at UCLA, Jennifer Chayes was offered a job at Microsoft. The offer, she says, “scared me to death,” but she took the job and is now managing director for Microsoft Research in New England, New York and Montreal. “There are brass rings that come along, and they look really scary, but I believe that we should grab them,” Chayes says on this episode of the Women in Data Science podcast.

Chayes advocates for strong data ethics. She believes that data scientists have “the opportunity to build algorithms with fairness, accountability, transparency and ethics, or FATE.” FATE became the name of a group that formed in one of her labs. This group is devoted to auditing and recognizing bias in data and algorithms and also to developing tactics and strategies to remove existing biases.

You can listen to the Women in Data Science podcast on Apple Podcasts, via the Women in Data Science website or Stanford Engineering Magazine.

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