Eliminating Bias

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 became the managing director for Microsoft Research in New England, New York and Montreal.

“There are brass rings that come along, and they always come along at the most inopportune times, and they look really scary, but I believe that we should grab them when they come along,” Chayes says during a conversation with Stanford’s Margot Gerritsen, Stanford professor and host of the Women in Data Science podcast. Chayes is a big advocate of eliminating biases in search algorithms and believes that data scientists have “the opportunity to build algorithms with fairness, accountability, transparency and ethics, or FATE.” FATE, a group that formed at one of Chayes’ labs, works to address inequity in the field.

In one particular instance, the group discovered that certain searches yielded certain results. Searches looking for computer programmers, for example, typically returned results for people with male names. The change Chayes’ team implemented in the search algorithm removed that built-in bias. Removing bias from hiring is not only fair, it results in better outcomes, she says. “I think that you’re more likely to ask the right questions if you have been on the wrong side of outcomes. So you’re much more likely to see a lack of fairness or bias as a problem before it happens.” Chayes believes that the field of data science is changing and that the increase in underrepresented voices will be critical to the future of the field moving forward.