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Marzyeh Ghassemi | Applying Machine Learning to Understand and Improve Health


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Podcast: Women in Data Science
Episode: Marzyeh Ghassemi | Applying Machine Learning to Understand and Improve Health
Episode pub date: 2019-08-28

Ghassemi explains how she is tackling two issues: eradicating bias in healthcare data and models, and understanding what it means to be healthy across different populations during her conversation with Women in Data Science Co-Director Karen Matthys on the Women in Data Science podcast.

She says that there are built-in biases in data, access to care, treatments, and outcomes. If we train models on data that is biased, it will operationalize those biases. Her goal is to recognize and eliminate those biases in the data and the models. For example, research shows that end-of-life care for minorities is significantly more aggressive. “This mistrust between patient and provider, which we can capture and model algorithmically, is predictive of who gets this aggressive end-of-life care.”

Ghassemi is also interested in the fundamental question of what it means to be healthy, and whether that rule generalizes. It requires a different mode for data collection and analysis. She explains that the typical process is that data is generated when you go to the doctor because you are sick. However, what matters more than your infrequent doctor check-in is how you’re experiencing things day to day, the self-report. She sees a huge opportunity in combining doctor visit data, self-reported data and data from wearable devices that’s passively collected from people that consent to their behavioral data being used. We can use all of those different kinds of data modalities to understand what it means to be healthy for all kinds of people.

She also offers valuable insights from her career in data science as a woman, a minority and a mother. She is a visible minority because she chooses to wear a headscarf. “I became comfortable very early on with defending choices that I had made about my life. And that for me really was instrumental in the academic process. Because what is academia if not constant rejection?”

Ghassemi made the decision to become a mother while pursuing her PhD. “As a society we should recognize that having kids is not a career hit.” She felt she was able to have kids and be successful as a graduate student because there was a community around her that was supportive and recognized that having children would enrich her life and experience. She credits having a supportive mentor as being instrumental in making it all work, saying, “You have to choose the race that you can be successful at.”

She wants young women entering the field to know there is no one defined path. She says don’t worry about checking boxes. Choose things that you are very passionate about. Find a mentor who’s willing to invest in you, and the path you want to take. Surround yourself with good people. It’s not the project that makes you successful; it’s the people. If you can’t trust the people around you, and learn how to work together, you are going to fail. Having the right mentors and having the right people around you should always be your guiding star.

RELATED LINKS
Connect with Marzyeh Ghassemi on Twitter (@MarzyehGhassemi) and LinkedIn
Find out more about Marzyeh on her personal website
Read more about the University of Toronto Faculty of Medicine and Vector Institute
Interview with Marzyeh: Artificial Intelligence Could Improve Health Care for All — Unless it Doesn’t
Connect with Margot Gerritsen on Twitter (@margootjeg) and LinkedIn
Find out more about Margot on her Stanford Profile
Find out more about Margot on her personal website

The podcast and artwork embedded on this page are from Professor Margot Gerritsen, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.

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Jennifer Chayes | Eliminating Bias

Podcast: Women in Data Science
Episode: Jennifer Chayes | Eliminating Bias
Episode pub date: 2018-10-19

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 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 fieldof data science is changing and that the increase in underrepresented voices will be critical to the future of the field moving forward.

The podcast and artwork embedded on this page are from Professor Margot Gerritsen, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.