Written by Nathan Babcock:
Recently we blogged about the lack of gender diversity in STEM fields, noting the fact that this gap is problematic for potential employees but also for the accuracy and fairness of data algorithms. This blog entry continues that discussion with features on two more notable women in statistics and data science who discussed their work on the Women in Data Science (WiDS) podcast out of Stanford University, hosted by Professor Margot Gerritsen and Cindy Orozco Bohorquez.
Dr. Kristian Lum is a statistician and research assistant professor at the University of Pennsylvania who says that following her interests has led her on an ever-changing career path across business, public service, and academia. In a talk on the Women in Data Science podcast entitled “Applying Statistics to Promote Fairness and Transparency”, she discussed her particular interest in algorithmic fairness. She has spent time at Duke University, University of Pennsylvania, Virginia Polytechnic Institute and State University and the Human Rights Data Analysis Group (HRDAG). As HRDAG’s lead statistician, she spent a lot of time working with and analyzing algorithmic bias in predictive policing. “Predictive policing” uses computer systems to analyze large sets of data, including historical crime data, to help decide where to deploy police or to identify individuals who are purportedly more likely to commit or be a victim of a crime” (Brennan Center). Dr. Lum’s work showed that predictive policing models can perpetuate the history of over-policing in certain areas and uphold racial bias in minority communities. Her work is an important contribution to recognizing bias in data use and in the presence of structural inequalities.
Another prominent woman in the data science field is Dr. Newsha Ajami, who works primarily in academia. Dr. Ajami is currently the director of Urban Water Policy with Stanford University’s Water in the West , a group focused on creating and promoting proper solutions to sustainable water problems in the western United States. By trade, Dr. Ajami is a hydrologist working on sustainable water resource management, water policy, and urban water strategy. She uses data science to improve the management behind water demand by increasing the understanding of how and why customers change the way they use water within utility companies. During her episode of the WiDS podcast, Dr. Ajami highlighted the importance of a multidisciplinary approach to working with data and emphasized the importance of this in research. Her work utilizes data science, engineering, public policy, behavioral economics, and more. Her work to improve urban water systems relies on the understanding of statistical models and data optimization, but it would be rendered meaningless without the ability to understand how water systems operate and the policy components to solving our problems. Ajami’s ability to work across this diverse range of fields is something she prides herself on. Approaching problems through a multidisciplinary lens is foundational to Quantitative Methods and a pillar of the QMSS program at Michigan. Dr. Ajami’s work offers a great example of the value of an interdisciplinary focus.
These two women who work with statistics, data and data science reinforce the importance of women being represented in this important and influential field of work! Keep listening to the Women in Data Science podcast for more inspiration and information from notable women in the field!
Sources:
- “Wids Podcast”. Women In Data Science (Wids), 2022, https://www.widsconference.org/podcast.html. Accessed 16 Mar 2022.
- Tamy Guberek, Daniel Guzmán, Megan Price, Kristian Lum and Patrick Ball. 2010. “To Count the Uncounted: An Estimation of Lethal Violence in Casanare,” A Report by the Benetech Human Rights Program. 10 February. (Available in Spanish) © 2010 Benetech. Creative Commons BY-NC-SA.