Creating a Biased-Free Job Board with Matthew Thomas
09/15/22 • 28 min
This bonus episode features Matthew Thomas, a data scientist at Inclusively and a graduate of the UVA M.S. in Data Science program. He talks about how Inclusively works to create and maintain a job board designed specifically for job seekers with disabilities. Matthew explains how typical job boards come with many built-in biases that can screen out qualified individuals without them even knowing. He discusses the challenges of removing biases from algorithms and the importance of honesty and self-criticism when examining a data science project.
As Cathy O’Neil challenged in Episode 1 of UVA Data Points, we should always ask ourselves, “For whom does this fail?” Matthew’s work is a good illustration of this sentiment in practice. In addition to discussing his work, Matthew also gives solid career advice for anyone seeking a similar career path in data science.
This bonus episode features Matthew Thomas, a data scientist at Inclusively and a graduate of the UVA M.S. in Data Science program. He talks about how Inclusively works to create and maintain a job board designed specifically for job seekers with disabilities. Matthew explains how typical job boards come with many built-in biases that can screen out qualified individuals without them even knowing. He discusses the challenges of removing biases from algorithms and the importance of honesty and self-criticism when examining a data science project.
As Cathy O’Neil challenged in Episode 1 of UVA Data Points, we should always ask ourselves, “For whom does this fail?” Matthew’s work is a good illustration of this sentiment in practice. In addition to discussing his work, Matthew also gives solid career advice for anyone seeking a similar career path in data science.
Previous Episode
Ethical Data Science with Cathy O'Neil
UVA Data Points sits down with Cathy O'Neil, author of Weapons of Math Destruction, and Brian Wright, Assistant Professor of Data Science at the University of Virginia. The candid dialogue ranges from O'Neil's new book The Shame Machine to her work as an algorithm audit consultant. The two also draw comparisons between data science problems and knitting, as well as discuss educating future data scientists.
Links:
https://mathbabe.org (Cathy O'Neil's website)
https://datascience.virginia.edu (UVA School of Data Science website)
Books mentioned:
Next Episode
Exploring the Popol Vuh with Allison Bigelow and Raf Alvardo
Rafael Alvarado and Allison Bigelow discuss the Multepal project, which focuses on decolonization and the digital humanities through encoding the Popol Vuh, the Mayan book of creation, and other indigenous texts. Their research connects the humanities to data science by framing digitization and data design as interpretive acts that can have far reaching consequences for our understanding of history and society.
Multepal Links:
Digital Edition of the Popol Vuh: https://multepal.github.io/app-aanalte/xom-all-flat-mod-pnums-lbids.html
Multepal Project: https://multepal.spanitalport.virginia.edu/
Multepal GitHub: https://github.com/Multepal
Books Mentioned:
Mining Language by Allison Bigelow
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