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Postdoctoral researcher will use AI to study implicit bias in middle school students, according to Adam Zewe, Communications Manager at Harvard School of Engineering and Applied Sciences.
Photo: Haewon Jeong
Even though progress has been made over the past decades, gender and racial disparities in STEM (science, technology, math, and engineering) fields continue to persist.
A 2021 Pew Research study found that only 9 percent and 8 percent of STEM jobs are held by Black and Hispanic workers, respectively. And while the study found that women hold 50 percent of all STEM jobs (including health-related jobs), the percentages are far lower for jobs in physical sciences (40 percent), computing (25 percent), and engineering (15 percent).
Could machine learning help researchers better understand the factors that contribute to those disparities? Or are machine-learning tools partly to blame for the gender and racial discrepancies in STEM? Haewon Jeong, a postdoctoral fellow in the lab of Flavio Calmon, Assistant Professor of Electrical Engineering at the Harvard John A. Paulson School of Engineering and Applied Sciences, is embarking on a research study to explore both questions...
While the algorithms may provide valuable insights, the risks the technology poses when applied to testing, grading, and class placement inspired Jeong to study the downsides of machine learning.
“Machine learning can be a double-edge sword,” she said. “If you just use machine learning without care, you can induce more bias.”
