Kellogg Insight’s Post

How do #stereotypes lurk in our language? Scholarly investigations of bias have expanded to examine not just a single sociodemographic group on its own, but the interaction of several, intersecting groups together (e.g., defined by gender and race simultaneously). “It’s important to understand how to study this intersectionality at scale,” suggests Kellogg assistant professor of management and organizations Tessa Charlesworth. And a new tool co-developed by Charlesworth can do just this. Using the novel Flexible Intersectional Stereotype Extraction procedure, or FISE, Charlesworth analyzed 840 billion words of text to unearth biases surrounding different intersectional groupings of people. Initial analysis found that historically powerful groups dominate in terms of how frequently they are discussed. And this research can be helpful amidst the rise of generative AI. Charlesworth suggests that “understanding the unique patterns of intersectional biases in #AI and LLMs will be necessary to make more fair technologies for the future.” Read more about Charlesworth’s work, and how #biases change over time, at Kellogg Insight. https://kell.gg/4l8i #KelloggLeader