Recently, I wrote a CACM blog post (https://bit.ly/2RgNf5p) to abolish the widespread use of the label "underrepresented minority" (URM), which refers to the low participation rates of racial and ethnic groups in fields such as computing and STEM relative to their representation in the U.S. population. Blacks, African Americans, Hispanics, Latinx, Native Americans, and Alaskan Natives are most commonly defined as URMs. The harmful and racist label URM implies that the aforementioned racial and ethnic groups are unworthy, unprivileged, and inferior. Language influences how we feel, react, and understand. Thus, dismantling racism starts with analyzing the language we use—especially since it says much about how we see people.
In this conversation, we will discuss why the label URM is racist language and how its harmfulness hides in our everyday talk. Then, we will consider inclusive and anti-racist language for expressing underrepresentation. We will conclude by discussing ways to identify and abolish URM-like language rom our everyday talk.
Tiffani L. Williams is a Teaching Professor and Director of Onramp Programs at the University of Illinois at Urbana-Champaign. From 2017 to 2020, she was the Director of Computer Science Programs and Professor of the Practice at Northeastern University-Charlotte. From 2005 to 2017, she was a faculty member in the Department of Computer Science and Engineering at Texas A&M University.
Her awards and honors include a McKnight Doctoral Fellowship, an Alfred P. Sloan Postdoctoral Fellowship, an Edward, Frances, and Shirley Daniels Fellowship at the Radcliffe Institute for Advanced Study, a Denice Denton Emerging Leader ABIE award, and a PopTech Science Fellow award. Williams has been recognized for teaching excellence at Texas A&M with the Graduate Faculty Teaching Excellence award, Undergraduate Faculty Teaching Excellence award, and the Distinguished Award in Teaching by the Association of Former Students.
Hosted by: Nancy Amato