Yesterday, I provided data and storytelling training at the American Association of Blacks in Higher Education’s Fall Research Institute. This was my fifth year serving as a facilitator/trainer. In my first year, I taught participants how to access public databases from the National Science Foundation, the National Center for Education Statistics, and the U.S. Census Bureau. It was the first time I had a captive audience to explain the importance of disaggregating data and train them on how to do it.
The second year, the Strada Education Foundation hosted the Institute, and participants focused on using data to think about “belonging” on their campuses. The third year brought more diverse interests. One participant was interested in journalism degrees conferred after Trump’s first term as president. Last year, I was sick and couldn’t travel, but I offered remote training to a woman who had transitioned from K–12 administration to a tenure-track faculty position in an education department.
This year, I heard a participant from the first year present on getting her organization to disaggregate the data in its dashboard. She questioned whether thier stakeholders would be surprised by what those data would show.
My eyes got a bit sweaty as I listened to her.
A facilitator talked about the aversion to using race, diversity, equity, inclusion, and similar terms in discussions. I offered the following in response:
Lead with the outcome. Ask stakeholders, board members, or whoever you need to buy in what characteristics they believe influence that outcome. They may identify race as one of the many characteristics that influence it.
They tell you. You don’t tell them.
A participant in medical education asked whether AABHE tracked outcomes by nationality for Black participants. That is, are you disaggregating within the “Black/African American” racial category?
I did a little dance on the inside.
This year, I wanted participants to leave with a product, not just knowledge about how to pull the data, but a one-pager they could use to create a deck and graphics. So I added modules on how to leverage AI using Claude and Perplexity, plus language and framing.
I had to leave to catch my flight, so I didn’t get to see my group report out in person. But I got to watch them on WhatsApp.
I was in the airport jumping up and down, so proud.
Selective Accountability
That excitement sits alongside a conflict in how this administration approaches accountability for race-based opportunities. In August 2025, the Department of Education directed the National Center for Education Statistics to collect admissions data disaggregated by race and sex, including information about applicants, admitted students, and enrolled students. The Department of Education says its admissions data “should be enhanced to capture information that could indicate whether institutions of higher education are using race-based preferencing”. The premise is that preferring one group has consequences for another. seat given to one applicant is a seat denied to another.
That logic should not stop at the admissions office. Racial preferences, or discrimination against a group, in jobs, promotions, pay raises, and contracts may not be zero-sum, but they do not allow for the maximization of the economic participation of all workers.
Meanwhile, the Census Bureau has proposed removing race and ethnicity questions from the decennial census questionnaire. It is a proposal, not a final rule. It leaves those questions on the American Community Survey.
The conflict is not whether to use race data, but which questions this administration will allow the data to answer. The Department of Education and the Census Bureau cite the same Supreme Court decision, Students for Fair Admissions. The Census Bureau questions “the meaning, significance, and impact of attempting to categorize persons by race and ethnicity” and calls race an immaterial characteristic.
Do you see the conflict?
Either race categories are too unreliable to ask about, or they are reliable enough to prove discrimination. If race is an immaterial characteristic, why ask schools to report data by race to the Department of Education?
Because you cannot find a preference or discrimination without measuring who is in the pool, who is chosen, and who advances. That requires race.
Stay sharp and mind all preferences.
Rhonda V. Sharpe is the president and founder of the Women’s Institute for Science, Equity and Race. Her research focuses on gender and racial inequality, STEM diversity, and the demographics of higher education.