Written by Fred L. Pincus, emeritus professor of Sociology, UMBC.

Two big assumptions underlie President Donald Trump’s attack on diversity, equity and inclusion (DEI) policies. The first is that discrimination against people of color is a thing of the past. The second is that DEI policies and practices discriminate against white people – especially white men – in what’s sometimes called “reverse discrimination.”

I’m a sociologist who’s spent decades studying race and inequality, and when I read the documents and statements coming out of the Trump White House, these assumptions jump out at me again and again – usually implicitly, but always there.

The problem is that the evidence doesn’t back these assumptions up.

For one thing, if discrimination against white Americans were widespread, you might expect large numbers to report being treated unfairly. But polling data shows otherwise. A 2025 Pew survey found that 70% of white Americans think Black people face “some” or “a lot” of discrimination in general, and roughly two-thirds say the same of Asian and Hispanic people. Meanwhile, only 45% of white Americans believe that white people in general experience that degree of discrimination.

In other words, white Americans believe that people of color, as a group, face more discrimination than white people do. People of color agree – and so do Americans overall.

In a second national study, using data collected in 2023, Americans were asked if they had personally experienced discrimination within the past year. Thirty-eight percent of white people said they had, compared to 54% of Black Americans, 50% of Latinos and 42% of Asian Americans. In other words, white Americans are much less likely to say that they’ve been discriminated against than people of color.

The ‘hard’ numbers show persistent privilege

These statistics are sometimes called “soft” data because they reflect people’s perceptions rather than verified incidents. To broaden the picture, it’s worth looking at “hard” data on measures like income, education and employment outcomes. These indicators also suggest that white Americans as a group are advantaged relative to people of color.

For example, federal agencies have documented racial disparities in income for decades, with white Americans, as a group, generally outearning Black and Latino Americans. This is true even when you control for education. When the Census Bureau looked at median annual earnings for Americans between 25 and 64 with at least a bachelor’s degree, it found that Black Americans received only 81% of what comparably educated white Americans earned, while Latinos earned only 80%. Asian Americans, on the other hand, earned 119% of what white people earned.

These gaps persist even when you hold college major constant. In the highest-paying major, electrical engineering, Black Americans earned only 71% of what white people did, while Latinos earned just 73%. Asian Americans, in contrast, earned 104% of what white people earned. In the lowest-paid major, family and consumer sciences, African Americans earned 97% of what white people did, and Latinos earned 94%. Asian Americans earned 117% of what white people earned. The same general pattern of white income advantage existed in all majors with two exceptions: Black people earned more in elementary education and nursing.

Remember, this is comparing individuals with a bachelor’s degree or higher to people with the same college major. Again, white Americans are still advantaged in most career paths over Black Americans and Latinos.

Disparities persist in the job market

Unemployment data show similar patterns. The July 2025 figures for workers at all education levels show that Black people were 1.9 times more likely to be unemployed than white Americans. Latinos were 1.4 times more likely to be unemployed, and Asian Americans, 1.1 times.

This same white advantage still occurs when looking only at workers who have earned a bachelor’s degree or more. Black Americans who have earned bachelor’s degrees or higher were 1.3 times more likely to be unemployed than similarly educated white Americans as of 2021, the last year for which data is available. Latinos with college degrees were 1.4 times more likely to be unemployed than similar white Americans. The white advantage was even higher for those with only a high school degree or less. Unfortunately, data for Asian Americans weren’t available.

In another study, researchers sent 80,000 fake resumes in response to 10,000 job listings posted by 97 of the largest employers in the country. The credentials on the resumes were essentially the same, but the names signaled race: Some had Black-sounding names, like Lakisha or Leroy, while others had more “white-sounding” names like Todd or Allison. This method is known as an “audit study.”

This research, which was conducted between 2019 and 2021, found that employers were 9.5% more likely to contact the Todds and Allisons than the Lakishas and Leroys within 30 days of receiving a resume. Of the 28 audit studies that have been conducted since 1989, each one showed that applicants with Black- or Latino-sounding names were less likely to be contacted that those with white-sounding or racially neutral names.

Finally, a 2025 study analyzed 600,000 letters of recommendation for college-bound students who used the Common App form during the 2018-19 and 2019-20 academic years. Only students who applied to at least one selective college were included. The study found that letters for Black and Latino students were shorter and said less about their intellectual promise.

Similarly, letters in support of first-generation students – that is, whose parents hadn’t graduated from a four-year college, and who are disproportionately likely to be Black and Latino – had fewer sentences dedicated to their scientific, athletic and artistic abilities, or their overall academic potential.

These and other studies don’t provide evidence of massive anti-white discrimination. Although scattered cases of white people being discriminated against undoubtedly exist, the data suggest that white people are still advantaged relative to non-Asian people of color. White Americans may be less advantaged than they were, but they’re still advantaged.

While it’s true that many working-class white Americans are having a tough time in the current economy, it’s not because of their race. It’s because of their class. It’s because of automation and overseas outsourcing taking away good jobs. It’s because of high health care costs and cuts in the safety nets.

In other words, while many working-class white people are struggling now, there’s little evidence race is the problem.

This article is republished from The Conversation under a Creative Commons license. Read the original article and see more than 300 UMBC articles available in The Conversation.

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A glass knifefish darts back and forth in a short tube, its brain activity being recorded in real time. This small fish, alternating between swift bursts of sensing activity and slower, task-driven behaviors, is helping scientists understand how animals decide when to gather information about their environment versus act on it. A team of researchers is blending neuroscience, math, and engineering to decode these choices, with potential to guide robots in uncertain terrains or unlock secrets of the brain.

The team’s research has just been funded by the Collaborative Research in Computational Neuroscience (CRCNS) program—a joint initiative of the National Institutes of Health (NIH) and the National Science Foundation (NSF) that supports interdisciplinary research. Kathleen Hoffman, professor of mathematics and statistics, co-leads the grant.

The CRCNS program emphasizes collaborative efforts to advance understanding of nervous system functions through computational tools. With the lead investigator at Johns Hopkins University and additional collaborators at the New Jersey Institute of Technology (NJIT), and the University of Minnesota, the team for the newly funded project spans biology, engineering, mathematics, and computer science—a mix well-positioned to discover deeper insights into brain mechanisms.

‘Explore’ or ‘exploit’?

The new project builds on the same team’s prior research, published in 2023 in Nature Machine Intelligence, which revealed similar decision-making patterns across species, from amoebas to humans. In that work, the team analyzed the behavior of glass knifefish—weakly electric fish that navigate dark waters using self-generated electric fields—in experiments run by Noah Cowan, the lead investigator for the new grant. Then they compared their findings to the behavior of other species as described in the scientific literature, uncovering similar patterns in 11 species, including bats, mice, moths, and humans.

In the prior work, “We looked at velocity distributions, and we found that there were two modes of movement. We called them ‘explore’ and ‘exploit,’ but you could also describe them as ‘fast’ and ‘slow,’” Hoffman explains. During experiments in narrow tubes, the fish alternated between two modes: rapid, exploratory movements to sense their surroundings (“explore”) and slower, deliberate actions using the information they’d collected (“exploit”).

That research challenged robotics norms, showing that animals don’t constantly scan their environment, but rather burst into action when needed, a strategy the team showed is both more economical and more effective. The new project ramps up data collection—from 40 seconds per trial to 10 minutes—allowing the team to reveal subtler patterns, like burst lengths and correlations between the fish’s movement mode and its position in the tube.

Deciphering animal decisions

A primary goal is to uncover what prompts the mode switch. “How does it decide when to switch? And the hypothesis that we’re considering is that it’s based on some internal measure of uncertainty in the fish, meaning that if the fish isn’t sure if it’s inside the tube, it’s going to move so it can gather sensory information,” Hoffman says.

To test this, the team integrates several methods. At the University of Minnesota, engineers led by Andrew Lamperski will apply machine learning to map relationships between sensory inputs and behavioral outputs in the form of mathematical functions. Hoffman handles data analysis, starting with manual pattern-spotting before coding. 

“I can’t wait to get my hands on the data,” Hoffman says. She’ll start by simply printing out the velocity and position results and poring over them visually. “I don’t think there’s anything better than the human brain to see patterns, and mathematics is the study of patterns,” she adds. After observing what looks like a pattern, she’ll bounce her ideas off the rest of the team, and eventually “go write a program to automatically go through all the data and see if that pattern recurs.” 

A boon for the project comes from NJIT, where biologist Eric Fortune will record neural activity via electrodes inserted into the fish’s brains during the movement experiments—a technique unavailable in prior work. This will let the team compare brain signals with behavior in real time, and look for an underlying mechanism that drives the switch from “explore” to “exploit.”

A scientific ‘dream team’

This project’s power lies in its teamwork. Hoffman coordinates from UMBC, analyzing data from all the collaborators. Cowan oversees behavioral tests on fish without brain probes, which allows for more complex experimental setups. Fortune at NJIT is handling the neural recordings, while Lamperski at Minnesota focuses on machine learning models that reflect what the others are seeing in the lab.

“What I love about this project is that all the components are necessary to elucidate the mechanism,” Hoffman reflects. “Nobody could do this completely on their own.” 

“I’m excited to have this dream team of mathematicians, engineers, and neuroscientists to assemble behind this problem,” Cowan said. “My lab at Hopkins has struggled to make sense of these movements for over a decade. This new team puts us on a path to finally decode the neural mechanisms animals use to switch gears between gathering task information, on the one hand, and getting the task done, on the other.”

‘My favorite kind of science’

This research could eventually transform robotics.

“If you want to build a robot that is going to mimic the motion of animals that exhibit this explore/exploit pattern for incorporating sensory information, you have to know how the animals do it,” Hoffman says. “This grant is focused on figuring out what that mechanism is.”

A robot that mimics natural intermittent sensing might navigate uncertain spaces, like disaster zones, more efficiently than constant-scanning models. The shared explore-exploit pattern also suggests broader relevance for the research, potentially informing understanding of neurological disorders—though Hoffman stresses those possibilities are further down the road. 

The grant will also open doors for students: Hoffman plans to involve undergraduates in data visualization and analysis, offering hands-on experience in interdisciplinary research that demonstrates how together, diverse minds can unlock secrets of the brain—with ripple effects in tech and health.

“The one thing I’m really excited about in this grant is that it’s completely multidisciplinary,” Hoffman says. “Everybody has a different perspective that helps us understand what’s going on. This is my favorite kind of science.”