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WHAT I GOT WRONG ABOUT D.E.I.  
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Eugenia Cheng
September 5, 2025
The New York Times
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_ Math teaches us that D.E.I. initiatives should be about carefully
defining metrics we use to measure how far people have come, thus how
far they have the potential to go. They should be about uncovering
when some people are constantly running uphill _

Illustration: enigmatriz / New York Times,

 

As a woman in the male-dominated field of mathematics, I once opposed
targeted efforts to help women succeed — what we now call diversity,
equity and inclusion initiatives, which are currently facing fierce
backlash. I wanted to be judged on the merit of my mathematics alone.

When I was admitted to the University of Cambridge as an undergraduate
in math in 1994, I felt that I was a part of a clear minority. I
struggled to keep up with some of the men in my class, many of whom
had gone to elite boys’ schools where they had intense preparation.
Yet I would progress to a Ph.D. and a career as a research
mathematician.

As my career has advanced, what I’ve learned is that D.E.I.
initiatives helped others see value in my abilities and experience
that would have been missed otherwise. And it was through the lens of
math that I came to understand this.

Math is not just a way of calculating numerical answers; it is a way
of thinking, using clear definitions for concepts and rigorous logic
to organize our thoughts and back up our assertions. Numbers can tell
us about representation, but they often don’t tell the full story.
The percentage of female math graduates in the United States has
improved to around 42 percent
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than 18 percent of university professors in mathematics are women. A
50-50 gender split might seem like equality, but not if it was
achieved by lowering standards to let more women in. We need to be
more careful than that. The nuance found in mathematics can show us a
clearer understanding of how to think about equality.

Math is famous for its equations, but equations are more subtle than
they first appear. A simple equation like 4 + 1 = 1 + 4 shows not just
that two values are equal but also that there are two subtly different
ways of adding the same numbers to produce the same result. A similar
approach applies to more advanced and complicated forms of math, such
as the study of shapes or paths through space. We make choices about
how to determine equality.

This is relevant to how we evaluate what people have achieved and make
predictions about how well they will do. We can get some insight into
how we should make these evaluations from a mathematical field called
metric spaces.

A metric is a way of measuring the distance between two points but not
necessarily physical distance; it could be how much time it takes with
traffic as a factor or how much energy will be expended, depending on
whether you’re going uphill or downhill. A distance cannot be
measured on the basis of the position of a single point. It requires
the effort of measuring the distance between two points. This may
sound redundant, but it’s an important clarification: Metrics can be
measured only by taking into account the starting point and ending
point, as well as relevant features of the journey — the whole
story.

When we evaluate people, we could do the same. Instead of just looking
at what they have achieved, we could also look at where they started
and be clearer about how we are measuring the metaphorical distance
they have come and whether we are taking into account the support they
had or the obstructions they faced.

If we are selecting sprinters for a track team, we might look at their
best times for the 100-meter dash. But if someone had, for some
reason, only ever run races uphill or against the wind, it would make
sense to take that into account and not compare that runner’s times
to others’ directly. We would be treating those people differently
but only because their paths were different; really we’d be
evaluating their paths fairly relative to their contexts.

Other forms of achievement are not as straightforward to measure, but
the idea is analogous. If someone achieved a certain SAT score after
months of tutoring and someone else earned the same score having never
seen an SAT before, it would be reasonable to be more impressed with
the latter result and think that the second test taker has more
potential. We should think of D.E.I. efforts as the best versions of
this and aim to design systems that can measure the fuller picture of
someone’s professional journey, not just the current result.

It took me a long time to realize that when I began my career, I had
probably worked much harder than I might have if I had had a different
identity. I had to work against people telling me I would never be
able to succeed. When I attended conferences, I dealt with
inappropriate behavior from men senior to me. I had to find my way in
my career having no mentors who looked at all like me. I am grateful
for the support of some senior mathematicians, and I now realize that
it wasn’t extra help because I was a woman; it was help in
overcoming the extra obstructions I faced as a woman.

It shouldn’t be called sexist to help people overcome sexism, and it
shouldn’t be called racist to help people overcome racism, but if we
give this help too crudely, then we leave ourselves open to these
criticisms. Math teaches us that D.E.I. initiatives should be about
carefully defining the metrics we use to measure how far people have
come and thus how far they have the potential to go. They should be
about uncovering when some people are constantly running uphill or
against the wind, which can inform us how to give everyone an equal
tailwind and an equal opportunity to succeed.

_[DR. CHENG is the scientist in residence at the School of the Art
Institute of Chicago. She is a mathematician and the author, most
recently, of “Unequal: The Math of When Things Do and Don’t Add Up
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* DEI
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* D.E.I
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* diversity equity and inclusion
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* woke culture
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* affirmative action
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* Racism
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* discrimination
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* Inequality
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* Equality
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* Math
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* mathematics
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* MAGA
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* Donald Trump
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* Trump 2.0
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