Beyond Good Intentions: Answering Common Criticisms of Anti-Racism in Science

Examining evidence for racism in STEMM fields and addressing common criticisms of anti-racism efforts with evidence-based responses.

2020 Watershed Moment STEMM Analysis Data-Driven Approach

Introduction: The Unignorable Call for Change in STEMM

The year 2020 was a watershed moment for institutions worldwide. The wrongful murders of George Floyd, Breonna Taylor, and others sparked a global confrontation with racial injustice that could not be ignored—even within the hallowed halls of science, technology, engineering, mathematics, and medicine (STEMM). As protests unfolded, a growing anti-racism movement forced a long-overdue conversation within scientific fields about how to combat deeply entrenched racial bias 1 .

Key Distinction

The scientific enterprise—distinct from the objective scientific method—is a human undertaking, subject to the same biases and societal forces that shape all human institutions.

For many in STEMM, this represented an uncomfortable paradigm shift. While the scientific method strives for impartiality, the scientific enterprise involves how experiments are conceived, performed, and interpreted in real practice, making it vulnerable to both individual and systemic racism 1 .

This article explores the evidence for racism in STEMM and addresses common criticisms of anti-racism efforts with evidence-based responses, providing a roadmap for creating a more equitable and innovative scientific future.

The Reality of Racism in Scientific Institutions

Before addressing the criticisms, it's crucial to understand the documented evidence of racial disparities in STEMM fields.

By the Numbers: Systemic Disparities

The data reveals stark inequalities in representation and advancement for scientists from marginalized backgrounds:

Racial Representation Disparities in U.S. STEMM

Population Group Percentage of U.S. Population Percentage of Biology Professors Percentage of Life Sciences PhD Students
Black Individuals ~13% 0.7% 5.9%
White Individuals ~60% 83.3% Not specified

Data sourced from PLOS Computational Biology analysis of STEMM workforce diversity 1 .

13% Less Likely

Black applicants are 13% less likely than white applicants to receive research funding from the National Institutes of Health (NIH) 1 .

Salary Disparities

Black and Hispanic/Latinx professionals in STEMM earn substantially lower salaries than white counterparts at all educational levels 1 .

From Microaggressions to Medical Bias

Racial discrimination in STEMM often manifests as microaggressions—commonplace indignities that communicate hostile or derogatory messages to people of color.

Women of Color Faculty

Frequently mistaken for university staff rather than faculty members 1 .

Black Professors

Often perceived by colleagues as having been hired primarily for their underrepresented minority status 1 .

Medical Bias Alert

In one study, 40% of medical students reportedly believed false claims about biological differences between races, such as that Black people have thicker skin than white people. Students who held these false beliefs rated Black patients' pain lower than white patients' and made less accurate treatment recommendations 1 .

Answering the Critics: Evidence-Based Responses to Common Arguments

Criticism 1

"There's no evidence of racism in STEMM."

"The scientific literature tells a different story."

As detailed above, numerous studies document racial disparities in hiring, funding, compensation, and professional advancement in STEMM fields 1 .

The distinction here is between the scientific method (which is objective) and the scientific enterprise (which involves human decisions about which research questions to pursue, who to hire, which projects to fund, and how to interpret results). The enterprise is undoubtedly influenced by racial biases, both implicit and explicit 1 .

Systemic racism in science doesn't necessarily require individual racist intent; it can be maintained by inaction and apathy toward existing disparities 1 .

Criticism 2

"Don't politicize STEMM! Stick to the science, not social issues."

"This argument assumes that science exists separately from society, which is fundamentally inaccurate."

Scientific research is inevitably influenced by societal values and priorities 1 . The questions researchers choose to investigate, the methods they use, and how they interpret their results are all shaped by their perspectives and experiences—including their unconscious biases.

Furthermore, the historical mistreatment of people of color in scientific research (such as the Tuskegee Syphilis Study or the nonconsensual use of Henrietta Lacks' cells) has created lasting distrust in medical and scientific institutions among marginalized communities 1 .

Addressing this legacy and building inclusive scientific practices isn't "politicizing" science; it's improving scientific accuracy and relevance for all populations.

A Closer Look: The CV Study Experiment

To understand how racial bias operates in academic science, let's examine a landmark study that reveals how discrimination can occur even when review processes appear objective on the surface.

Methodology: Identical Credentials, Different Names

Researchers conducted a controlled experiment to investigate bias in the academic hiring process 1 . The study design was straightforward but powerful:

CV Preparation

Researchers created identical academic CVs with identical qualifications, publications, and experience.

Name Manipulation

The only difference between CVs was the applicant's name—some had traditionally white-sounding names, while others had traditionally Black or Latinx-sounding names.

Evaluation

These identical CVs were sent to professors across various institutions who were asked to evaluate the applicants.

Measurement

Researchers measured how favorably professors rated identical CVs based on the perceived racial identity of the applicant.

Results and Analysis: The Impact of Unconscious Bias

CV Study Results - Faculty Ratings of Identical Applications

Applicant Perception Average Favorability Rating Perceived Competence Likelihood of Hiring
Traditionally White Name Significantly Higher Higher Greater
Traditionally Black Name Significantly Lower Lower Reduced
Traditionally Latinx Name Significantly Lower Lower Reduced

Data reflects trends found in study of professor evaluations of CVs with racially identifiable names 1 .

Implicit Racial Bias

This experiment provides crucial evidence for what's known as implicit racial bias—unconscious prejudices that can influence a person's perceptions without their awareness 1 . The study helps explain why racial disparities persist in STEMM fields despite many institutions having formal diversity policies and non-discrimination statements.

Global Initiatives for More Inclusive Science

The recognition of racial disparities in STEMM has sparked action at institutional and national levels worldwide:

Anti-Racism and Diversity Initiatives in Scientific Institutions

Initiative Lead Organization Focus & Approach
Elevate: Boosting Diversity in STEM Australian Academy of Science Industry partnerships to grow women and non-binary leaders in STEM through scholarships and support 3 .
Superstars of STEM Science and Technology Australia Raising visibility of diverse STEM professionals to challenge stereotypes through media training and mentoring 3 .
Anti-Racism Research Grants University of Michigan Funding graduate research on structural racism and racial justice across various domains, including STEM 6 .
Women in STEM and Entrepreneurship Grants Australian Government Eliminating barriers to women's participation in STEM, focusing on those facing multiple barriers 3 .
Diversity & Inclusion Toolkit Australian Academy of Technology and Engineering Practical guidance for STEM businesses to create more diverse and inclusive workforces 7 .
Partnerships
Visibility
Funding
Resources

The Scientist's Toolkit: Key Concepts for Anti-Racism Work

Understanding these foundational concepts is essential for productive conversations about racial equity in STEMM:

Systemic Racism

Policies and practices entrenched in established institutions that result in the exclusion or promotion of designated groups. Importantly, it does not require individual intent but is maintained by inaction toward discriminatory outcomes 1 .

Intersectionality

How multiple aspects of one's identity (such as race, ethnicity, gender expression, sexual identity, class, culture, and age) contribute to compounding experiences of discrimination. This concept, developed by legal scholar Kimberlé Crenshaw, helps explain how different forms of inequality interact 5 .

Implicit Racial Bias

Unconscious prejudices against a racial group that can influence a person's perceptions without their awareness. These biases can affect hiring decisions, peer reviews, and student evaluations regardless of conscious intentions 1 .

Colorism

A form of prejudice in which people with lighter skin are treated more preferentially than people with darker skin, independent of race. This can create hierarchies of privilege and discrimination within racial groups 1 .

Interest Convergence

The concept, developed by Derrick Bell, that progress for people of color tends to occur only when it also serves the interests of dominant white groups. This helps explain why racial progress is often slow and incremental 5 .

Conclusion: Toward a More Innovative and Equitable Scientific Future

Confronting racism in STEMM isn't about assigning blame or guilt; it's about recognizing the evidence of racial disparities and working systematically to create a scientific enterprise that truly lives up to its ideals of objectivity and meritocracy.

The research clearly shows that racial bias—both individual and systemic—affects who enters scientific fields, who succeeds, whose pain is taken seriously in medical settings, and which research questions get pursued.

The future of scientific innovation depends on embracing the full spectrum of human talent. When we exclude or marginalize brilliant minds because of racial bias, we not only harm individuals but also impoverish scientific progress itself.

By implementing evidence-based anti-racism policies, fostering inclusive environments, and continuously examining our practices, we can build a scientific community that truly merits the descriptor "objective"—one that harnesses diverse perspectives to solve humanity's most pressing challenges.

The Data Are Clear

The path forward requires moving beyond good intentions to implement concrete changes that will make STEMM fields more equitable, innovative, and responsive to all communities they serve.

Scientific Progress

Requires diverse perspectives and inclusive practices to reach its full potential.

Collective Action

Creating equitable scientific institutions requires commitment from all stakeholders.

References

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