Neuroscience has to grapple with a long legacy of racism if it wants to move into the future

Many parts of neuroscience research have a race problem. Black people are often excluded from studies due to the texture of their hair, receive erroneous and inaccurate readings due to the melanin content of their skin, and are severely underrepresented in neuroimaging datasets.

Now neurotechnology is undergoing a moment of tremendous change, as Elon Musk’s Neuralink has obtained independent review board approval to conduct its first human trials for the R1 robot and N1 brain implant. That makes it an especially good time to have a frank conversation about who gets to lead innovation in neuroscience, especially within neuroengineering. Getting this wrong has vast consequences.

Those of us in neuroscience are pushing the boundaries of what we can observe with the brain as emerging neurotechnologies push us toward a future in which man and machine are, quite literally, more connected than ever. While many of the applications of brain-computer interfaces (BCI) are promising, especially those around neurorehabilitation and restoring function to individuals with paralysis, those being developed by Neuralink and others also raise many questions of safety, efficacy, ethics, and inclusion.

Both the precursors to and current field of neuroscience have many times been weaponized to entrench Black inferiority in academic thought or exclude Black people in technological design. We must reckon with the racism that has perpetuated our field in order to ensure that our future discoveries and innovations are just and equitable. These are some of the reasons for Black in Neuro, a nonprofit organization I co-founded in 2020 and run.

Our organization coined the term #NeuroRacism to speak to the ways neuroscience and its related fields can discriminate against Black people. In short, the racism and stress Black people encounter leads to disparities in mental health incidence and access to interventions, as well as higher rates of a variety of diseases, including amyotrophic lateral sclerosis (ALS), brain cancers such as glioblastoma, Alzheimer’s and related dementias, and stroke. We are often diagnosed later, underrepresented in clinical trials, and less likely to survive neurological diseases.

These inequalities are compounded by how various neuroimaging techniques don’t serve Black patients well. Electroencephalography (EEG), which monitors electrical signals that emanate from the brain, is not optimized for Black hair, so participants may have to shave their heads or be excluded from studies completely. Increasingly popular light-based techniques, such as functional near-infrared spectroscopy (fNIRS), have not been corrected for those with higher melanin contents in their skin, which can lead to confounding or less accurate results. This has also been seen with pulse oximetry, a measure of oxygen saturation within your blood, a vital parameter in bedside patient monitoring.

Further still, the history of pseudosciences such as phrenology and race science have also led to lasting consequences on the perception of Black people as lesser beings. Race is a social construct, not a biological certainty, therefore classification using a flawed premise will lead to flawed science.,

Yet many health care professionals continue to believe that Black people’s skin is tougher or that we have higher pain thresholds. Such erroneous ideas contribute to higher rates of maternal mortality in childbirth and rates of medical abuse and negligence. Lower qualities of care have also been observed for Black autistic patients compared with white autistic patients. Our phenotypic variations can be accounted for with proper evidence, without the unscientific conclusion of inferiority and resultant malpractice.

The near future also presents issues. We live in a world that is becoming increasingly automated and reliant upon artificial intelligence. AI and neuroscience are complementary fields that have historically driven each other forward. Revolutions in AI in recent years have come due to the development of methods such as deep learning, which is inspired by the workings of the human brain — also known as artificial neural networks. AI is already a useful tool within neuroscience research; however, its wider use has many potentially negative implications for Black people. The datasets that are used to train these algorithms are often not diverse or rely on historical data, which leads to the outputs being less accurate when Black people are involved and potentially harmful. Already from the use of AI, Black people have been the victims of mistaken identity in the criminal justice system, been improperly rejected for home loans, and have had to be deemed much sicker than white patients to be recommended for the same level of health care.

Many of those problems in both AI and neuroscience could have been avoided had Black experts been present and, equally important, listened to during decision-making. Black people earned 7% of all doctoral degrees in 2019 despite being 13% of the U.S. population. The National Science Foundation has also previously reported that only 4.8% of engineers and scientists in this country are Black. The National Center for Education Statistics found that 6% of full-time faculty were Black. Black people account for 4% of those who receive neuroscience Ph.D.s, 3% of postdoctoral appointees, and 1% of faculty, according to a Society for Neuroscience 2017 report. Our historical exclusion still looms in the present, with the lack of diverse teams, often without Black neuroscientists in the room and the rarity of Black leadership, ultimately leading to technologies and practices that do not work for everybody and by extension that can cause or perpetuate harm.

Interventions to get more Black neuroscientists and engineers into academia or industry are only the beginning, though. Black scientists are cited less, often have their work stolen and face disparities in the awarding of funding, in addition to the everyday racism and microaggressions that push us out of scientific research.

So where do we go from here? I envision a future in which we reduce biases in the data that we collect, where we clean up the environments that produce our cutting-edge research and innovation, adjust our current technologies and practices to include more people and create new technologies with diverse teams, for the benefit of all. While we still have a chance, let’s reflect, correct, and do better.

De-Shaine Murray is a Wu Tsai Institute postdoctoral fellow at Yale University, Black in Neuro co-founder and development director and a Public Voices Fellow of the OpEd Project. Black in Neuro is a registered 501(c)3 non-profit organization which aims to support Black neuroscientists across the world.