Mustafa I. Hussain, MS, Department of Informatics
Science has had a problem for a long time. The problem resurfaced recently, when news broke that Nobel Prize-winning biologist James Watson believes that race determines intelligence (Yancy-Bragg, 2019). Many of us instructors may find silence on such issues comfortable—but our silence is not helpful (Robertson & Chaney, 2017).
Schinske et al. (2016) found that instructors can shift students’ stereotypes of scientists—“what scientists look like”—by asking students to study scientists of color. That is good. At the same time, we should not ignore the reasons why “implicit biases”—or prejudices—have been so reliably found in so many individual students’ heads (Patton, 2016).
Nearly every discipline has been complicit in inhumanity (Pine and Hillard, 1990, p. 595). Still, many of these histories continue. Take, for example, the return of physiognomy—judging a person’s character by their physical appearance—in psychology and data science (Bartlett et al., 2018).
To shift the historical trajectory of racial discrimination in universities, we may need to teach students to view our own fields critically. Accordingly, in what follows, I offer some strategies, based on the critical pedagogy literature and my personal experience in technological programs.
Explain scientific knowledge production in nonlinear terms
Positivists usually present science as a straightforward, linear accumulation of facts that inevitably inches closer to objective truth (Kuhn, 2012). However, this usually conflicts with scientists’ real lives (Latour and Woolgar, 1979).
Teaching students that science is a linear accumulation of facts may lead them to face emerging science news with uncritical faith later on in life (e.g., Briggs, 2019). For example, students should be capable of recognizing pseudoscience—from anti-vaccine “studies” to contemporary physiognomy—long after graduation. So, we might instead present a more complete picture that includes scientific upheavals and redirections—paradigm shifts.
Acknowledge uncomfortable histories and continuities in core classes
More complete histories will also include the periods that give us the most discomfort. For example, many famous scientists, like Darwin and Watson, have promoted racial prejudice in scientific disguise (Barta, 2005; Yancy-Bragg, 2019).
It may be tempting to leave the most uncomfortable content to the degree’s ethics or diversity requirements (Patton, 2016). This may be harmful, for at least two reasons:
- First, if the lessons of ethics and diversity classes do not appear to matter outside of those classes, then students may wrongly assume that ethics are primarily the concern of ethicists, and proceed to deprioritize the lessons of those classes.
- Second, if the core curriculum appears ‘apolitical,’ students may assume that, apart from a handful of random errors, science as a whole marches forward unproblematically. On the contrary—discrediting bad science requires real work.
Privilege humanity over science
I love science. I also believe that humanity is more important.
For example: Physicians harvested some of Henrietta Lacks’s cells without permission, consent, or compensation, and biologists continue to clone and study these cells for monetary and reputational gain (hooks, 2013). To this end, authors tend to portray the “HeLa cells” as Lacks’s unwitting yet heroic sacrifice. However, the “greater good” does not make the medical establishment’s ongoing injustice any less painful (hooks, 2013).
We might instead say that Lacks’s right to bodily autonomy should have been respected, that those who violated her rights were wrong to have done so, that the resulting scientific knowledge does not excuse prior and ongoing wrongdoing, that her descendants should be compensated, and that compensation cannot undo the harm inflicted.
The distinction may appear subtle, but students of color often notice these ‘subtleties’ (Solorzano et al., 2000). In sum, if we care about diversity, we must choose our words—and design our curricula—with care.
Bartlett, A, Lewis, J, Reyes-Galindo, L, & Stephens, N (2018). The locus of legitimate interpretation in Big Data sciences: Lessons for computational social science from -omic biology and high-energy physics. Big Data & Society, 5(1), 2053951718768831.
Latour, B & Woolgar, S (1986) . Laboratory life: the construction of scientific facts. Princeton, New Jersey: Princeton University Press. Originally published 1979 in Los Angeles, by Sage Publications. ISBN 9780691094182.
Schinske, JN, Perkins, H, Snyder, A, & Wyer, M (2016). Scientist spotlight homework assignments shift students’ stereotypes of scientists and enhance science identity in a diverse introductory science class. CBE—Life Sciences Education, 15(3), ar47.
Solorzano, D, Ceja, M, & Yosso, T (2000). Critical race theory, racial microaggressions, and campus racial climate: The experiences of African American college students. Journal of Negro education, 60-73.
Matthew Mahavongtrakul edited this post on April 23rd, 2019.