The Science of Scientific Bias



This year’s David Kopf lecture on Neuroethics was given by Dr. Jo Handelsman, entitled “The Fallacy of Fairness: Diversity in Academic Science”. Dr. Handelsman is a microbiologist who recently spent three years as the Associate Director for Science at the White House Office of Science and Technology Policy, and has also led some of the most well-known studies of gender bias in science.

She began her talk by pointing out that increasing diversity in science is not only a moral obligation, but also has major potential benefits for scientific discovery. Diverse groups have been shown to produce more effective, innovative, and well-reasoned solutions to complex problems. I think this is especially true in psychology - if we are trying to create theories of how all humans think and act, we shouldn’t be building teams composed of a thin slice of humanity.

Almost all scientists agree in principle that we should not be discriminating based on race or gender. However, the process of recruiting, mentoring, hiring, and promotion relies heavily on “gut feelings” and subtle social cues, which are highly susceptible to implicit bias. Dr. Handelsman covered a wide array of studies over the past several decades, ranging from observational analyses to randomized controlled trials of scientists making hiring decisions. I’ll just mention two of the studies she described which I found the most interesting:

  • How it is possible that people can be making biased decisions, but still think they were objective when they reflect on those decisions? A fascinating study by Uhlmann & Cohen showed that subjects rationalized biased hiring decisions after the fact by redefining their evaluation criteria. For example, when choosing whether to hire a male candidate or a female candidate, who both had (randomized) positive and negative aspects to their resumes, the subjects would decide that the positive aspects of the male candidate were the most important for the job and that he therefore deserved the position. This is interestingly similar to the way that p-hacking distorts scientific results, and the solution to the problem may be the same. Just as pre-registration forces scientists to define their analyses ahead of time, Uhlmann & Cohen showed that forcing subjects to commit to their importance criteria before seeing the applications eliminated the hiring bias.

  • Even relatively simple training exercises can be effective in making people more aware of implicit bias. Dr. Handelsman and her colleagues created a set of short videos called VIDS (Video Interventions for Diversity in STEM), consisting of narrative films illustrating issues that have been studied in the implicit bias literature, along with expert videos describing the findings of these studies. They then ran multiple experiments showing that these videos were effective at educating viewers, and made them more likely to notice biased behavior. I plan on making these videos required viewing in my lab, and would encourage everyone working in STEM to watch them as well (the narrative videos are only 30 minutes total).


Thank you so much for sharing this post on the lecture. I wasn’t able to make it, so I find all the links and information EXTREMELY helpful! Thank you!