Cows, Wall Street and science

How applying findings on diversity and bias
can help improve the research workforce

Published June 29 2016

At the turn of the 20th century, a diverse group of exhibition-goers were asked to guess the weight of a displayed steer. Their averaged guess was better than the guess of any single person’s, be they amatuer or pro. This finding has been replicated in a variety of settings in the century since.

Diversifying the research workforce is inherently complex. At the National Institutes of Health, we have learned that addressing such complexity requires a rigorous scientific approach, which is consistent with the ways that we address the challenges of science discovery.

The scientific necessity of diversity

The discovery that happens at the NIH gets done by people — creative scientists of all types all across the nation. As the NIH’s chief officer for scientific workforce diversity, I am keenly interested in a couple of deceptively simple questions when it comes to hiring these people. Who are the best ones for the task, and how can we be sure we’re getting the best ideas? Luckily, science is helping me answer these questions. It tells me that in many ways diversity drives innovation and that we ought to be assembling diverse teams of scientists if we want to hasten discovery.

Let’s consider a few real-life scenarios

It’s 1906 at the West of England Fat Stock and Poultry Exhibition. People are lined up to guess the weight of a steer on display. Some know a lot about the weight of such animals. Others don’t. In the end, all the guesses are averaged. That average turns out to be within one pound of the actual weight of the cow and closer than any one person’s guess. This finding, which has been replicated in other settings, suggests that the output of a group of different people is more accurate than any one person, be they an amateur or a pro.

In a more recent study from 2014, a group of ethnically similar financial traders were 33 percent less able to predict stock prices accurately than an ethnically diverse group. What’s more, the similar group members accepted inflated prices based on speculation, contributing to a harmful financial bubble.

But these findings about diverse versus non-diverse groups don’t just play out at stock shows and on Wall Street. They are true in science as well. In a 2013 study, economists looked at 2.6 million U.S. scientific papers across many fields, using surnames of co-authors as a proxy for assessing ethnic diversity. They controlled for other factors. The first result was expected. Like associated with like, and ethnically similar scientists published more often together. But a second result may surprise you. Those papers with co-authors of multiple ethnicities had more citations and were published in journals with higher impact factors.

This essay is part of ASBMB Today’s ongoing discussion of diversity and inclusion matters in biochemistry and molecular biology.
Click here for more.

Researchers believe that when diversity produces better outcomes, it is because friction is productive. In the face of difference, we have a tendency to take a closer look at our own assumptions, and we feel compelled to listen to others’ views.

You could sum it up this way: The wisdom of the crowd is the power of diversity. But if we know this, why do scientists who pride themselves on objectively evaluating data consistently rate applications with male names higher than those with female names, even when the information contained in the applications is identical? Why are applicants whose names suggest that they might be African-American less likely to receive a job call-back compared with applicants with white-sounding names?

Tackling bias

Something is getting in the way of our ability to leverage the power of diversity. Is that something simply bias? And, if so, is it bias that we are aware we have or bias we don’t even recognize in ourselves?

Research shows that mental shortcuts, or heuristics, lead to judgment errors that emerge in everyday life and also in major decisions, such as recruitment and hiring. Because the human brain is wired to process multiple pieces of information rapidly by using mental shortcuts, we are all guilty of bias.

Recognizing this human truth is a first step to interrupting subconscious snap judgments that may not have the best — or fairest — outcomes. At the NIH, we are working on ways to minimize subconscious, or implicit, bias in scientific settings. One way that we do this is by providing educational modules on bias to scientists leading recruitment searches. To see whether the modules are effective, we give a pre- and post-test to measure attitudes known to be associated with certain biases, and then we assess the diversity in their selected pool of candidates. At other institutions, these approaches have had a positive impact on diversity outcomes, and, if we are successful, we hope to expand the education across the NIH. Stay tuned for the results!

Hannah Valantine Hannah Valantine is the chief officer for scientific workforce diversity at the National Institutes of Health. She leads the agency’s effort to diversify the national scientific workforce and expand recruitment and retention.