NIH to probe racial disparity in grant awards

Aug 22, 2014

By Sara Reardon


Richard Nakamura, director of the Center for Scientific Review at the US National Institutes of Health (NIH), does not consider himself to be racially biased. Yet a test of his speed at associating certain words with faces of different races revealed a slight unconscious prejudice against minorities. If the director of the institute that oversees the NIH’s grant process harbours these inclinations, he wonders, are grant reviewers affected as well?

To answer that question, the NIH will launch ambitious analyses beginning in September to determine whether bias hampers minority scientists who seek agency funding. A 2011 study in Science found that white researchers receive NIH grants at nearly twice the rate that African American researchers do (see ‘Grant gap’). Even when factors such as publication record and training are considered, an African American scientist is still only two-thirds as likely as a white scientist to be funded (D. K. Ginther et al. Science 333, 1015–1019; 2011). The disparity seems to arise early during the review process, when grants are first rated.

The findings spurred the NIH to launch a ten-year, US$500-million effort in 2012 to train and mentor minority scientists. But officials acknowledge that the racial gap among grantees is not just because there are fewer qualified applications from minority researchers. Now the agency will look inward to determine where its grant process may be failing — and what to do about it.

One basic issue that the NIH will address is whether grant reviewers are thinking about an applicant’s race at all, even unconsciously. A team will strip names, racial identification and other identifying information from some proposals before reviewers see them, and look at what happens to grant scores. (Such identity stripping is surprisingly difficult: even citations might reveal who the applicant is, and reviewers need some information about an applicant to make a fair appraisal.) The results could be telling. “If the disparity drops with anonymization, that’s clear evidence of bias,” says Nakamura.

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