CSR Launches Competition for Ideas to Detect Bias and Maximize Fairness in Peer Review

As part of NIH’s efforts to address racial disparities Exit icon in grant funding, the Center for Scientific Review (CSR) has just launched two America COMPETES Act Challenges. We hope that the ideas we receive will help us maximize the fairness and vitality of the peer review process, and we encourage you to enter.

One challenge, New Methods to Detect Bias in Peer Review, solicits ideas for strategies to detect possible bias in the NIH peer review process. Submissions can include approaches, strategies, methodologies and/or measures that would be sensitive to detecting bias among reviewers due to gender, race/ethnicity, institutional affiliation, area of science and amount of research experience. We’ll award first place ($10,000) and second place ($5,000) prizes in two categories: best empirically based idea and most creative idea.

The other challenge, Strategies to Strengthen Fairness and Impartiality in Peer Review, seeks ideas for reviewer training methods aimed at enhancing fairness and impartiality in NIH peer review. The submission does not require the full development of training materials. However, ideas should be presented with enough detail to allow assessment of their ability to address fairness and impartiality in review with regard to gender, race/ethnicity, institutional affiliation, area of science and amount of research experience. We’ll give first place ($10,000) and second place ($5,000) prizes for the best overall idea.

The challenges close on June 30, and winners will be announced on September 2. Details on the rules and submission procedures are on the CSR Challenge Web site and at http://www.challenge.gov Exit icon.

This contest is just one of many initiatives CSR is working on to evaluate the sources of racial disparities in grant funding in collaboration with the ACD Diversity Working Group Subcommittee on Peer Review.

One comment on “CSR Launches Competition for Ideas to Detect Bias and Maximize Fairness in Peer Review

  1. When did taking into account amount of research experience become a bias?
    Does it not make sense to prefer an experienced researcher, who is likely to carry out the research plan she outlines, over someone who does not have any track record?

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