Best Practices

These practices have been shown to reduce bias:

  • Develop and prioritize evaluation criteria prior to evaluating candidates; apply them consistently to all applicants. Use an evaluation rubric for each candidate. If a diversity statement was requested of applicants, create evaluation criteria for assessing its strength before reviewing the submissions.
  • Disambiguate criteria as much as possible (Biernat & Fuegen, 2001);
  • Be able to defend every decision for eliminating or advancing a candidate.
  • Use an inclusion strategy rather than exclusion strategy when evaluating CVs. An inclusion strategy identifies which candidates are suitable for consideration; whereas, an exclusion strategy decides which should be eliminated.
  • Spend sufficient time evaluating each applicant. Reduce time pressure and cognitive distraction when evaluating applications.
  • Evaluate each candidate’s entire application; don’t depend too heavily on only one element.

Evaluate Your Judgments

Periodically evaluate your judgments, determine whether qualified women and underrepresented minorities are included in your pool, and consider whether evaluation biases and assumptions are influencing your decisions. Assign someone to remind the committee members to reflect on the following questions (Fine & Handelsman, 2012b):

  • Are women and minority candidates subject to different expectations or standards in order to be considered as qualified as majority men?
  • Have the accomplishments, ideas, and findings of women or minority candidates been undervalued or unfairly attributed to a research director or collaborators despite contrary evidence in publications or letters of reference?
  • Is the ability of women or minorities to run a research group, raise funds, and supervise students and staff of different gender or ethnicity being underestimated?
  • Are assumptions about possible family responsibilities and their effect on a candidate’s career path negatively influencing evaluation of a candidate’s merit, despite evidence of productivity?
  • Are negative assumptions about whether women or minority candidates will ‘fit in’ to the existing environment influencing evaluation?


After the initial review of candidates, reflect on the following questions (Gilies, 2016):

  • What facts support our decisions to include or exclude a candidate? Where might we be speculating?
  • How do the demographics of our shortlist compare with our qualified pool, and with the national pool?
  • Have we generated an interview list with more than one minority finalist?
  • If a high percentage of underrepresented candidates were weeded out, do we know why? Can we reconsider our pool with a more inclusive lens, or extend the search?


Nhora Lucía Serrano, Ph.D.

Director of Learning and Research Services

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