OIBR Guidelines for Mitigating Bias

 Outreach and Inclusion 

  • Engage in targeted outreach to women/URM investigators with encouragement to apply 
  • Consider funding that is targeted specifically to women/URM 
  • Include statements regarding the value of women/URM as investigators and topics related to diversity, equity and inclusion 

Committee Membership 

  • Diversify reviewers to include women and URM BUT be careful not to overextend women/URMs (tend to be over committed to service) 
  • Ensure adequate time for decision-making to reduce barriers to participation on awards committees (e.g., allow call-ins and virtual participation; do not schedule during times that may be hard for certain groups to attend such as childcare drop-off or pick-up) 

Review Criteria and Procedures 

  • Include review criteria with calls for proposals and awards 
  • Avoid vague criteria such as “excellence” or “merit” 
  • Omit ratings related to the stature/tenure/prior grant funding of applicants when rating (unless directly relevant to the award) 
  • De-emphasize productivity (i.e., publications) as “markers of expertise” for Grant Development Program applicants 
  • Consider diversity of research team on seed grant proposals dealing with race/gender disparities; all white/male teams may not be adequate 
  • Create and utilize guidelines with clear descriptions of criteria for evaluation 
  • Standardize the review, scoring and selection process 


  • Implement a conflict of interest policy 
  • Inform applicants, nominators, and reviewers who the final decision-maker(s) is/are for awards and Grant Development Program selection 
  • Publish membership of review panels 
  • Publically post awardees to increase transparency and accountability 


  • Review all materials (including decision letters) for biased language (including unnecessary pronouns, say “The applicant” versus “Her…”) and periodically review calls for awards and programs 
  • Collect background information on applicants and examine trends in awards for potential bias