Bias in Peer Review – A Training Module
Introduction
Welcome to bias in peer review, a training module for peer reviewers.
This interactive module is designed to promote an understanding of unconscious bias and how
it can affect the peer review process. It will also provide strategies for mitigating bias during
the review process.

Objectives
By the end of this course, you will be able to:•understand what unconscious bias is;•understand how unconscious bias can impact the peer review process; and•integrate methods for mitigating the influence of unconscious bias.

Objective 1
In this section, we will explore the difference between explicit bias and implicit (orunconscious) bias.

How biases are formed 1
Biases are shortcuts our brain forms based on:•

our own culture;•experiences;•

things other people tell us;•

institutional influences; and•other external influences, such as social media.

When faced with situations or people, we use mental maps and patterns to classify them bymaking a number of automatic associations. Not surprisingly, our perceptions andassumptions based on these automatic associations are not always correct.

Because our unconscious biases tend to be ingrained, it takes some work to disrupt them, but
it can be done through active reflection and practicing inclusive behaviours. Doing this work
benefits us, the people around us and the peer review process; it also contributes to research
excellence.

Consider the following scenario:
You are out with friends and see someone wearing a sports jersey from your favourite team.
You decide that you like them and decide to have a friendly conversation with them.
In this situation, you are consciously aware of the reason that you like that person. You makea deliberate decision to talk to them and you are aware of the process by which you reachedthat decision. This is an example of explicit bias.

What is explicit bias? 2
Explicit bias is a result of conscious thought and can be deliberately regulated. People are
more motivated to control their biases if there are social norms in place, which dictate thatprejudice is not socially acceptable.
Every peer reviewer is asked to sign a Conflict of Interest Agreement, which states that a
conflict may be deemed to exist if reviewers feel for any reason unable to provide an impartial
review of the application.
Explicit bias is an example of being unable to provide an impartial review, and is therefore aconflict of interest. For this reason, explicit bias is not explored in this module.

Consider the following scenario:
You are out with friends and see someone at the table beside you. The person gives you an
uneasy feeling and you feel uncomfortable. Your friend notices and asks you what is wrong,but you aren’t sure. You cannot describe why you feel that way.
In this situation the person at the table beside you might remind you of someone from yourpast with whom you had a negative experience. You are not aware of this, and the feelings
are unconscious.
This is an example of unconscious bias. The feeling was involuntarily formed and you cannot
explain why you feel that way. The feeling is caused by a past experience and you were
unaware of it until you felt uncomfortable.

What is unconscious bias? 1 3 4 5An unconscious bias is an implicit attitude, stereotype, motivation or assumption that canoccur without one’s knowledge, control or intention. Unconscious bias is a result of our life

experiences and affects all types of people. Examples of unconscious bias include gender bias,
cultural bias, age bias, language and institutional bias.
Unconscious biases are important to recognize in instances when quality, relevance andcompetence are being evaluated. Examples of these instances include peer review of grants,
search committees, reference letters and peer review of manuscripts.

How does unconscious bias work?
Unconscious bias affects our judgment without us realizing.

For example, these tables appear to be different sizes. We think they are different just bylooking at them; however, in reality, they are the same size.

This is an example of how our brains form shortcuts, and of how unconscious bias occurs.

Effects of bias 6 7
Unconscious bias may not significantly affect every single interaction or decision but it canhave lasting consequences. Decisions made based on unconscious bias can compound overtime to significantly impact the lives and opportunities of others who are affected by thedecisions we make.
Small biases in peer review of career boosting resources (like grants and journal publications)can make a big difference in the long-term career of an investigator.

In this example, a computer simulation studied the effects of a one per cent bias and foundthat cycle had an impact on how the blues and oranges were represented over time, andultimately the representation of each colour drastically changed as a result of this unconscious
bias. It is therefore very important to mitigate unconscious bias in peer review.

Biased indicators of excellence 20 21 22 23 24 25
The granting agencies fund excellent research and researchers, but what if the indicators of
excellence used in evaluation of candidates are biased? Many reviewers may assume thatthey are applying an objective standard of excellence when they look at three traditional
metrics:•

years of experience;•

number of publications and citations; and•size of a candidate’s research grants.

However, it has been shown that all three of these metrics are subject to unconscious bias.

Did you know?
Research has shown that underrepresented groups are more likely to face systemic barriers(such as more expectation to sit on committees) that limit their lab time, result in lessresearch funding and contend with the view that their publications are less significant.
In addition, women face less integration into the scientific community, for example, by
holding positions and memberships in scientific associations and on the editorial boards ofjournals. These barriers have resulted in women publishing 20 per cent fewer publications
than men in the natural sciences, despite being equally qualified.
This bias has extended to the peer review process, where female first authors suggested
female reviewers 21 per cent of the time, while men suggested women just 15 per cent of thetime. In addition, men cite their own papers at a 35 per cent higher rate than women.
When looking at aggregated data, it has also been shown that women self-promote their own
works at a lower level and their publications receive lower recognition (citations) from thescientific community despite being published in journals with higher impact factors.
Unconscious bias is also a key factor when evaluating the productivity of researchers withcareer leaves and slowdowns. Members of underrepresented populations are more likely toface career interruptions because of parental, family or medical reasons. For example, recent
newcomers to Canada may need leaves or face slowdowns in order to address immigrationand settlement issues.
This can understandably have an effect of the productivity of the researcher, meaning two
equally qualified candidates may not have the same number of publications, but this does notmake the candidate with a career leave any less excellent. Therefore, relying on publications
as an indicator of excellence without taking into consideration the impact on productivity of
the leaves unduly disadvantages researchers with career leaves.
For example, a recent study found that after a parental leave, male economists had a 19
percentage-point rise in the probability of tenure in their first job. In contrast, women’s
chances of gaining tenure fell by 22 percentage points. This is because men are more able totake advantage of this leave to publish in top-tier journals. In short, not taking gender into
account has the consequence of raising the bar for tenure even further for women.
Despite this, there are many studies that show that greater diversity leads to better research.
In other words, the research enterprise won’t reach its full potential for excellence unless it isdiverse. This underscores the need to use unbiased indicators of excellence in the peer review
process.

Bias in peer review
There are many types of unconscious bias that affect our daily lives, and this is no different in
the review process. There are, however, types of unconscious bias that are more likely toappear in the review process.
These include gender bias, institutional bias, age bias, culture bias (including Indigenous bias
and geographic bias) and language bias.
Bias can be influenced by the characteristics of both the reviewer and the applicant, as well as
the nature of the application and where the research is being conducted and by whom.
It is important to note that bias may not only be towards someone with different
characteristics than you; bias can also occur towards someone with the same characteristics

as you. For example, female reviewers can be just as unconsciously biased against female
applicants as male reviewers.

Objective 2
In this section, we will explore different types of unconscious bias and how they can impact
the peer review process.

What is gender bias? 10
Unconscious gender bias has been defined as the differential treatment of men and women,
the impact of which may be positive, negative or neutral.

Evidence of gender bias by linguistic analysis 11 12
In a 2003 study in the United States, academic linguists reviewed 312 letters ofrecommendation for faculty hired at a major medical school.
Letters written for female applicants were shorter and less focused on the candidate’s recordof accomplishment; they used more gendered terms such as “intelligent young lady” or“insightful young woman.” Letters for women included more grindstone adjectives such as
hardworking, conscientious, dependable, thorough, dedicated, careful or meticulous, whereas
letters for male applicants included more standout adjectives such as excellent, superb,
outstanding or unique.
This finding suggests that women’s success is more often associated with effort while men’s
success is associated with ability.
Letters written for female applicants included more doubt raisers such as “she worked hard on
the project that she accepted,” and were significantly more likely to have references to one’s
personal life than those written for men.
In contrast, letters written for male applicants were more likely to have references to theirCV, publications or patents.
It is therefore important to evaluate each candidate’s entire application, and not rely too
heavily on only one element.

Institutional bias 13 14

Another common bias in peer review is institutional bias. Institutional bias refers to bias that
occurs due to the reputation of the institution the size, type, or location of the institution or
prior research conducted by that institution.
Prestige bias and affiliation bias can influence institutional bias as well—either due to thestatus of the institution or due to a reviewer’s current or previous affiliation with theinstitution.
Examples of institutional bias from reviews have focused on the availability of resources, thesize of the institution and collaboration with other institutions.

Read the application and then answer the question:
A research proposal has been submitted by a principal investigator at a small institution withlimited access to resources. The research proposal will study broadband internet systems inrural locations across Canada.

Which of the following is an unbiased response to the research proposal?
A. Given the size of the institution, the applicant has limited training experience and is
unsuitable for this grant.
B. The institution does not have a viable research capacity due to its size and will not beable to conduct the research proposed successfully.
C. The affiliated institution lacks resources needed to support this research and theapplicant has not secured these resources elsewhere.
D. The institution should not support a grant because of its size and limited capacity.

Correct answer:
The answer is C. Depending on the nature of the program, access to environments in other organizations
may be essential to deliver on the objectives of the program. Reviewers should consider thatappropriate support environments may be found within an institution, or may be built through“networked” environments or platforms outside of the host institution. In this case, the institution does
not have the capacity to support the research, and the research has not secured appropriate support in
networked environments either.

Age bias
Age bias refers to bias that occurs due to the age of the applicant and can be directed topeople who are older or younger.
Read the application and then answer the question:
The principal applicant of an application is someone that you know is thinking of retiring
within the next couple of years. The application specifies a grant duration of five years.

In this situation, the possible retirement of the principal investigator isreason to reject the application
A. True
B. False

Correct answer:
False – Principal applicants can change for a number of reasons and processes are in place to addressthese circumstances. The applicants age and/or personal life circumstances should not be considered inassessing the quality of the application. In the event of retirement of a principal applicant, areplacement will be considered. When replacing the nominated principal applicant, the replacement isnormally chosen from among the existing principal applicants and co-applicants, as these sets ofexpertise have been reviewed by the peer review committee and contributed to the recommendation tofund the research project.

Indigenous bias
Indigenous bias can be experienced in a variety of ways including applying (explicitly or
unconsciously) assumptions on the quality, merit, value, relevance, importance, success andcompetence of either an Indigenous or non-Indigenous allied scholar, or Indigenous researchmethodologies, based on racial, historical, and cultural biases and stereotypes.
This is critical to be cognizant of when reviewing any works involving First Nations, Inuit, orMétis individuals (and/or communities).
Historically, the Indigenous peoples of Canada have been marginalized, disadvantaged, andexcluded from all mainstream opportunities, through continued systemic barriers,
assumptions, and biases.
There is a growing movement to create an equitable, fair relationship; these longstanding
biases are slowly being challenged and addressed.
With the federal acceptance of the Final Report of the Truth and Reconciliation Commission
and the signing of the UN Declaration on the Rights of Indigenous Peoples, a new respectful
relationship can be built among Indigenous peoples and Canada, confronting the nation’s oldbiases.
It is therefore of utmost importance, through the peer review process, that thereviewer have the knowledge, expertise, openness, and humility to be able to fairly assess
an application based on Indigenous Ways of Knowing or using Indigenous researchmethodologies.
The reviewer must have respect for, knowledge of, and uphold the TCPS-2
Chapter 9 guidelines in reviewing an application involving Indigenous Peoples.

Language bias 26 27 28
Canada’s linguistic duality can give rise to bias during peer reviews, in particular for
francophones. First of all, scientific culture not only in Canada but also throughout the world
increasingly places greater value on research conducted and published in English.
This is against the principle of respect for Canada’s linguistic duality, as well as the principlesof transferring knowledge to and exchanging knowledge with local populations.
Furthermore, if French applications are reviewed by committee members who do not haveexcellent French skills, their understanding of the proposal may be impacted. This may affecttheir confidence in their comprehension and assessment, and subsequently implicit preference
to review in their own first language. Applicants and reviewers may also experience concern(or stereotype threat) with their second language skills being evaluated by their peers, andthis can reduce their performance.
Research also shows that one cause of linguistic bias is “editors and reviewers having little
awareness of and sensitivity to the challenges of nonnative English-speaking contributors inwriting in English for publication.
Reviewers’ assessments may be impacted by their impression of what constitutes well-written
applications if the quality of the writing does not meet their expectations.

Objective 3
In this section, we will learn about how to integrate methods for mitigating the influence ofunconscious bias.

Identifying bias 15 16
It is important to take steps to mitigate bias. In order to do so, you must first recognize thatyou have biases.
Identify what those biases are and take steps to mitigate bias in your thought process. The
following will offer you some tools to help mitigate bias.

Implicit Association Test
Disclaimer: The Implicit Association Test is an optional part of this learning module, andis externally designed and run by Project Implicit. Please read the information provided onthe website linked below and click “I wish to proceed” if you decide to take the test. At theend of the test, Project Implicit asks for some demographic information. It is optional for
you to provide this information, and you can choose to bypass this section by pressing “OK. ” You will then receive the test results. CIHR, NSERC, SSHRC and the Tri-agency
Institutional Programs Secretariat do not collect any data from this test.
Please take some time to do the Implicit Association Test.
The Implicit Association Test measures the strength of associations between concepts andevaluations or stereotypes. The main idea is that making a response is easier when closely
related items share the same response key.
Once you have identified potential sources of unconscious bias, continue reading to learn how
to mitigate biases.

How to mitigate bias 17 18 19 12
The steps below are evidence based behavioural strategies that can be practiced while
reading an application to mitigate bias in the review process.

•Stereotype replacement: Think about a stereotype that you hold and consciously replace
it with accurate information.

•

Positive counterstereotype imaging: Picture someone who counterstereotypically fills a
traditionally stereotyped role.

•

Perspective taking: Take the perspective of someone in a stereotyped group (example
below).

•

Individuation: Gather specific information about an applicant to prevent groupstereotypes from leading to potentially inaccurate assumptions (example below).

The following scenario is an example of perspective taking:
Before evaluating a research proposal written by a member of a minority group, imagine indetail what it is like to be a person in a stereotyped group. For example, imagine what it islike to be an Indigenous researcher and to be questioned on the validity of Indigenous
research methodologies, based on racial, historical, and cultural biases and stereotypes.

The following scenario is an example of individuation:
A late career female investigator has submitted a research proposal. While reviewing her
application, make sure you gather specific information about the applicant in order to prevent
group stereotypes from leading to potentially inaccurate assumptions. Use the adjudication

criteria and sub-criteria outlined in the peer review manual to gather the facts andinformation about the applicant.

Additional tips 12
Here are some additional tips for minimizing the influence of bias and assumptions.
Spend sufficient time evaluating each applicant. Studies have shown that evaluators who
were busy or distracted by other tasks gave women lower scores than men for the same
written evaluation of job performance.
Apply the criteria consistently to all applicants. Research shows that different standards may
be used to evaluate applicants of different genders.
Evaluate each candidate’s entire application. Don't rely too heavily on only one element of theapplication to evaluate an applicant—recall the linguistic analysis that revealed unconscious
gender bias.
Periodically evaluate your judgments and consider whether evaluation biases are influencing
your decisions; ask yourself questions such as:•

Are underrepresented candidates subject to different expectations or standards in order
to be considered as qualified as the majority?

•

Is research from smaller institutions or minority groups being undervalued?

•

Have accomplishments, ideas or findings of underrepresented candidates been unfairly
attributed to research directors or collaborators despite evidence to the contrary?

Conclusion
Being aware of unconscious bias is the first step in mitigating these sources of bias. Bylearning about unconscious bias, doing the Implicit Association Test and using the additional
resources provided, you can review applications in a manner that is more conscious, fair andavoids unequal outcomes.
Greater awareness of unconscious bias will lead to an improved review process and greater
parity in grant and award distribution.

Survey
Before concluding this module, please complete a survey to assist the Natural Sciences andEngineering Research Council (NSERC) and the Social Sciences and Humanities ResearchCouncil (SSHRC) in tracking the uptake, and improving the quality, of the learning.

Congratulations, you have now completed the learning on unconscious bias. You should now
be able to understand what unconscious bias is, understand how unconscious bias can impact
the peer review process and integrate methods for mitigating the influence of unconscious
bias in the review process.

Acknowledgments
A special thanks to all those at the Canadian Institutes of Health Research who developed theoriginal version of this module. Thanks in particular to:•

the CIHR Institute of Gender and Health, and Knowledge Translation program at the Li
Ka Shing Knowledge Institute at St. Michael’s Hospital in Toronto, Ontario, for theirdevelopment of the information relevant to gender bias;•

the CIHR Institute of Aboriginal Peoples’ Health, for their development of information
relevant to Indigenous bias;•

the Association des collèges et universités de la francophonie canadienne, for theirdevelopment of information relevant to language bias; and•the many people who reviewed the content, including (but not limited to) Dr. Carole Lee(University of Washington), Dr. Molly Carnes (University of Wisconsin) and Dr. Calvin Lai(Harvard University).

Resources
Training resources:•

Association of American Medical Colleges Unconscious Bias Training•

The Canada Research Chair Program’s Equity, Diversity and Inclusion Practices

•

Canada Research Chairs Program, Limiting Unconscious Bias•

CIHR Peer Review Manual for Grant Applications, 2016•

How to Survive Peer Review, BMJ Books•

Implicit Association Test•

NSERC’s equity, diversity and inclusion webpage•

SSHRC’s Manual for Adjudication Committee Members 2016-17•

SSHRC’s Guidelines for the Merit Review of Aboriginal Research•

Tri-Council Policy Statement on Research Involving the First Nations, Inuit and Métis
Peoples of Canada

Additional online/training resources:•

Bornmann, Lutz, Rüdiger Mutz and Hans-Dieter Daniel (2012), “Does gender matter in
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•

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•

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•

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•

Drummond, Rennie, Annette Flanagin and Fiona Godlee (2015), The Eighth International
Congress on Peer Review and Biomedical Publication: A Call for Research. The JAMA
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•

Emerson, Gwendolyn B., et al. (2010), Testing for the Presence of Positive-Outcome Bias
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•

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•

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Recommendations and Editorial Decisions, The JAMA Network 272(2):137-138.
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•

Gender Bias Learning Project•

Hames, Irene (2007), Peer Review and Manuscript Management in Scientific
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•

Hopewell, Sally, et al. (2014), Impact of peer review on reports of randomised trials
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•

Kaatz, Anna, Belinda Gutierrez and Molly Carnes (2014), Threats to objectivity in peer
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•

Leading Women, Unconscious Bias at the Office – Does Your Company Have a Plan?

•

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practices,” Research Evaluation, 21:48–60.

•

Miller, David I., Alice H. Eagly and Marcia C. Linn (August 2015), “Women’s
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nations,” Journal of Educational Psychology, Vol. 107(3), 631-644.

•

National Collaborating Centre for Aboriginal Health — Aboriginal Research Designs•

Science Magazine (2015), A little bias in peer review scores can translate into big money,
simulation finds.

•

Smith, Richard (2006), “Peer review: a flawed process at the heart of science andjournals,” Journal of the Royal Society of Medicine, 99(4):178-182.

•

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