45Unconscious Bias is most extreme when…

7. Awards Inequity in Scientific SocietiesAwards are important indicators of career success and are key in recruitment, hiring,promotion, recognition, and tenure decisions. Since 2010, AWIS (Association for
Women in Science) has partnered with eighteen STEM disciplinary societies toresearch patterns in awards allocations, focusing on the influence of unconscious bias
in the selection procedures aimed at fostering gender equity in awards. The results
indicated that “Awards allocations are stratified along gendered lines: Women wereconsistently under-represented among recipients of scholarly and research awards andoverrepresented among recipients of teaching and service awards relative to their
proportion among PhD., full professors and disciplinary society membership” (Fig.3A).
The results indicated that unconscious biases based on social stereotypes influenced
the under- recognition of women for research and over-recognition of women forservices, teaching and mentoring
AWIS #02 in AWIS AWARDS Series (www.awis.org)

Biological and Life Sciences

Mathematical Sciences

60%

40%

30

2030%

Physical Sciences20

1991-2000 2001-2010 2011-2014% WomenScholarly Award Winners

01991-2000 2001-2010 2011-2014

0% Women
Teaching/Service Award Winners

Implicit Association Test (IAT)In the last 20 years, there have been a number of efforts to develop measures thatreflect “Implicit Bias/Unconscious Bias”. Implicit Association Test (IAT) is an example. IAT
measures implicit attitudes that one is not aware of. For example, one may believe thatwomen and men should be treated equally in leadership positions, but people’s
automatic response associates leadership positions with men more often than withwomen. There are various types of bias associated with gender, race, age, religion andeven weight.
An example of IAT can be found at:
Harvard’s “Project Implicit”: Dasgupta and Asgari (2004).
https://implicit.harvard.edu/Understanding
Unconscious Bias

Tools to Interrupt Unconscious Biases

100• Individuals are tired, rushed or cognitively burdened,• Individual demographic traits are rare in a group making decisions and/or agroup being evaluated• Valid quantitative performance information is lacking• Evaluation criteria are vague or ambiguous

1991-2000 2001-2010 2011-2014% Women
Tenure Track Faculty

Fig. 3A. Awards allocations are stratified along gendered lines
Refered from “AWIS #02 in AWIS AWARDS Series (www.awis.org)”

There are many on-line tools designed to interrupt unconscious biases. Below are
examples of tools offered by the United States universities. They were developed under
the United States National Science Foundation’s ADVANCE program(https://www.nsf.gov/ehr/Materials/ADVANCEBrochure.pdf).
University of Wisconsin-Madison
Women in Science & Engineering Leadership Institute (WISELI)
http://wiseli.engr.wisc.edu/
University of California, Berkeley
Tools for Change, University of California
http://www.toolsforchangeinstem.org/

8. Change Requires Continued Effort
Change is possible with continued, conscious efforts. Following critically reflectivemeetings with AWIS in 2010 and 2012, many societies implemented substantialchanges in their selection procedures that allowed for improvements leading towards
equity in making awards. However, awards cycles over time reveal that without
repeated and intentional efforts, it becomes easy to slip back into problematic andunconscious patterns. Repeated and intentional efforts are necessary for sustainable
and equitable change to be realized.
AWIS #02 in AWIS AWARDS Series (www.awis.org)

35%2515520102011201220132014% Women Biological and Life
Sciences Scholarly Awards% Women Mathematical
Sciences Scholarly Awards% Women Physical Sciences
Scholarly Awards% Women Biological and Life
Sciences Tenure Track Faculty% Women Mathematical
Sciences Tenure Track Faculty% Women Physical Sciences
Tenure Track Faculty

Fig.3B. Change is possible with continued, conscious efforts.
(Red arrows indicate when interventions were made.)

University of Washington, Seattle
The UW ADVANCE Center for Institutional Change
https://advance.washington.edu
Stanford University
The VMware Women's Leadership Innovation Lab at Stanford University(SEE BIAS/BLOCK BIAS)
https://womensleadership.stanford.edu/tools

Closing Remarks
As unconscious bias resides outside of awareness, it is not possible to completely
eliminate it, but, we can minimize its negative impacts by recognizing that we all haveunconscious bias, understanding how our biases can manifest, and avoiding situations
where unconscious bias can become extreme. We hope this leaflet will be helpful andvaluable in creating the inclusive environments where both men and women can realize
their full potential without encountering barriers and biases.

Acknowledgement
This leaflet is prepared by Dr. Hisako Ohtsubo, Nihon University, for the EPMEWSE
based on the Japanese leaflet (http://djrenrakukai.org/doc_pdf/2017/UnconsciousBias_leaflet.pdf) published in August 2017.

Refered from “AWIS #02 in AWIS AWARDS Series (www.awis.org)”

All rights reserved.
Unauthorized copying and reproduction of all the contents of the leaflet are strictly
prohibited. When you refer them, please cite as follows: “Understanding Unconscious
Bias” by EPMEWSE (RENRAKUKAI), March 2019.
https://www.djrenrakukai.org/doc_pdf/2019/UnconsciousBias_leaflet_eng.pdf

March 2019
Version 1.1EPMEWSE (RENRAKUKAI)
The Japan Inter-Society Liaison Association Committee for Promoting
Equal Participation of Men and Women in Science and EngineeringJapan
Copyright ⓒ 2017 EPMEWSE
Unauthorized copying and reproduction prohibited.

Introduction

Unconscious bias, also called implicit bias, is a form of stereotyping that is often
unintentional, automatic, and outside our awareness. All of us, both men and women,
are influenced by our experiences and make judgments without being explicitly awareof how our decisions are impacted by our unconscious. Unconscious bias links socialgroups with characteristics, such as gender, race, and religion, which generate relative
disadvantages for especially underrepresented groups, such as women in STEM
(Science, Technology, Engineering and Mathematics). As unconscious bias resides
outside of awareness, it is not possible to completely eliminate it, but it is possible tominimize its negative impact by recognizing that we all have it and by understanding
how it works.

Unconscious Bias is a Relatively New Concept.
Unconscious Bias is an idea that began to be widely recognized for its impact ondecision making around the year 2000.
Daniel Kahneman
Daniel Kahneman is an American psychologist, well known for his work on thepsychology of judgment and decision-making, as well as in behavioral economics, forwhich he was awarded the Nobel Prize in Economic Sciences in 2002. The concept ofunconscious bias appeared for the first time in his book* (*Heuristics and Biases: Thepsychology of Intuitive Judgment. Cambridge University Press).
In his more recent book, “Thinking Fast and Slow” (2011), he describes two different
ways that the brain forms thoughts: "System 1" is fast, automatic, stereotypic andemotional; "System 2" is slower, more effortful, more logical, and conscious. The bookdelineates cognitive biases associated with each type of thinking, from framing choices
to people's tendency to substitute a difficult question for one that is easy-to-answer,
which often leads to an error in judgment.
Virginia Valian
Virginia Valian is Distinguished Professor of Psychology at Hunter College. Her book,“Why So Slow?” was a breakthrough, having significant impacts on the women's
movement in early 2000’s. According to her, both men and women have unconsciousbiases about gender schemas, resulting in small differences in characteristics,
behaviors, and evaluations of men and women, which accumulate to generate
disparities between them.
“Valian's goal is to make the invisible factors that retard women's progress visible, sothat fair treatment of men and women will be possible.” (MIT Press, 1999)
Nancy Hopkins
Nancy Hopkins, Professor Emeritus at MIT, is known for her work in establishing thezebra fish as an experimental model system and identifying genes required for its
development. She is also well known for her effort to identify unequal treatment offemale faculty at MIT. She and her colleagues collected data and presented the evidence
of inequity to the MIT president, who acknowledged and corrected the inequities
among the faculty members at MIT. In her 2014 Boston University Graduation Speech,
she stated, “If you asked me to name the greatest discoveries of the past 50 years,
alongside things like the Internet and the Higgs particle, I would include the discovery
of unconscious bias” (May 18, 2014).

Unconscious Bias Schema
1. Stereotype Threat
Stereotype Threat results when an individual absorbs a widely held and fixed conceptthat is an oversimplified image of how members of a particular type or group act. For
example, preconception such as girls are naturally lacking in ability in mathematics gets
imbedded in the brain early in life, leading to girls not choosing science for their field ofstudy or occupation. Moreover, members of negatively stereotyped groups may actually
underperform, relative to their own ability, if they are reminded that they are membersof the stereotyped group. Thus, the existence of these stereotypes not only impact how
others judge women and members of other under-represented groups but also how theyjudge themselves leading to barriers against the full participation of women andmembers of other under-represented groups in the workplace.

22. Privilege
Privilege is a systemic form of advantage. The privileged take it for granted that they
deserve to get a more positive evaluation, therefore, it strongly linked to bias. It isinvisible and affects everyone, by often categorizing people into the group based on acertain attribute (gender, occupation, educational background, race and so on) andassuming that every member of that group is equally qualified or able. People tend totreat favorably those who belong to the same group as themselves, and to be suspiciousof those who do not. A typical example is a so-called "Old Boys’ Network" where it ishard for women to break into.

3

4. Science Faculty’s Gender Biases Favor Male Students
CVs for a Lab Manager application were studied. The science faculty reviewed thesame CVs with different names (genders). They rated “male” applicants as morecompetent, hirable, deserving of mentorship, and worth higher salaries than theidentically credentialed “female” applicants, whom they found more likable. Thispattern held for both male and female reviewers.
C.A. Moss-Racusin et al. (2012) PNAS, 109, 16474-16479

3. Micro-aggression, Subtle in Action, but Persistent
Micro-aggression comprises attitudes that are subtle, but persistent. It includes
everyday acts of exclusion that denigrate the capabilities of underrepresented groups.
Micro-aggression includes behaviors such as interruption, translation, misidentification(call by the wrong name, or have one’s name repeatedly mispronounced), exclusion, andmarginalization.

Unconscious Bias in Action
The following examples highlight evidence of unconscious bias in action. All reports
are based on research in the field of social sciences, cognitive sciences, behavioralsciences, and increasingly neurosciences.

Evaluation by Science
Faculty Members

15Competence, hireability, and mentoringby student gender condition

43(US$)
30,00028,000226,000

10

Mean starting salaryfor students

Competence

Hireability

Mentoring

24,000
MaleFemaleFig. 1 Science faculty’s subtle gender biases favor male students

1. Orchestrating Impartiality
This research analyzed whether gender biases could help account for the significant
underrepresentation of women in the United States orchestras, where women’s ratio
was around 5-10% in the 1970’s and 1980’s despite the fact that women comprised ofabout 45% of the top United States music school graduates. Some orchestras began
experimenting with a “blind” audition. Musicians were hidden behind a screen andjudges could not see the gender of the musician. The rate at which female musicianspassed the initial audition increased almost immediately.
Most major United States orchestras had changed their hiring policies by 2000.
Openings became widely advertised in the union papers, and many positions attracted
more than 100 applicants where fewer than 20 would have been considered before. At
present, ratio of women players reached 25%-46% in the top United States orchestras
C. Goldin and C. Rouse. (2000) American Economic Review 90, 715-741

2. Emily and Greg are More Hirable than Lakisha and Jamal!
This is a famous field research experiment designed to survey the differentialtreatment based on race still seen in the United State labor market. In order tomanipulate perceived race, researchers prepared identical resumes with randomly
assigned African-American names (such as Lakisha Washington or Jamal Jones) or
Caucasian-sounding names (like Emily Walsh or Greg Baker). The results showed thatapplicants assigned Caucasian names could expect on average one callback for every
ten advertisements applied to. On the other hand, those assigned African-Americannames would need to apply to about 15 different advertisements to achieve the sameresult. Caucasian names receive 50 percent more callbacks for interviews. This was atypical bias indicating that employers judged the applicant as unfavorable simply basedon the names which they perceived as having a disadvantaged background.
M. Bertrand and S. Mullainathan (2004) University of Chicago Graduate School of Business, NBER
and CEPRMIT and NBER

3. Motherhood Penalty?
Examiners reviewed information four candidates for the position of Associate VP ofFinancial Affairs. All had MBAs, were in their mid-30s, and had been performing equallywell. Applicants were a male parent, a female parent, a male non-parent and a female
non-parent. Circling a “Children” or “No children” response to a question indicatedparental status. Screening recommendation demonstrated that a father was considered
to be more able than a mother, and his starting salary was higher. The woman withoutchildren tends to get a higher rating than a father or a man without children, and was
twice more likely to be recommended for hire than the mother. It is evident that
unconscious bias against a mother, not a woman, played a role in the evaluation
process. No difference was seen in the evaluation result by the gender of the rater.
S. J. Correll, et al. (2007). Am J. Sociology, 112, 1297-1339.

5. Are Women Visible Enough in Scientific Societies?
Unconscious bias is present in the selection of speakers at the annual meetings ofthe Molecular Biology Society of Japan (FY2008, 2009 and 2010). This society is one of
the largest among life science related societies in Japan. Female ratio of the members
is about 30%. This survey shows how gender of organizers affected the percentage ofinvited female speakers at the annual meetings over the three-year period from 2008
to 2010. The percentage of the female speakers invited to symposia organized solely
by men was much smaller than those organized by both men and women. Female ratioof the speakers in the symposia organized by all-male committees were only 10%,whereas the ratio jumped up to 32% with the presence of just one female committee
member. Unconscious bias on the part of male scientists could be present whenevaluating their female colleagues.
M. K. Homma, et al. (2013). Genes to Cells. 18, 529-532.

Gender of organizers and a percentage ofinvited female speakers at the symposium
Organized by both men
Organized solely by men
and women by genderby gender

10%
050

100%
N=8232%
050
Men

100%
N=194
Women

M. K. Homma, et al. (2013). Genes to Cells. 18, 529-532.

Fig.2. Are Women Visible Enough in Scientific Societies?

6. Academic Recommendation Letter
This study found differences in letters of recommendation written for female andmale candidates for academic medical faculty positions. Letters written for women
were more likely to refer to their compassion, teaching, and effort as opposed to theirachievements, research, and abilities, which are the characteristics significantly
stressed for male applicants. The traits stressed for the women are based on cultural
female stereotypes, and are less valued for success in academic medicine.
F. Trix and C. Psenka (2003). Discourse & Society 12:191-220