The Science of Unconscious Bias

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The Science of Unconscious Bias

• Toni Schmader
• Department of Psychology
• University of Arizona

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Outline of Presentation

• Understanding unconscious associations
• Demonstration of our biases
• How unconscious bias affects our behavior
• Breaking free of biases

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Being of Two Minds

• Reflective system for controlled processing
• Conscious, explicit
• Effortful, requires motivation
• Takes more time
• Reflexive system for automatic processing
• Often unconscious, implicit
• Requires little effort
• Fast
• Different neural structures distinguish the two
• Satpute Lieberman (2006)

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The Reflexive System UsesImplicit Associations

• Cognitive links between concepts that co-vary
• Bring one to mind, others are activated
• Activation can happen unconsciously
• ...can be at odds with conscious
  goals
• can influence attention, perception,
  judgment and behavior

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• LAUNDRY

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• The procedure is quite simple. First, you
  arrange things into different groups. Of course,
  one pile may be sufficient, depending on how much
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  due to lack of facilities, that is the next step
  otherwise you are pretty well set. It is
  important not to overdo things. That is, it is
  better to do too few things at once than too
  many. At first the whole procedure will seem
  complicated. Soon, however, it will become just
  another facet of life.

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Count the Number of Passes between White vs.
Black shirted Players
Neisser (1979)
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Unconscious Gender Biases

• Unequal gender distribution of men and women in
  certain roles creates implicit associations
• Eagly (1987) Glick Fiske (1996)
• With domains
• Work male Family female
• Science male Arts female
• That generalize to traits
• Male independent, competent
• Female cooperative, warm

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One Way to Measure Unconscious Bias

• The Implicit Association Test (IAT)
• Greenwald, McGhee, Schwartz (1998)
• Measures strength of association between concepts
• Based on premise that associated concepts will be
  easier to categorize together

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Men and Women both Show Implicit Gender Biases

• Association of math male
• arts female
• Nosek et al. (2002)

Association of men independent
women communal Rudman Glick (2001)
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Data on the IAT(Nosek, Banaji, Greenwald, 2005)
In comparison, effect size for gender differences
in complex mathematical problem solving d
.29 Hyde, Fennema, Lamon, 1990
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Implications for Behavior

• Implicit racial biases predict
• Amygdala activation (fear response)
• Phelps et al. (2000)
• Lower performance ratings
• Amodio Devine (2006)
• Avoid the other group
• Amodio Devine (2006) Phills Kawakami (2005)
• More negative interactions
• Dovidio et al., (2002) McConnell Leibold
  (2001)

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Dovidio et al., 2002
Predicted What Was Said
r .36
r .40
Degree of Explicit Bias Im not prejudiced
Degree of Implicit Bias Black Bad
Predicted How it Was Said
r -.41
r .34
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Implications for Behavior

• Implicit gender biases
• Predict biased ratings of job candidates
• Rudman Glick (2001)
• Might be manifested in letters of recommendation
• Schmader et al. (2008), Trix Psenka (2003)
• Men are more often described with superlatives
  as having ability
• Women are more often described as working hard
• Can contribute to womens weaker association with
  math
• Even among math science majors
• Nosek et al. (2002)

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A Two Strategy Solution
Consciously Override Biases
Change Implicit Associations
Unconscious Biases
Judgment Behavior
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1) Overriding Unconscious Bias

• Be motivated to control bias
• Be aware of the potential for bias
• Take the time to consider individual
  characteristics and avoid stereotyped evaluations

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ExampleWhen writing evaluations, avoid

• 1. Using first names for women or minority
  faculty and titles for men (Joan was an
• asset to our department. vs.- Dr. Smith was an
  asset to our department.)
• 2. Gendered adjectives (Dr. Sarah Gray is a
  caring, compassionate physician vs.
• Dr. Joel Gray has been very successful with his
  patients)
• 3. Doubt raisers or negative language (although
  her publications are not numerous
• or while not the best student I have had, s/he)
• 4. Potentially negative language (S/he requires
  only minimal supervision or
• S/he is totally intolerant of shoddy research)
• 5. Faint praise (S/he worked hard on projects
  that s/he was assigned or S/he has
• never had temper tantrums)
• 6. Hedges (S/he responds well to feedback)
• 7. Unnecessarily invoking a stereotype (She is
  not overly emotional He is very
• confident yet not arrogant or S/he is
  extremely productive, especially as

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A Two Strategy Solution
Consciously Override Biases
Change Implicit Associations
Unconscious Biases
Judgment Behavior
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2) Changing Unconscious Bias

• The effectiveness of education (Rudman et al.,
  2001)

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2) Changing Unconscious Bias

• The effectiveness of education (Rudman et al.,
  2001)
• The effectiveness of exposure (Dasgupta Asgari,
  2004)

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2) Changing Unconscious Bias

• The effectiveness of education (Rudman et al.,
  2001)
• The effectiveness of exposure (Dasgupta Asgari,
  2004)

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Take-Away Points

• Implicit bias is distinct from conscious
  motivation
• We all have these biases due to cultural exposure
• They can affect behavior unless we override them
• They can be changed with education and exposure

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Questions, comments, insights?
Take other Implicit Associations Tests Online
https//implicit.harvard.edu/implicit/
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Workplace Conversations

• 18 male and 18 female STEM faculty
• 88 response rate
• Electronically Activated Recorder (EAR)
• Sampled audio snippets during 3 workdays
• Participants complete workplace surveys of job
  satisfaction and disengagement
• Coding
• Conversational snippets transcribed coded for
  content

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Conversations with male colleagues Conversations with male colleagues Conversations with female colleagues Conversations with female colleagues
Male Participants Female Participants Male Participants Female Participants
Research talk
Job disengagement -.42a .72b .44bc -.18acd
Job satisfaction -.27a -.23abd .33bc .41c
Collaboration talk
Job disengagement -.26a .39b .51b .06ab
Job satisfaction -.24abc -.50ab .03abc .31ac
Social talk
Job disengagement .51a -.50b -.22bc .50ad
Job satisfaction .29a .58ab -.25ac -.29cd
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Conclusions

• Female faculty feel greater job disengagement and
  less satisfaction
• to the degree that they discuss research and
  collaboration
• and do not discuss social topics
• with their male
  colleagues