The Science of Unconscious Bias Toni Schmader Department of Psychology University of Arizona.
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Transcript of The Science of Unconscious Bias Toni Schmader Department of Psychology University of Arizona.
The Science of The Science of Unconscious BiasUnconscious Bias
Toni SchmaderToni SchmaderDepartment of PsychologyDepartment of Psychology
University of ArizonaUniversity of Arizona
Outline of PresentationOutline of Presentation
Understanding unconscious associationsUnderstanding unconscious associations
Demonstration of our biasesDemonstration of our biases
How unconscious bias affects our How unconscious bias affects our behaviorbehavior
Breaking free of biasesBreaking free of biases
Being of Two MindsBeing of Two Minds
Reflective systemReflective system for controlled processing for controlled processing Conscious, explicitConscious, explicit Effortful, requires motivationEffortful, requires motivation Takes more timeTakes more time
Reflexive systemReflexive system for automatic processing for automatic processing Often unconscious, implicitOften unconscious, implicit Requires little effortRequires little effort FastFast
Different neural structures distinguish the Different neural structures distinguish the twotwo Satpute & Lieberman (2006)Satpute & Lieberman (2006)
The Reflexive System UsesThe Reflexive System UsesImplicit AssociationsImplicit Associations
Cognitive links between concepts that co-varyCognitive links between concepts that co-vary
Bring one to mind, others are activatedBring one to mind, others are activated
Activation can happen unconsciouslyActivation can happen unconsciously
...can be at odds with conscious goals...can be at odds with conscious goals
… …can influence attention, can influence attention, perception, perception, judgment and behaviorjudgment and behavior
The procedure is quite simple. First, you The procedure is quite simple. First, you arrange things into different groups. Of arrange things into different groups. Of course, one pile may be sufficient, course, one pile may be sufficient, depending on how much there is to do. If depending on how much there is to do. If you have to go somewhere else due to you have to go somewhere else due to lack of facilities, that is the next step; lack of facilities, that is the next step; otherwise you are pretty well set. It is otherwise you are pretty well set. It is important not to overdo things. That is, it important not to overdo things. That is, it is better to do too few things at once is better to do too few things at once than too many. At first the whole than too many. At first the whole procedure will seem complicated. Soon, procedure will seem complicated. Soon, however, it will become just another however, it will become just another facet of life.facet of life.
Unconscious Gender BiasesUnconscious Gender Biases Unequal gender distribution of men and Unequal gender distribution of men and
women in certain roles creates implicit women in certain roles creates implicit associationsassociations Eagly (1987); Glick & Fiske (1996)Eagly (1987); Glick & Fiske (1996)
With domains…With domains… Work = male; Family = femaleWork = male; Family = female Science = male; Arts = femaleScience = male; Arts = female
That generalize to traits…That generalize to traits… Male = independent, competentMale = independent, competent Female = cooperative, warmFemale = cooperative, warm
One Way to Measure One Way to Measure Unconscious BiasUnconscious Bias
The Implicit Association Test (IAT)The Implicit Association Test (IAT)Greenwald, McGhee, & Schwartz (1998)Greenwald, McGhee, & Schwartz (1998)
Measures strength of association Measures strength of association between conceptsbetween concepts
Based on premise that associated Based on premise that associated concepts will be easier to categorize concepts will be easier to categorize togethertogether
Microsoft PowerPoint Presentation
Men and Women both Show Men and Women both Show Implicit Gender BiasesImplicit Gender Biases
Association of math Association of math = male & = male &
arts = femalearts = female
Nosek et al. (2002)Nosek et al. (2002)
0.0
100.0
200.0
300.0
400.0
500.0
IAT
eff
ec
t (m
s)
Men
Women
Association of men Association of men = independent & = independent & women = communalwomen = communal
Rudman & Glick (2001)Rudman & Glick (2001)
0.0
0.5
1.0
1.5
Eff
ec
t S
ize
(d
)
Men
Women
Data on the IATData on the IAT(Nosek, Banaji, & Greenwald, 2005)(Nosek, Banaji, & Greenwald, 2005)
In comparison, effect size for gender differences in complex mathematical problem solving: d = .29
Hyde, Fennema, & Lamon, 1990
Implications for BehaviorImplications for Behavior Implicit Implicit racial biasesracial biases predict… predict…
Amygdala activation (fear response)Amygdala activation (fear response) Phelps et al. (2000)Phelps et al. (2000)
Lower performance ratingsLower performance ratings Amodio & Devine (2006)Amodio & Devine (2006)
Avoid the other groupAvoid the other group Amodio & Devine (2006); Phills & Kawakami (2005)Amodio & Devine (2006); Phills & Kawakami (2005)
More negative interactionsMore negative interactions Dovidio et al., (2002); McConnell & Leibold (2001)Dovidio et al., (2002); McConnell & Leibold (2001)
Predicted What
Was Said
Predicted How it
Was Said
Her view of the
Interactionr = -.41**
r = .40**
His view of the
Interaction
Degree of Implicit Bias“Black = Bad”
Degree of Explicit Bias
“I’m not prejudiced”
Dovidio et al., 2002
r = .36*
r = .34*
Implications for BehaviorImplications for Behavior Implicit Implicit gender biasesgender biases … …
Predict biased ratings of job candidates Predict biased ratings of job candidates Rudman & Glick (2001)Rudman & Glick (2001)
Might be manifested in letters of recommendationMight be manifested in letters of recommendation Schmader et al. (2008), Trix & Psenka (2003)Schmader et al. (2008), Trix & Psenka (2003) Men are more often described with superlatives & as having Men are more often described with superlatives & as having
abilityability Women are more often described as working hardWomen are more often described as working hard
Can contribute to women’s weaker association with Can contribute to women’s weaker association with mathmath
Even among math & science majorsEven among math & science majorsNosek et al. (2002)Nosek et al. (2002)
A Two Strategy SolutionA Two Strategy Solution
Unconscious Biases
Judgment &Behavior
Consciously OverrideBiases
Change ImplicitAssociations
1) Overriding Unconscious Bias1) Overriding Unconscious Bias
Be Be motivatedmotivated to control bias to control bias
Be Be awareaware of the potential for bias of the potential for bias
Take the Take the timetime to consider individual to consider individual characteristics and avoid stereotyped characteristics and avoid stereotyped evaluationsevaluations
ExampleExampleWhen writing evaluations, avoid:When writing evaluations, avoid:
1. Using first names for women or minority faculty and titles for men (Joan was anasset 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 hasnever 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 assomeone who attended inner city schools and a large state university”
A Two Strategy SolutionA Two Strategy Solution
Unconscious Biases
Judgment &Behavior
Consciously OverrideBiases
Change ImplicitAssociations
2) Changing Unconscious Bias2) Changing Unconscious Bias
The effectiveness of education The effectiveness of education (Rudman et al., (Rudman et al., 2001) 2001)
-200.0
-100.0
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200.0
Study 1 Study 2
Ch
ang
e in
IA
T e
ffec
t (m
s)
Control Class w/ White Professor
Prejudice Seminar w/ Black Professor
2) Changing Unconscious Bias2) Changing Unconscious Bias
The effectiveness of education The effectiveness of education (Rudman et al., (Rudman et al., 2001)2001)
The effectiveness of exposure The effectiveness of exposure (Dasgupta & Asgari, (Dasgupta & Asgari, 2004)2004)
2) Changing Unconscious Bias2) Changing Unconscious Bias
The effectiveness of education The effectiveness of education (Rudman et al., (Rudman et al., 2001)2001)
The effectiveness of exposure The effectiveness of exposure (Dasgupta & Asgari, (Dasgupta & Asgari, 2004)2004)
Take-Away PointsTake-Away Points
Implicit bias is distinct from conscious motivationImplicit bias is distinct from conscious motivation
We all have these biases due to cultural exposureWe all have these biases due to cultural exposure
They can affect behavior unless we override themThey can affect behavior unless we override them
They can be changed with education and They can be changed with education and exposureexposure
Questions, comments, insights?Questions, comments, insights?
Take other Implicit Associations Tests Online: https://implicit.harvard.edu/implicit/
Workplace ConversationsWorkplace Conversations
18 male and 18 female STEM faculty18 male and 18 female STEM faculty 88% response rate88% response rate
Electronically Activated Recorder (EAR)Electronically Activated Recorder (EAR) Sampled audio snippets during 3 workdaysSampled audio snippets during 3 workdays Participants complete workplace surveys of job Participants complete workplace surveys of job
satisfaction and disengagementsatisfaction and disengagement
CodingCoding Conversational snippets transcribed & coded Conversational snippets transcribed & coded
for contentfor content
Conversations with male 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
ConclusionsConclusions
Female faculty feel greater job Female faculty feel greater job disengagement and less satisfaction…disengagement and less satisfaction… to the degree that they discuss research and to the degree that they discuss research and
collaboration collaboration and and do notdo not discuss social topics discuss social topics
… …with their male with their male colleaguescolleagues