1. The Science of Unconscious Bias
Toni Schmader
Department of Psychology
University of Arizona

2. Outline of Presentation
Understanding unconscious associations
Demonstration of our biases
How unconscious bias affects our behavior
Breaking free of biases

3. 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)
I look around the room and see a group of people are in the business of using of the reflective system. As academics, we like to think of ourselves as reflective thinkers. The type of people who always act based on conscious consideration of the evidence. But we need the reflective system as much of the time our brain runs on autopilot. This is easily seen for everyday tasks. Like when your good intentions to stop at the market on the way home from work get foiled because your brain ran the more automatic routine of driving straight home.
This reflexive system is quite functional – it allows us to accomplish things on based on the well-practiced routines or automatic associations we have learned from years of experience navigating our social worlds.
But when it comes to our interactions with others, our more automatic and reflexive system can sometimes leads to biases, that our more reflective system would not commit.

4. The Reflexive System Uses Implicit 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
Our brains are exquisitely skilled of observing co-variation in the world. When we see time and time again that the presence of dark clouds precedes the fall of rain, we learn to associate clouds and rain. When the state legislature meets, we might learn to expect budget cues. The repeated exposure to this covariation means that over time, an implicit cognitive association is formed between these two concepts…such that when I say legislature, many of us might even wince in anticipation of the negative news we fear will follow.

6. LAUNDRY

8. The procedure is quite simple
The procedure is quite simple. First, you arrange things into different groups. Of course, one pile may be sufficient, depending on how much there is to do. If you have to go somewhere else 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.

10. XXXX
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COW

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

16. 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
Our brains are exquisitely skilled of observing co-variation in the world. When we see time and time again that the presence of dark clouds precedes the fall of rain, we learn to associate clouds and rain. When the state legislature meets, we might learn to expect budget cues. The repeated exposure to this covariation means that over time, an implicit cognitive association is formed between these two concepts…such that when I say legislature, many of us might even wince in anticipation of the negative news we fear will follow.

17. 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

18. 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)
Men and women showed equivalent tendency to unconsciously associate men with agency and women with warmth.
On a more conscious measure, men gender stereotypic associations were stronger than were women’s.
Greenwald et al. (2003) describe the scoring algorithm
for calculating the IAT effect in detail. It involves calculating
the difference in average response latency
between the two sorting conditions and dividing by the
standard deviation of all latencies for both sorting tasks.
Thus, the IAT score (called D) is a cousin of Cohen’s d
calculation of

19. 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
From method paper in PSPB. Data collected on the web (note, that some participants are excluded from each of these samples)

20. 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)
Phelps shows that amount of IAT bias predicts amount of amygdala activation when viewing (unfamiliar) black faces.
Avoidance studies show that people with strong IAT bias are faster to push a joystick away from them when presented with black faces vs. white faces; or sitting farther away from the person
Amodio and Devine show the difference between attitudes (predicting preference and behavioral association) and stereotypes (predicting expectancies). Performance ratings on essay the person presumably wrote or

21. His view of the Interaction Her view of the Interaction
Dovidio et al., 2002
His view
of the Interaction
Predicted What
Was Said
r = .36*
r = .40**
Degree of
Explicit Bias
“I’m not prejudiced”
Degree of
Implicit Bias
“Black = Bad”
Her view
of the Interaction
Predicted How it
Was Said
r = -.41**
r = .34*

22. 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 women’s weaker association with math
Even among math & science majors
Nosek et al. (2002)
Among men & women, independent = male bias predicts…
Rating assertive male candidate as more qualified
Rating assertive female candidate as lacking warmth
Rudman & Glick (2001)
Among female college students, math = male bias predicts…
An unconscious negative attitude toward math
Poorer performance on math SAT
Even among women majoring in math and science
Nosek et al. (2002)

23. A Two Strategy Solution
Change Implicit
Associations
Consciously
Override
Biases
Unconscious
Biases
Judgment &
Behavior

24. 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
Instead of having the frame of avoiding being prejudice, have the frame of approaching being fair.

25. Example When 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
someone who attended inner city schools and a large state university”

26. A Two Strategy Solution
Change Implicit
Associations
Consciously
Override
Biases
Unconscious
Biases
Judgment &
Behavior

27. 2) Changing Unconscious Bias
The effectiveness of education (Rudman et al., 2001)
Prejudice Seminar was taught by black male professor, control class was a methods course taught by a white female professor.
IAT measured at the beginning and end of semester.
Non-Black participants show significant decrease in negative implicit associations with blacks after taking the course.
Other analyses suggest change due to liking the professor and greater contact with Blacks.

28. 2) Changing Unconscious Bias
The effectiveness of education (Rudman et al., 2001)
The effectiveness of exposure (Dasgupta & Asgari, 2004)
Sample of college freshman women tested at start of freshman year and one year later
IAT is leadership = male
No difference between women at the two schools in IAT at time 1, but significant difference at time 2.
Replicate this effect in an experiment manipulating exposure to female leaders

29. 2) Changing Unconscious Bias
The effectiveness of education (Rudman et al., 2001)
The effectiveness of exposure (Dasgupta & Asgari, 2004)
Sample of college freshman women tested at start of freshman year and one year later
IAT is leadership = male
No difference between women at the two schools in IAT at time 1, but significant difference at time 2.
Replicate this effect in an experiment manipulating exposure to female leaders

30. 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

31. Questions, comments, insights?
Take other Implicit Associations Tests Online:

32. 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

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

34. 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