Implicit Bias & Policing Science and Implications

Jack Glaser Goldman School of Public Policy University of California, Berkeley Read these words COUCH STOOL SEAT PEW BENCH APPLE ORANGE SEED PIT JUICE GLASS CURTAINS BLINDS PANE SILL SLUMBER BED DOZE SNORE PILLOW Count down slowly by 7s from 140 to zero

140, 133, 126,… Word Memory Task Sky Sleep Seed Water Bench Fruit Word Memory Task Fire Chair Glass Street Window Pillow Test Words from our memory task: Sky Bench Glass Sleep Fruit Street Seed Fire Window Water Chair Pillow Schematic Memories • Test Words from our memory task: Sky Bench Glass Sleep Fruit Street Seed Fire Window Water Chair Pillow Heuristics & Biases

• Much Economic theory assumes a rational actor – That people will maximize gains and minimize losses • But some gains and losses are not material • And humans are prone to reasoning errors Heuristics & Biases • Just a few examples: – Availability Heuristic • People will make assumptions based on what they can think of (what is available in their memory) – Base Rate Neglect • People fail to take into account overall probabilities when making predictions about individuals – Sunk Costs • People will continue to pursue an inferior course of action because they have invested in it – Confirmation Bias • People will seek, attend to, and better remember information that supports their beliefs

HUMAN COGNITION (MEMORY AND JUDGMENT) IS “SCHEMATIC”

We perceive and recall things through existing networks of mental associations (schemas) “SOCIAL COGNITION” IS ALSO SCHEMATIC

We perceive and judge other people through schemas called stereotypes, and more evaluative filters, called prejudice. These are intergroup biases. How Bias Causes Discrimination • Darley & Gross (1983) – Subjects were led to believe child was low or high socio- economic status – No effect of SES on ratings of ability… – Unless, they watched a video of her test performance – Then high-SES child rated above average, low-SES child rated below average. Stereotypes bias the interpretation of ambiguous behavior Bias is Normal… • Stems from human tendencies to: –Categorize –Perceive correlations (stereotype) –Favor our own –Make judgments with incomplete information (or to conserve mental resources) …but not necessarily desirable. Nonconscious (“Implicit”) Bias • Two major components of consciousness: – Awareness • Explicit = awareness • Implicit = no awareness – Control • Intentional = deliberate (controllable) • Automatic = no control (involuntary) –Also limited energy and cognitive resources –Extremely rapid H_T H_T H_T Implicit/Automatic Cognition • Most of our memory processes (perception, attention, encoding, storage, and retrieval) operate automatically

• This is true for memory about people and groups, too Importance of Implicit Bias • Measures of implicit bias not only circumvent “self-presentation” efforts, but actually tap attitudes and beliefs people are not aware that they possess and/or have no control over. Implicit Association Test (IAT) • A convenient, highly replicable, and widely used measure of automatic associations/biases • Developed by Greenwald & Banaji • First published in 1998 (Greenwald, McGhee, & Schwartz)

www.projectimplicit.org FLOWER INSECT

MOTH FLOWER INSECT

ROSE UNPLEASANT PLEASANT

HELL UNPLEASANT PLEASANT

HEAVEN UNPLEASANT PLEASANT or or FLOWER INSECT

CENTIPEDE UNPLEASANT PLEASANT or or FLOWER INSECT

HAPPINESS UNPLEASANT PLEASANT or or FLOWER INSECT

DEATH UNPLEASANT PLEASANT or or FLOWER INSECT

LILY UNPLEASANT PLEASANT or or FLOWER INSECT UNPLEASANT PLEASANT or or INSECT FLOWER UNPLEASANT PLEASANT or or INSECT FLOWER

LILY UNPLEASANT PLEASANT or or INSECT FLOWER

PAIN UNPLEASANT PLEASANT or or INSECT FLOWER

JOY UNPLEASANT PLEASANT or or INSECT FLOWER

BEETLE Implicit Association Test (IAT) • Greenwald, McGhee, & Schwartz (1998) found: • People more likely to associate flowers with pleasant and insects with unpleasant • (but not entomologists) • Group Differences: • Japanese subjects more like to associate Japanese with good and Korean with bad • Korean subjects show the opposite • Accentuated by “cultural immersion” • White subjects associate White with good and Black with bad Implicit Biases Predict Behavior

• Employment discrimination (Rooth, 2007) • Resource allocation (Rudman & Ashmore, 2007) • Voting (Finn & Glaser, 2010) • Crime (Gray et al., 2005) • Suicidality (Nock et al., 2010) • Use of force (Glaser & Knowles, 2008) • Medical decisions (Green et al., 2008) Implicit Bias and Medical Care • Green et al. (2008, Jnl. Gen. Internal Med.): Implicit Bias and Medical Care • Green et al.: – Medical residents’ implicit preference for Whites over Blacks predicted tendency to recommend treating (with Thrombolysis) Whites more than Blacks.

White “Patient”

Black “Patient”

Treatment withThrombolysis Treatment LOW HIGH Implicit Anti-Black Bias “Shooter Bias” Correll et al. (2002): “The Police Officer’s Dilemma” “Shooter Bias” Correll et al. (2002): “The Police Officer’s Dilemma” Implicit Stereotype  Shooting

150

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-50 Shooter Bias (ms) Bias Shooter

-100

-150 Low High Race-Weapons Stereotype (RWS)

Glaser & Knowles (2008) H_T (From Eberhardt, Goff, Purdie, & Davies, 2004) • Subliminal race primes affect judgments of crime-related objects (Eberhardt et al., 2004) Do Police Stereotype?

• Police officers are normal human beings with normal human cognition; • Police are exposed to the same influences; • Police are therefore subject to stereotyping Yes, police stereotype too • Police officers subliminally primed with Black faces were faster to identify objects as a weapons (Eberhardt et al., 2004) Yes, police stereotype too • Plant & Peruche (2005, 2006) and Correll et al. (2007) found “shooter bias” in police samples Not Inevitable • James, James, & Vila (2016) Racial Bias in Criminal Justice Comes as No Surprise to Social Psychologists

Policy Implications

• Merely prohibiting discrimination is insufficient • Training has not proven effective • “De-biasing”? Reducing Implicit Bias • Lai et al. (2014): The “Competition” (Figure 1)

-.25 0 .25 .50 .75 Bias Reduction Effect (Cohen’s d) Reducing Implicit Bias • Lai et al. (2016): The Sequel (re-test later)

0 Drivers of Disparity

Example: Higher per % of capita rates of Blacks Arrested arrest for --minus-- TOTAL African % of DISPARITY Americans Whites Arrested (Some) Drivers of Disparity

Example: Deployment Patterns? Higher per Crime Category capita rates of Priorities? arrest for Officer Bias? African TOTAL Complainant DISPARITY Americans Bias? Educational Opportunities Higher rates Employment Opportunities of offending? Parental Incarceration Drivers of Disparity

Deployment Patterns Crime DECISION POINTS Category Priorities Surveil Stop Search Etc. Officer Bias TOTAL Complainant DISPARITY Bias

Higher rates of offending Disproportionate Attention Causes Disparity

Majority Group Minority Group Disproportionate Attention Causes Disparity Arrest Rate = 3 ÷ 60 = 5%

Both groups: 20% Offenders+ + + + o+ + o ++ Arrest Rate = 3 ÷ 30 = 10% +o ++ + + o + +o + + ++ + +o + o +o + + + + + + + +o + + Majority+ + Group

++ Minority + o + + + + + + o+ o + +Group++ + o + + o + +o+ + + + + + o + + + o+ 30… + 60… o + + + + + People People + + Simulating the Effects of Profiling

% of Offending Incarcerated Group Population Rate initially Stop Rate Minority 20% 10% 5% 12.5% Majority 80% 10% 5% 3.125% Total 100% 10% 5% 5% (From Glaser, JPAM, 2006) Profiling: Different Offending Rates

% of Offending Incarcerated Stop Rate Group Population Rate Initially Minority 20% 25% 12.5% 12.5% Majority 80% 6.25% 3.125% 3.125% Total 100% 10% 5% 5% Collateral Consequences

• Race-based policing (whatever the cause) inherently drives disparities

• Disparate outcomes of policing accrue to broader minority communities Collateral Consequences

Disparities in Cause broader impacts on • Contact • Stigma & Alienation • Citation • Financial cost • Arrest • Disenfranchisement • Use or Force • Trauma & mortality • Incarceration • Human capital, family & community coherence Best Hope for Lastingly Reducing Bias • Intergroup Contact • Well studied, robust effects • Pettigrew & Tropp (2006) meta-analysis • A generational process • Clear implications for “community- oriented policing” General Principles for Addressing Implicit Bias • Reducing the impact of implicit bias, rather than reducing the biases themselves. • True Blinding or “Cloaking” (if possible) • Make categories explicit • Heighten Accountability • Increase time and focus for decisions • Need motivation and opportunity • Reduce Discretion Reducing Discretion • More information-based judgments  Less stereotype-based judgments – American employers who conduct formal criminal background checks are more likely to hire African Americans (Holzer, Raphael, & Stoll, 2006) – Formal drug testing programs are associated with higher rates of hiring of African Americans (Wozniak, 2014) – US Customs circa 1998… Reducing Discretionary Searches Case: U.S. Customs circa 1999 • Raymond Kelly takes charge of Customs Service • Institutes changes in traveler searches • Reduces suspicion criteria from 43 to 6 (mostly behavioral) Reducing Discretionary Searches: U.S. Customs circa 1999 35000 16

30000 14 12 25000 10 20000 1998 8 15000 2000 6 10000 4

5000 2

0 0 Searches Hits % Hit Rate

From Ramirez et al. (2003) Reducing Discretionary Searches: U.S. Customs circa 1999

1998

Searches Hits Hit Rate

Black 6,141 365 5.9%

White 11,765 677 5.8%

Latino 14,951 209 1.4%

From Ramirez et al. (2003) Reducing Discretionary Searches: U.S. Customs circa 1999

1998 2000

Searches Hits Hit Rate Searches Hits Hit Rate

Black 6,141 365 5.9% 2,437 384 15.8%

White 11,765 677 5.8% 2,931 462 15.8%

Latino 14,951 209 1.4% 2,731 358 13.1%

From Ramirez et al. (2003) Case: NYPD Pedestrian Stops Per Year 700000

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0 2002 2004 2006 2008 2010 2012 2014 Pedestrian Stops …and what happens next NYPD, 2011 (peak year) 400000

350000 Stops 300000 Frisks 250000 Physical Force 200000 Arrests

150000 Summonses

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50000

0 Black Hispanic White Inferring Bias (Circumventing Benchmarks) Outcomes of Frisks, NYPD, 2011 16 14 Black 12 10 8 Hispanic 6 4 White

% of Those Frisked Those % of 2 0 Outcome (“yield rate”) disparities

• Indicate differential suspicion thresholds – If one group has lower hit rates, it strongly suggests they are subjected to a lower suspicion threshold NYPD Stops Per Year 700000

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0 2002 2004 2006 2008 2010 2012 2014 NYPD: % Contraband Per Stop

7%

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3% Blacks 2% Whites 1% Latinos 0% 2011 2012 2013 2014 2015 NYPD: % Weapons Per Stop

6%

5%

4%

3%

2% Blacks Whites 1% Latinos 0% 2011 2012 2013 2014 2015 NYPD: % Arrested Per Stop

25%

20%

15%

10% Blacks

5% Whites Latinos 0% 2011 2012 2013 2014 2015 General Principles for Addressing Implicit Bias • Reducing the impact of implicit bias, rather than reducing the biases themselves. • True Blinding or “Cloaking” (if possible) • Make categories explicit • Heighten Accountability • Increase time and focus for decisions • Need motivation and opportunity • Reduce Discretion

Extra Slides… Enforcement Rates for Males Ages 18-20, NYC

BLACK

100,000

HISPANIC (From Chauhan, Rate 100,000 per Rate WHITE Warner, Fera, Balazon, Lu, & Welsh, 2015, Figure 23) Year 2011