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News & Press: ABTJ Article

Disrupting Implicit Bias

Tuesday, July 16, 2019   (0 Comments)
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By: Hon. Bernice B. Donald, Judge, U.S. Court of Appeals for the 6th Circuit and Sarah E. Redfield, Professor of Law, University of New Hampshire*

Key Points

  1. Implicit bias is real and it matters.
  2. Implicit bias contributes to disproportionality and unfairness in many areas of society, including bankruptcy decisions.
  3. Being motivated to limit impact of implicit bias in critical decisions works. Training works.
  4. Become aware, take the test, IAT, https://implicit.harvard.edu/.
We say doctor, what do you say? You are far more likely to say nurse than bluebird. Quick! We say: Picture a doctor. You are far more likely to picture a white male than, for example, a woman of color. Recall the airline attendant who, when seeking emergency medical assistance, for a passenger, rejected the African American, female doctor who volunteered, saying, that an actual doctor was needed.1 One more: we say Picture a lawyer. More likely than not, your first response was a picture of a white male. For both doctors and lawyers, the white male pictures would be statistically accurate. For lawyers, according to the Bureau of Labor,2 88% of U.S. attorneys are white and 62% are male. (Numbers for judges, magistrates, and other judicial workers are similar, 85% white and 68% male, for bankruptcy trustees, xxxx). But, we said, Quick! Did you consciously think about what you knew about Bureau of Labor statistics and consider your picture? It is far more likely that your brain responded to the prime, lawyer, unconsciously (or as some scientists prefer indirectly or implicitly), calling up an image using mental shortcuts called schema.3

So, what does this have to do with bankruptcy and bankruptcy trustees? This introductory exercise sets the stage for understanding how our brains work both implicitly and explicitly, what some researchers describe as Thinking Fast/Thinking Slow, System 1/System 2 Thinking.4 This in turn offers new science-based insight into how we make our decisions, and this insight helps explain the kind of disproportionalities and inequities we see manifested in society. This new science starts from a position of no blame where no one is called an -ist of any kind, but where the manifestations and other evidence of inequities can be addressed.5

We know these manifestations only too well. Most often cited is the criminal justice data, for example, the fact that the prison population is disproportionate to the population and over five times more African American than white.6 For another example, consider the data from the Office for Civil Rights showing that in preschool African American children are suspended or expelled far more often than their white peers and far out of proportion to their representation in the population (18% of the preschoolers, 48% of preschoolers suspended more than once).7

Some of the data in bankruptcy filings offers analogous statistics. For example, researchers Jean Braucher and her colleagues asked consumer bankruptcy attorneys what advice they would give a couple; they were presented with a description of ambiguous financial information (such that filing chapter 7 or 13 were equally likely). The results? Reggie and Latisha were advised to file chapter 13 more than twice as often as Todd and Allison.8 This is a classic kind of study, where facts are held equal and the prime and schema of a race-oriented name produces different results.9 On a broader scale, Paul Kiel and Hannah Fresques, writing in ProPublica, concluded that “Black people struggling with debts are far less likely than their white peers to gain lasting relief from bankruptcy.”10 Other researchers have put the difference at half: “Blacks have less than half the chance of bankruptcy success as non-blacks; this worsens the recent insight that blacks are overrepresented in bankruptcy because of attorney steering to chapter 13,” 11 where “only one variable significantly predicted chapter choice—race, as measured by the racial composition of the debtor’s zip code.”12 In other words data analysis is clear: “More than amount of debt, prior bankruptcies, or having a job—all features that the bankruptcy system does account for in considering a person’s eligibility for chapter 13—race matters.”13 While these analyses attribute much of this to lawyers and to the choice between chapter 7 and 13,14 the culture of bankruptcy proceedings more generally also plays a role. Trustees can, for example, look more closely at the filings, can recognize when chapter 13 proceedings are apt to fail, especially given five-year settlements. They can perhaps look as (or more) closely to the debtor’s situation than to the creditors and attorneys’ fees.

Whatever the source of these kinds of disproportionalities, it’s important to recognize that they do not necessarily reflect real behavioral differences.15 Nor do they reflect that school administrators, teachers, lawyers, judges, or bankruptcy trustees, are racially biased.16

What is the explanation, then, for this kind of intransigent disproportionality? While explanations are multifaceted and grounded in long-standing culture and practice, one explanation from the emerging science is that these disproportionalities result in part from implicit bias: “As psychology research in recent decades suggests, one reason for this divide is that much discrimination may be driven by implicit bias rather than explicit prejudice.”17 We may respond implicitly to a client or petitioner with a mental schema of a black debtor just as we responded to the prime doctor with a picture of a white male, that is, before we focus (if we do) consciously on that person and situation in an individualized way.

Like the prime and response exercise we started with, implicit bias and its correlates in group dynamics and communication are all about our quick, indirect unconscious responses. Implicit bias is defined as learned associations that affect our understanding, actions, and decisions. These associations are absorbed from all around us from a very early age, and they are outside direct awareness or control. In contrast, explicit bias is defined as deliberately generated understanding, actions, and decisions. These decisions are verbally endorsed evaluations, directly experienced as our own.18 It used to be that if we wanted to know if you were biased, we asked, Are you biased against poor people? Most likely you answered that you were not, because you sincerely believe you are not biased or because you do not want to appear biased or even because you do not know you are biased; but whatever the cause, these representations were apt to be inaccurate.19

Starting in the mid-1990s, psychologists developed a new approach, one that measured instead of asked.20 The most common measure is the Implicit Association Test (IAT). The IAT is an online test that measures the speed of your response pairing a prime (word/picture, like the prompt lawyer) with another word (for example, good/bad). The speed of your response shows how comfortable you are with an association.21 The comparative result reflects your implicit bias. Millions of people taking the IAT have shown that implicit biases are pervasive.22 Aggregated results indicate that a majority demonstrate an automatic preference for the dominant group, European American over African American, women and families over women and careers, young over elderly, abled over disabled, etc.23

We urge everyone to take the IAT available at https://implicit.harvard.edu/. There are many test choices: race, age, weight, working-women, etc. The test takes about ten minutes, and at the end you get a message about your results. For example, you might learn that you show a slight bias in favor of women and families as compared to women and careers (Professor Redfield’s results). These results can be surprising or even disturbing. (Professor Redfield has worked since she was 14 and often with very successful professional women). So, a heads up: take your results as information to consider, to increase your own awareness, nothing more.

But more important than IAT results is the more basic idea—a game-changer: the understanding that as humans we can and do hold two views at the same time, and we may be influenced by our implicit view without our awareness. This insight from the IAT and similar efforts is supported by the physical neuroscience research as well.24 So when Professor Redfield says she is a strong supporter of women lawyers and judges, she is telling us the truth, her consciously held view (explicitly), and at the same time she may have implicit biases that mediate in favor of women and families. To reiterate, both are true at the same time.

This means that we may be making decisions where our implicit bias unbeknownst to us leads to a result different from our conscious intention—what scientists describe as an action disassociated from our consciously held attitudes and beliefs. As Dr. Mahzarin Banaji and her colleague Dr. Calvin Lai describe this discovery:

 

Using a variety of methods to get at these associations has led to a striking set of discoveries. Among the most central of these discoveries is that within the same individual mind there exists multiple actors: a deliberative decision-maker who aspires to egalitarian ideals and a less conscious partisan who is attentive to the similarity, familiarity, and social standing of those who are judged.25

 

Do we believe that the bankruptcy figures mentioned above are because the decisionmakers along the way are biased against individuals because of their race or ethnicity or biased against people on the basis of their socioeconomic status? We do not. Rather we acknowledge that we are all human, that as humans we are implicitly biased, and that it is this implicit bias that cumulates and manifests itself negatively without our conscious intention.

We note here one other point of particular relevance to legal decisionmakers. The science shows we are most likely to respond with implicit bias and rely on our stereotypes in certain situations: those where we have the opportunity to exercise discretion and those where the information is ambiguous.26 This is, of course, exactly what lawyers and judges and trustees do all the time. Given this tendency to fall into stereotypes in these situation, it is all the more important for us to be particularly mindful, what we call, taking time to stare at the information/situation before us; and it is also all the more reason for us to ask how else this implicit bias can be interrupted, and how.

Implicit bias can be successfully interrupted. We know this from training efforts that have documented changed results. For just one example, training judges about implicit bias and providing tools to help limit discretion, produced a measurable change in their juvenile detention decisions to meet the stated goal of keeping children with their families over an extended period of time.27 Good training that is data and science-driven can help us learn when to focus on our quick, System 1 implicit responses and use our slower, conscious System 2 thinking to reach a sound result.28 Good training can offer strategies for interruption. We make a few suggestions here and invite folks to reach out to us for more detail on actual approaches or on training suggestions:29

1.  Become aware. This article is a short beginning, but do more: read more, observe more. Notice times when you might be reacting implicitly when you meet a new person. Take time to ask a few more questions, clarify ambiguity.

2.  Decide in your own context what decision points may be places where bias need be interrupted. We are all cognitive misers, saving our mental energy;30 for each person/institution focus points will vary.

3.  Be mindful of confirmation bias, watch for how you respond to ambiguity. Know that there is a strong tendency toward confirmation bias, where we tend to hear and pay attention more to information that confirms our view and to disregard information that doesn’t. Ask for more information, slow down at key decision points.

4.  Be mindful around discretion. Consider where you can limit your discretion so as to limit opportunities to let implicit bias or stereotypes take over without your conscious intent.31 Take for example, the blinding approach used in orchestras, where once auditions were held behind screens increasing numbers of women who got seats.32 While we can’t make all our decisions with such a dramatic approach, we can try to limit unnecessary cues that could prompt implicitly biased responses.

5.  Be watching your messaging. Take for example, the well-documented impact of negative micromessages—small messages that implicitly send signals that can cumulate in negative results—and change them as you can: shake hands with everyone if  you shake hands with anyone; call everyone by title, Mr. Smith, Ms. Smith, Attorney Smith. 33

As we said, these are a very few, fairly simple, pointers; they come from data-based work on successful approaches for training to interrupt implicit bias. There are others that are more complex and more systemic in nature that need to await a longer article and approach.

But here is #6. Be trained.

 

ENDNOTES:

A note on notes: One aspect of implicit bias is invisibility of the stigmatized individual. We make a small step toward addressing this by trying to include all authors in notes rather than reverting to the Bluebook suggested et al.

* Bernice Donald, U.S. Court of Appeals for the 6th Circuit (and former judge for the U.S. Bankruptcy Court for the Western District of Tennessee) and Sarah Redfield, Professor of Law, University of New Hampshire, worked together on the ABA’s book on implicit bias, which work underlies this piece. Enhancing Justice: Reducing Bias (Sarah E. Redfield ed., 2017). The authors are co-chairs of the ABA Criminal Justice Section’s Implicit Bias Initiative.

1   
Christine Hauser, Black Doctor Says Delta Flight Attendant Rejected Her; Sought ‘Actual Physician’, NY Times, Oct. 15, 2016.

2   
Bureau of Labor, Labor Force Statistics from the Current Population Survey, Household Data, Annual Averages, Table 11. Employed Persons by Detailed Occupation, Sex, Race, and Hispanic or Latino Ethnicity (for doctors/surgeons, 70.8% white and 60% male).

3   
Prime, Psychology, http://psychology.iresearchnet.com/social-psychology/social-cognition/priming/.

4   
Daniel Kahneman, Thinking Fast and Slow (2011).

5   
Corinne A. Moss-Racusin, Jojanneke van der Toorn, John F. Dovidio, Victoria L. Brescoll, Mark J. Graham & Jo Handelsman, A “Scientific Diversity” Intervention to Reduce Gender Bias in a Sample of Life Scientists, 15 Life Sci. Educ. 1 at Table 1 (2016).

6   
E.g., Alison Walsh, The Criminal Justice System Is Riddled with Racial Disparities (Aug. 15, 2016), https://www.prisonpolicy.org/blog/2016/08/15/cjrace/; Ashley Nellis, The Color of Justice: Racial and Ethnic Disparity in State Prisons 3-4 (2016).

7   
U.S. Dep’t of Educ. Off. for C.R., Civil Rights Data Collection, Data Snapshot: School Discipline. Preschool Students Receiving Suspensions, by Race and Ethnicity 7 (2014), http://ocrdata.ed.gov/Downloads/CRDC-School-Discipline-Snapshot.pdf.

8   
Jean Braucher, Dov Cohen & Robert M. Lawless, Race, Attorney Influence, and Bankruptcy Chapter Choice, 9 J. Empirical Legal Stud. 393, 395-96 (2012).

9   
Marianne Bertrand & Sendhil Mullainathan, Are Emily and Greg More Employable than Lakisha and Jamal? 94 Am. Ec. Rev. 991 (2004).

10 
Paul Kiel & Hannah Fresques, Data Analysis: Bankruptcy and Race in America, ProPublica Sept. 27, 2017, https://projects.propublica.org/graphics/bankruptcy-data-analysis [https://perma.cc/KK6C-K75X]; see also Annie Waldman & Paul Kiel, Racial Disparity in Debt Collection Lawsuits: A Study of Three Metro Areas ProPublica, Oct. 8, 2015.

11 
Sara S. Greene, Parina Patel & Katherine Porter, Cracking the Code: An Empirical Analysis of Consumer Bankruptcy Outcomes, 101 Minn. L. Rev. 1031, 1036, 1062 (2017).

12 
Robert M. Lawless & Angela Littwin, Local Legal Culture from R2D2 to Big Data, 96 Tex. L. Rev. 1353, 1357 (2018).

13 
Sara S. Greene, Parina Patel & Katherine Porter, Cracking the Code: An Empirical Analysis of Consumer Bankruptcy Outcomes, 101 Minn. L. Rev. 1031, 1036, 1062 (2017). {{OR supra note 11 at 1036, 1962}}

14 
Jean Braucher, Dov Cohen & Robert M. Lawless, Race, Attorney Influence, and Bankruptcy Chapter Choice, 9 J. Empirical Legal Stud. 393, 395-96 (2012). {{OR supra note 8 at 395-6}}

15 
Walter S. Gilliam, Angela N. Maupin, Chin R. Reyes, Maria Accavitti & Frederick Shic, Do Early Educators’ Implicit Biases Regarding Sex and Race Relate to Behavior Expectations and Recommendations of Preschool Expulsions and Suspensions? Yale Research Study Brief (2016).

16 
See Jeffrey J. Rachlinski, Chris Guthrie & Andrew Wistrich, Inside the Bankruptcy Judges' Mind, 88 B.U. L. REV. 1227, 1230 (2006).

17 
B. Keith Payne, Laura Niemi & John M. Doris, How to Think About “Implicit Bias,” Sci. Am., March 27, 2018.

18 
Bernice B. Donald & Sarah E. Redfield, Framing the Discussion in Enhancing Justice: Reducing Bias 13-14 (Sarah E. Redfield ed., 2017).

19 
David M. Amodio & Saaid A. Mendoza, Implicit Intergroup Bias: Cognitive, Affective, and Motivational Underpinnings 4 (2010), http://www.psych.nyu.edu/amodiolab/Publications_files/Amodio_Mendoza_Implicit_Intergroup_Bias.pdf.

20 
Anthony G. Greenwald, Debbie E. McGhee & Jordan L.K. Schwartz, Measuring Individual Differences in Implicit Cognition: The Implicit Association Test, 74 J. Personality & Soc. Psychol. 1464, 1465 (1998).

21 
Project Implicit, https://implicit.harvard.edu/implicit/
iatdetails.html.

22 
E.g., Brian A. Nosek, Frederick L. Smyth, Jeffrey J. Hansen, Thierry Devos, Nicole M. Lindner, Kate A. Ranganath, Colin Tucker Smith, Kristina R. Olson, Dolly Chugh, Anthony G. Greenwald & Mahzarin R. Banaji, Pervasiveness and Correlates of Implicit Attitudes and Stereotypes, 18 Eur. Rev. Soc. Psychol. 36, 36 (2007).

23 
Bernice B. Donald & Sarah E. Redfield, Implicit Bias: Should the Legal Community Be Bothered?, 2 PLI Current 615, 619(2018).

24 
Matthew D. Lieberman, Social Cognitive Neuroscience: A Review of Core Processes, 58 Ann. Rev. Psychol. 259, 262 (2007).

25 
Calvin K. Lai & Mahzarin R. Banaji, The Psychology of Implicit Intergroup Bias and the Prospect of Change 4 (2018), http://www.people.fas.harvard.edu/~banaji/research/publications/articles/2017_Lai.pdf.

26 
Id. at 10-11. {Lai & Banaji}

27 
Jesse Russell & Alicia Summers, Reflective Decision-Making and Foster Care Placements, 19 Psychol. Pub. & Law 2 (2013).

28 
Bernice B. Donald & Sarah E. Redfield, Implicit Bias: Should the Legal Community Be Bothered?, 2 PLI Current 615, 625-26 (2018). {{OR Donald supra note 23 at 625-26}}

29 
Sarah E. Redfield & Bernice B. Donald, Ten Tips for Interrupting Implicit Response (Bias) (on file with authors).

30 
E. Philip Tetlock, Accountability: A Social Check on the Fundamental Attribution Error, 48 Soc. Psychol. Q. 227, 228 (1985).

31 
Mark W. Bennett, Manifestations of Implicit Bias in the Courts in Enhancing Justice: Reducing Bias 65 (Sarah E. Redfield ed., 2017).

32 
Claudia Goldin & Cecilia Rouse, Orchestrating Impartiality: The Impact of “Blind” Auditions on Female Musicians, 90 Am. Econ. Rev. 715 (2000).

33 
Mary Rowe, The Saturn’s Rings Phenomenon, 50 Harv. Med. Alumni Bull. 14 (1975).


 

About the Authors

Hon. Bernice Donald is a judge on the U.S. Court of Appeals for the 6th Circuit. She was nominated to that position by President Barack Obama and was confirmed by a vote in the Senate on September 6, 2011. Prior to that, Judge Donald sat on the U.S. District Court for the Western District of Tennessee. She was appointed to the district court by President William Jefferson Clinton in December 1995. She was sworn into office in January 1996. She previously served as judge of U.S. Bankruptcy Court for the Western District of Tennessee, becoming the first African American woman in the history of the United States to serve as a bankruptcy judge. In 1982, she was elected to the General Sessions Criminal Court, where she became the first African American woman to serve as a judge in the history of the State of Tennessee. She received her law degree from the University of Memphis School of Law where she has served as an adjunct faculty member. She also serves as faculty for the Federal Judicial Center and the National Judicial College. In 1996, Chief Justice Rehnquist appointed Judge Donald to the Judicial Conference Advisory Committee on Bankruptcy Rules where she served for six years. She is extremely active in the American, Tennessee, and Memphis Bar Associations, serving in vital leadership roles in key committees. She currently serves as secretary of the 430,000 member American Bar Association estate and business planning.

Sarah Redfield is professor emerita at the University of New Hampshire School of Law and affiliate professor at the University of New Hampshire College of Education and Women’s Studies Program. She is a member of the Maine Bar.

Education law is her primary practice and teaching area. Her research and scholarship are focused on diversity and inclusion in the legal profession and along the education pipeline. Her current work continues her long-standing interest in Diversity, Equity & Inclusion and concentrates on implicit bias and on strategies to interrupt that bias and reduce the negative consequences of its manifestations in legal, medical, education, and workplace environments.

With Judge Bernice Donald, Professor Redfield is co-chair of the Criminal Justice Section Implicit Bias Initiative. She also currently serves on several high-level American Bar Association (ABA) diversity initiatives including the Diversity and Inclusion Advisory Council; the ABA Commission on Disability Rights; and the Criminal Justice Section Women’s Task Force.