Unconscious Bias Training
About Unconscious Bias
Bias is a prejudice in favor of or against one thing, person, or group compared with another usually in a way that's considered to be unfair. Biases may be held by an individual, group, or institution and can have negative or positive consequences. There are types of biases 1. Conscious bias (also known as explicit bias) and 2. Unconscious bias (also known as implicit bias)
It is important to note that biases, conscious or unconscious, are not limited to ethnicity and race. Though racial bias and discrimination are well documented, biases may exist toward any social group. One's age, gender, gender identity physical abilities, religion, sexual orientation, weight, and many other characteristics are subject to bias.
Unconscious biases are social stereotypes about certain groups of people that individuals form outside their own conscious awareness. Everyone holds unconscious beliefs about various social and identity groups, and these biases stem from one's tendency to organize social worlds by categorizing.
Unconscious bias is far more prevalent than conscious prejudice and often incompatible with one's conscious values. Certain scenarios can activate unconscious attitudes and beliefs. For example, biases may be more prevalent when multi-tasking or working under time pressure.
Over the last three decades, our understanding of unconscious bias has evolved. The nature of unconscious bias is well understood, and an instrument (Implicit Association Test) to assess unconscious bias has been developed and rigorously tested.
Here's what we know:
- Unconscious biases develop at an early age: biases emerge during middle childhood and appear to develop across childhood (Dore, 2014).
- Unconscious biases have real world effects on behavior (Dasgupta, 2004).
- Unconscious biases are malleable-one can take steps to minimize the impact of unconscious bias (Dasgupta, 2013; Dasgupta & Greenwald, 2013).
A substantial amount of research has been published demonstrating impact of unconscious bias in various domains including the criminal justice system, education, and health/health care (Kirwan Institute, 2014). Bias may have an impact on: hiring, and mentoring and may contribute to healthcare disparities.
- Fictitious resumes with White-sounding names sent to help-wanted ads were more likely to receive callbacks for interviews compared to resumes with African-American sounding names. Resumes with White-sounding names received 50% more callbacks for interviews (Bertrand & Mullainathan, 2004).
- Science faculty rated male applicants for a laboratory manager position as significantly more competent and hireable than female applicants. Faculty also selected a higher starting salary and offered more career mentoring to the male applicant (Moss-Racusin et al, 2012).
- Among mentored career K08 or K23 recipients – mean salary of female researchers was about $31,000 less than males (Jagsi et al., 2013).
- Implicit bias among health care professionals can influence their behaviors and judgments (Stone & Moskowitz, 2011).
- Since 1997, more than 30 studies have been published relevant to unconscious bias and clinical decision-making. Racial bias is prevalent among healthcare providers and it appears that race influences medical decision making of healthcare providers (Paradies, 2013).
For many years, scientists have been working on instruments to assess unconscious bias (also known as implicit associations). Of the various tools that are available, the Implicit Association Test (IAT) is one of the most popular and well-studies. The IAT was developed as part of a project to detect unconscious bias based on several factors including race, gender, sexual orientation and national origin. It was developed as part of Project Implicit, which blends basic research and educational outreach in a virtual laboratory that allows users to exam one's own hidden biases and understand stereotypes that exist below one's conscious awareness. Project Implicit comprises a network of laboratories, technicians, and research scientists at Harvard University, the University of Washington and the University of Virginia.
How does the IAT work?
The IAT measures the relative strength of associations between pairs of concepts. It is designed as a sorting task in which individuals are asked to sort images or words that appear on a computer screen into one of two categories. The basic premise is that when two concepts are highly correlated, people are able to pair those concepts more quickly than two concepts that are not well associated. The IAT is relatively resistant to social desirability concern, and the reliability and validity have been rigorously tested.
How is the IAT used?
The IAT is powerful instrument, which has been used to explore the impact of unconscious bias on behavior. Here are some examples highlighting the use of the IAT in healthcare.
- A greater pro-White bias (measured using the IAT) among physicians resulted in an increased likelihood of prescribing thrombolytics for White patients compared to Blacks presenting with acute coronary syndrome (Green, 2007).
- A greater pro-White bias (measured using the IAT) was associated with a greater inclination to prescribe pain medications for White versus Black children (Sabin, 2012).
- Greater pro-White bias (measured using the IAT) was associated with poorer ratings of interpersonal care among Black patients (Cooper, 2012).
Unconscious biases are not permanent. In fact, they are malleable and steps can be taken to limit their impact on our thoughts and behaviors (Dasgupta, 2013).
When considering strategies to address unconscious bias one must consider individual and institutional strategies.
Individual strategies to address unconscious bias include:
- Promoting self-awareness: recognizing one's biases using the Implicit Association Test (or other instruments to assess bias) is the first step.
- Understanding the nature of bias is also essential. The strategy of categorization that gives rise to unconscious bias is a normal aspect of human cognition. Understanding this important concept can help individuals approach their own biases in a more informed and open way (Burgess, 2007).
- Opportunities to have discussions, with others (especially those from socially dissimilar groups) can also be helpful. Sharing your biases can help others feel more secure about exploring their own biases. It's important to have these conversations in a safe space-individuals must be open to alternative perspectives and viewpoints.
- Facilitated discussions and training sessions promoting bias literacy utilizing the concepts and techniques listed about have been proven effective in minimizing bias. Evidence suggests that providing unconscious bias training for faculty members reduces the impact of bias in the workplace (Carnes, 2012).
All institutions should:
- Develop concrete, objective indicators & outcomes for hiring, evaluation, and promotion to reduce standard stereotypes (Fiske & Taylor, 1991; Heilman, 2001; Bernat & Manis, 1994)
- Develop standardized criteria to assess the impact of individual contributions in performance evaluations (Heilman & Haynes, 2005)
- Develop and utilize structured interviews and develop objective evaluation criteria for hiring (Martell & Guzzo, 1991; Heilman, 2001)
- Provide unconscious bias training workshops for all constituents
Below is a list of resources to learn more about unconscious bias. Please click on the link at the bottom of the page to sign up for a live, in-person unconscious bias training session at UCSF. You can also explore a more comprehensive list of recent and classic implicit bias literature.
- E-Learning Seminar: What You Don't Know: The Science of Unconscious Bias and What to do About It in the Search and Recruitment Process. Association of American Medical Colleges (AAMC).
- Exploring Unconscious Bias in Academic Medicine. Association of American Medical Colleges (AAMC).
- Project Implicit. Link to the Implicit Association Test (IAT)
- Proven Strategies for Addressing Unconscious Bias in the Workplace. Includes an overview of unconscious bias and includes case studies to explore the impact of unconscious bias in the workplace. Diversity Best Practices. Sponsored by Cook Ross.
- State of the Science: Implicit Bias Review 2014. Kirwan Institute for the Study of Race and Ethnicity.
- The New Science of Unconscious Bias: Workforce & Patient Care Implications. This program explores the scientific basis for this new understanding of human bias and the implications of unconscious bias theory for the health care system both in terms of workforce bias and in terms of threats to clinical objectivity.
- The Science of Equality, Volume 1: Addressing Implicit Bias, Racial Anxiety, and Stereotype Threat in Education and Health Care. Perception Institute.
- Unconscious Bias. Cook Ross. Learn more about unconscious bias. Includes links to learn more about training and thought leadership in unconscious bias.
- Unconscious Bias Training for the Health Professions. Association of American Medical Colleges (AAMC).
- Women in Science. This special issue of Nature takes a hard look at the gender gap — from bench to boardroom — and at what is being done to close it.
- The Neuroscience of Unconscious Bias. The American Bar Association Litigation Section.
- Unconscious Bias in Academic Medicine. Proceedings of the 2017 AAMC Diversity and Inclusion Innovation Forum.
Bertrand, M, Mullainathan, S. Are Emily And Greg More Employable Than Lakisha And Jamal? A Field Experiment On Labor Market Discrimination. American Economic Review, 2004, v94(4,Sep), 991-1013.
Biernat M, Manis M. Shifting standards and stereotype-based judgments. J Pers Soc Psychol. 1994 Jan;66(1):5-20.
Burgess D, van Ryn M, Dovidio J, Saha S. Reducing racial bias among health care providers: lessons from social-cognitive psychology. J Gen Intern Med. 2007 Jun;22(6):882-7.
Carnes M, Devine PG, Isaac C, Manwell LB, Ford CE, Byars-Winston A, Fine E, Sheridan JT. Promoting Institutional Change Through Bias Literacy. J Divers High Educ. 2012 Jun;5(2):63-77.
Cooper LA, Roter DL, Carson KA, Beach MC, Sabin JA, Greenwald AG, Inui TS. The associations of clinicians' implicit attitudes about race with medical visit communication and patient ratings of interpersonal care. Am J Public Health. 2012 May;102(5):979-87.
Dasgupta, N. (2004). Implicit Ingroup Favoritism, Outgroup Favoritism, and Their Behavioral Manifestations. Social Justice Research, 17(2), 143-169.
Dasgupta, N. (2013). Implicit Attitudes and Beliefs Adapt to Situations: A Decade of Research on the Malleability of Implicit Prejudice, Stereotypes, and the Self-Concept. Advances in Experimental Social Psychology, 47, 233-279.
Dasgupta, N, Greenwald, A. G. (2001). On the Malleability of Automatic Attitudes: Combating Automatic Prejudice With Images of Admired and Disliked Individuals. Journal of Personality and Social Psychology, 81(5), 800-814.
Dore, R. A., Hoffman, K. M., Lillard, A. S. and Trawalter, S. (2014), Children's racial bias in perceptions of others' pain. British Journal of Developmental Psychology, 32: 218–231.
Fiske, S.T. & Taylor, S.E (1991). Social Cognition. International Edition. McGraw-Hill Series in Social Psychology.
Glicksman, Eve. Unconscious Bias in Academic Medicine: Overcoming the Prejudices We Don't Know We Have. Association of American Medical Colleges. 2016 January.
Green AR, Carney DR, Pallin DJ, Ngo LH, Raymond KL, Iezzoni LI, Banaji MR. Implicit bias among physicians and its prediction of thrombolysis decisions for black and white patients. J Gen Intern Med. 2007 Sep;22(9):1231-8.
Heilman ME, Alcott VB. What I think you think of me: women's reactions to being viewed as beneficiaries of preferential selection. J Appl Psychol. 2001 Aug;86(4):574-82.
Heilman ME, Haynes MC. No credit where credit is due: attributional rationalization of women's success in male-female teams. J Appl Psychol. 2005 Sep;90(5):905-16.
Jagsi R, Griffith KA, Stewart A, Sambuco D, DeCastro R, Ubel PA. Gender differences in salary in a recent cohort of early-career physician-researchers. Acad Med. 2013 Nov;88(11):1689-99.
Kirwan Institute (2014). State of the Science: Implicit Bias Review 2014.
Martell, R.F, Guzzo, R. A. (1991). The dynamics of implicit theories of group performance: When and how do they operate? Organizational Behavior and Human Decision Processes, 50, 51–74.
Moss-Racusin CA, Dovidio JF, Brescoll VL, Graham MJ, Handelsman J. Science faculty's subtle gender biases favor male students. Proc Natl Acad Sci U S A. 2012 Oct 9;109(41):16474-9.
Paradies Y, Priest N, Ben J, Truong M, Gupta A, Pieterse A, Kelaher M, Gee G. Racism as a determinant of health: a protocol for conducting a systematic review and meta-analysis. Syst Rev. 2013 Sep 23;2:85.
Sabin JA, Greenwald AG. The influence of implicit bias on treatment recommendations for 4 common pediatric conditions: pain, urinary tract infection, attention deficit hyperactivity disorder, and asthma. Am J Public Health. 2012 May;102(5):988-95.
Stone J, Moskowitz GB. Non-conscious bias in medical decision making: what can be done to reduce it? Med Educ. 2011 Aug;45(8):768-76.
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