University of Oregon

Implicit Bias

The National Center for State Courts and the American Bar Association Section of Litigation produced the following video to address the issue of implicit bias in the judicial system. The video explores the neurological research that shows how implicit bias informs our cognition and behavior, regardless of our level of education or a desire to be colorblind.

Reviewing Applicants: Research on Bias and Assumptions

This section on unconscious bias is reproduced and adapted with permission from WISELI: Women in Science and Engineering Leadership Institute® at the University of Wisconsin-Madison.  It is based on the following publication:  Fine, E., Handelsman, J. Reviewing Applicants:  Research on Bias and Assumptions.  Copyright ©2005, 2006 The Board of Regents of the University of Wisconsin System.

Categorization and Stereotyping

Categorization is a cognitive process that occurs largely outside of conscious awareness and helps people to cope in a complex and demanding environment.  The categorization process extends to categorizing people by groups.

Stereotyping, the unconscious habits of thought that link personal attributes to group membership, is an inevitable result of categorization.  Stereotype-based expectations give rise to biased attributions.  (Reskin, 2000)

A large body of research suggests each one of us holds implicit biases that impact our judgment.  Implicit bias is, in essence, part of the human condition.  As such, it inevitably impacts interactions with others and processes in which we engage, including the faculty search process.  Research suggests that we all engage in unconsciously biased assessments and decision making processes.  With this understanding, we can more swiftly move away from blame and embarrassment, and towards efforts to identify, understand and minimize negative impacts of unintended bias as we search for and hire outstanding faculty.

“We all like to think that we are objective scholars who judge people solely on their credentials and achievements, but copious research shows that every one of us has a lifetime of experience and cultural history that shapes the review process.” (Fine & Handelsman, 2006).

The results from controlled research studies demonstrate that people often hold implicit or unconscious assumptions that influence their judgments.  Studies include expectations or assumptions about physical or social characteristics associated with race, gender, and ethnicity and several studies relate directly to the hiring process.

Examples of common social assumptions or expectations:

  • When shown photographs of people of the same height, evaluators overestimated the heights of male subjects and underestimated the heights of female subjects, even though a reference point, such as a doorway, was provided (Biernat et al. 1991)
  • When shown photographs of men with similar athletic abilities, evaluators rated the athletic ability of African American men higher than that of white men (Biernat & Manis 1994)
  • When asked to choose counselors from among a group of equally competent applicants who were neither exceptionally qualified nor unqualified for the position, students more often chose white candidates than African American candidates, indicating their willingness to give members of the majority group the benefit of the doubt (Dovidio & Gaertner 2000).

These studies show that we often apply generalizations that may or may not be balid to the evaluation of individuals (Beiby & Baron, 1986).  If generalizations can lead us to inaccurately evaluate characteristics as objective and easily measured as height, what happens when the qualities we are evaluating are not as objective or as easily measured?

Examples of assumptions or biases that can influence the evaluation of applications:

  • When rating the quality of verbal skills as indicated by vocabulary definitions, evaluators rated the skills lower if they were told an African American provided the definitions than if a white person provided them (Biernat & Manis, 1994)
  • Randomly assigning different names to resumes showed that job applicants with “white-sounding names” were more likely to be interviewed for open positions than were equally qualified applicants with “African American –sounding names.” (Bertrand & Mullainathan, 2004)
  • When symphony orchestras adopted “blind” auditions by using a screen to conceal candidates’ identities, the hiring of women musicians increased.  Blind auditions fostered impartiality by preventing assumptions that women musicians have “smaller techniques” and produce “poorer sound” from influencing evaluation (Goldin & Rouse, 2000).
  • Research shows that incongruities between perceptions of female gender roles and leadership roles cause evaluators to assume that women will be less competent leaders.  When women leaders provide clear evidence of their competence, thus violating traditional gender norms, evaluators perceive them to be less likeable and are less likely to recommend them for hiring or promotion (Eagly & Karau, 2002 ; Ridgeway, 2001; Heilman, et al, 2004).

Examples of assumptions or biases in academic job-related contexts:

  • A study of over 300 recommendation letters for medical faculty hired by a large U.S. medical school found that letters for female applicants differed systematically from those for males.  Letters written for women were shorter, provided “minimal assurance” rather than solid recommendation, raised more doubts, portrayed women as students and teachers while portraying men as researchers and professional, and more frequently mentioned women’s personal lives (Trix & Psneka, 2003).
  • In a national study, 238 academic psychologists (118 male, 120 female) evaluated a curriculum vitae randomly assigned a male or a female name.  Both male and female participants gave the male applicant better evaluations for teaching, research, and service experience and were more likely to recommend hiring the male than the female applicant (Steinpreis et al., 1999)
  • A study of postdoctoral fellowships awarded by the Medical Research Council of Sweden found that women candidates needed substantially more publications to achieve the same rating as men, unless they personally knew someone on the panel (Wenneras & Wold, 1997).

Suggestions for minimizing the influence of bias and assumptions:

Determine whether qualified women and underrepresented minorities are included in your pool at rates expected based on availability, and consider whether evaluation biases and assumptions are influencing your decisions by asking yourself the following questions:

  • Are women and minority candidates subject to different expectations in areas such as numbers of publications, name recognition, or personal acquaintance with a committee member?  (Recall the example of the Swedish Medical Research Council).
  • Have the accomplishments, ideas, and findings of women or minority candidates been undervalued or unfairly attributed to a research director or collaborators despite contrary evidence in publications or letters of reference? (Recall the biases seen in evaluations of written descriptions of job performance.)
  • Is the ability of women or minorities to run a research group, raise funds, and supervise students and staff of different gender or ethnicity being underestimated? (Recall social assumptions about leadership abilities).
  • Are assumptions about possible family responsibilities and their effect on a candidate’s career path negatively influencing evaluation of a candidate’s merit, despite evidence of productivity? (Recall studies of the influence of generalizations on evaluation.)
  • Are negative assumptions about whether women or minority candidates will “fit in” to the existing environment influencing evaluation? (Recall students’ choice of counselor).

Resources

Empirical Studies

Bertrand, M., & Sendhil Mullainathan. (2004). Are Emily and Greg more employable than Lakisha and Jamal? American Economic Review, 94, 991-1013.

Bielby, W.T., & Baron, J.N. (1986). Men and women at work: Sex segregation and statistical discrimination. The American Journal of Sociology 91, 759-799.

Biernat, M., & Fuegen, K. (2001). Shifting standards and the evaluation of competence: Complexity in gender-based judgment and decision making. Journal of Social Issues 57, 707-724.

Biernat, M., & Manis, M. (1994). Shifting standards and stereotype-based judgments. Journal of Personality and Social Psychology 66, 5-20.

Dovidio, J.F., & Gaertner, S.L. (2000). Aversive racism and selection decisions: 1989 and 1999. Psychological Science, 11, 315-319.

Eagly, A.H., & Karau, S.J. (2002). Role congruity theory of prejudice toward female leaders. Psychological Review, 109, 573-598. [PDF]

Goldberg, C. (2005). Relational demography and similarity attraction in interview assessments and subsequent offer decisions. Group and Organization Management, 30, 597-624.

Goldin, C., & Rouse, C. (2000). Orchestrating impartiality: The impact of “blind” auditions on female musicians. The American Economic Review, 90, 715-741. [PDF]

Heilman, M.E. Wallen, A.S., Fuchs, D. & Tamkins, M.M. (2004). Penalties for success: Reactions to women who succeed at male gender-typed tasks. Journal of Applied Psychology, 89, 416-427.

Martell, R. F. (1991), Sex bias at work: The effects of attentional and memory demands on performance ratings of men and women. Journal of Applied Social Psychology, 21, 1939–1960.

Nosek, B.A., Smyth, F. L., Sririam, N., Lindner, N.M. , Devos, T., Ayala, A., & Bar-Anan, Y. (2009). National differences in gender-science stereotypes predict national sex differences in science and math achievement. Proceedings of the National Academy of Sciences, 106, 10593-10597.

Steinpreis, R., Anders, K., & Ritzke, D. (1999). The impact of gender on the review of the curricula vitae of job applicants and tenure candidates: A national empirical study. Sex Roles, 41, 509-528.

Trix, F.& Psenka, C. (2003). Exploring the color of glass: Letters of recommendation for female and male medical faculty. Discourse & Society, 14, 191-220.

Wennerås, C., & Wold, A. (1997). Nepotism and sexism in peer-review. Nature, 387, 341-343.

Theoretical and Conceptual Articles

Bilimoria, D., & Buch, K. (2010). The search is on: Engendering faculty diversity through more effective search and recruitment. Change, 42(4), 27-32.

Butler, I., & Godsil, R. (2010). Best man for the job? How bias affects hiring. Americans for American Values.  Retrieved from http://writers.unconsciousbias.org

Moody, JoAnn. (2010) Rising above cognitive errors: Guidelines to Improve faculty searches, evaluations, and decision-making. Publication can be ordered directly from Joann Moody at www.DiversityOnCampus.com

Reskin, Barbara. 2000. The proximate cause of employment discrimination. Contemporary Sociology, 29, 319-328.

Valian, V. (1999). The cognitive bases of gender bias. Brooklyn Law Review, 65, 1051-1062. [PDF]

Additional Resources

Advancement of Women in Science and Engineering Careers (ADVANCE) Resource Page on Bias

Fine, E., Handelsman, J. (2005). Reviewing applicants: Research on bias and assumptions. [PDF]

Implicit Associations Tests Online: https://implicit.harvard.edu/implicit/

Tversky, A., Kahneman, D. (1990). Judgment Under Uncertainty: Heuristics and Biases. Judgment and Decision Making: An Interdisciplinary Reader (2nd Ed). Edited by Connolly, Arkes, Hammond.

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