Bias in employment and education

Many common biases are based on a person’s gender, race and age but can occur in a positive or negative way for all categories of people and in situations where people are confronted by difference.

Studies using identical CVs and job applications have demonstrated name-based discrimination against applicants, disproportionately affecting women and visible minorities.

University faculty selectors (both male and female) rated applicants as being more competent, more hireable, worth higher salaries and offered more career mentoring where the application had a male name compared to an identical application having a female name (3,4).

Again, using identical applications with the only difference being the applicant’s name, selectors have demonstrated stark preferences for applicants with distinctly European/Pakeha names to applicants with distinctly Asian (5), East Asian and Middle Eastern (6) or African-American names (7).

A recent comparative study by Blank, Houkamau and Kingi revealed the implicit biases in education, finding teachers with negative implicit attitudes were more likely to evaluate Māori and African-American students less intelligently and less likely to achieve at school. These low expectations and biases create a downward spiralling self-fulfilling prophecy (8).

Increasing attention is being giving to improving the participation of women in STEMM (science, technology, engineering, mathematics and medicine). In her 2015 text, Nicola Gaston (9) details the extent of how our unconscious biases against female scientists works against this aim and she warns of its damaging consequences for science and for society.

Amid concern about bias against, and under-representation of, women and ethnic minority students and staff, universities, private and public sector organisations internationally are attempting to address this issue.

Up until the 1970s, symphony orchestras in America previously had less than 20% female instrumentalists. By introducing a screen between the musician and the audition committee (leaving the applicants audible but not visible to the judges) the proportion of the women hired by major symphony orchestras doubled to 40% (10).

A number of universities and the Civil Service and local government in the UK (11, 13), the Victorian State Government in Australia (12) and universities such as Macquarie University, NSW, have trialled the use of anonymous screening where details such as name, age, gender, address, nationality, university they received their degree/PhD etc.are removed from all job applications.

While these measures will remove information that triggers biases and may equalise entry into study and employment, structural processes and cultures in which unconscious biases and implicit associations are recognised and redressed are also needed.

Recent research demonstrates that to remove prejudice and bias, organisation-wide and multi-faceted interventions are essential.

Specific intentional interventions which have been found to mitigate prejudice include:

• Increasing contact with diverse groups and exposure to counter-stereotypical exemplars
• Using objective decision-making processes and tools
• Individuation
• Perspective taking
• Removal of structural inequities in organisational policies and practices.

See the following reviews of interventions:

Equality Challenge Unit UK Review. Unconscious bias and higher education 2013.

FitzGerald C, Martin A, Berner D. et al. Interventions designed to reduce implicit prejudices and implicit stereotypes in real world contexts: a systematic review. BMC Psychol 7, 29 (2019).

See also: 

• Overcoming unconscious bias
• Unconscious bias resources and references