Unconscious Bias

This is another long post. The essential points are:

• There is a body of evidence that women in STEM are adversely affected by unconscious bias.
• These implicit assumptions about women's roles (gender schemas) are a cognitive necessity for dealing with the social world but they can lead to inaccurate judgements.
• Men and women share these implicit assumptions.
• There are effective interventions that mitigate the effects of unconscious bias.

Way back in August I mentioned that there were two big ideas that made an impact on me when I started working for the Women in Science, Engineering and Technology Initiative at the University of Cambridge. The one I discussed in August was Teresa Rees's description of different approaches to gender equality as 'Tinkering, Tailoring and Transforming' – basically these correspond to removing discrimination, adapting women to fit the system and adapting the system to enable everyone to contribute to their full potential. The other big idea is the concept of unconscious bias.

I’ll start with a story. A few years ago when I was travelling on a train I glanced across the carriage and noticed that one of the passengers was reading ‘An Introduction to Electronics’.  ‘Ah’, I thought to myself,’ a young man using his commuting time to catch up on some study.’ A bit later I took a better look around the carriage and realised that the reader of ‘An Introduction to Electronics’ was, in fact, a young woman. I had spent around twenty-five years doing physics. Part of my job at the University of Cambridge was to run workshops on unconscious bias and women in science. To add to the irony the reason I was on the train was that I was returning from a meeting about a mentoring project for female undergraduate engineers organised by a female professor of electrical engineering. And my initial assumption was that someone reading a book about electronics would be male.

Unconscious bias refers to implicit expectations we all have about the roles and behaviour of members of particular groups. The reason this idea had a great impact on me was that it made sense of my experience that  people who professed a belief in equality, indeed were genuinely committed to achieving equality, nevertheless acquiesced in practices that unintentionally made it more difficult for women to progress in science and engineering.

I learnt about unconscious bias affecting women in professional careers from Virginia Valian’s book Why so Slow? The Advancement of women. Valian uses the phrase ‘gender schemas’ to describe the implicit hypotheses that we hold about sex differences. The content of these gender schemas does not depend on gender: men and women have the same beliefs. For example, a study of the relationship between the strength of an implicit association between ‘male’ and ‘science’ and a measure of achievement in science found that men and women had equally strong associations of ‘science’ with ‘male’ [Nosek et al, '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 (2009) ]. Valian emphasizes that gender schemas are a cognitive necessity for making sense of the social world. However, in some contexts, they can lead to lower expectations for women.

Subsequently there have been two frequently cited studies reinforcing the points made by Valian. One, Steinpreis et al, 'The Impact of Gender on the Review of the Curricula Vitae of Job Applicants and Tenure Candidates: A National Empirical Study' Sex Roles 41, 718 (1999) (pdf), reports a study in which CVs that differed only in whether the name was given as Karen Miller or as Brian Miller. The study found that both men and women were more likely to vote to hire a male job applicant than a female job applicant with an identical record. In the other study Trix and Psenka, 'Exploring the Color of Glass: Letters of Recommendation for Female and Male Medical Faculty' Discourse and Society 14, 191 (2003) found that letters of recommendation for women were shorter, contained twice as many ‘doubt raisers’ (‘she has a somewhat challenging personality’), more ‘grindstone adjectives’ (conscientious, diligent) and fewer ‘stand-out adjectives (superb, outstanding). ( Similar results have been obtained by Schmader et al in a study of letters of recommendation for faculty positions in biochemistry and chemistry,' A Linguistic Comparison of Letters of Recommendation for Male and Female Chemistry and Biochemistry Job Applicants', Sex Roles 57, 509 (2007).)

The Project Implicit website has on-line tests that measure the strength of implicit associations such as that between ‘male’ and ‘science’.

What can we do about it?

The Project Implicit FAQ suggests that individuals who find that they have an implicit bias that they would rather not have could try seeking experiences that contradict their implicit bias, being conscious of their bias and its potential effect on their judgements and consciously planning actions that compensate for their known unconscious preference.

Valian suggests:

• Learning about gender schemas, accepting that we may have expectations that we are unaware of that may contradict what we consciously believe and consciously changing our behaviour.
• Challenging implicit hypotheses, for example, by imagining our response to someone’s behaviour if the person concerned was male rather than female.
• Reducing reliance on gender schemas by
• Spending more time on decisions. People use gender schemas to make automatic responses. If more time is available there is less need to rely on an automatic response.
• Giving decisions our full attention. Making judgements while distracted by another task increases the reliance on gender schemas.
• Holding decision makers accountable. People are more likely to form accurate judgements if they know there decisions will be reviewed.
• Increasing the number of women in the candidate pool. Gender schemas become less important when women form a reasonable proportion of the group being evaluated.
• Avoiding errors of reasoning such as:
• Failure to appreciate covariation, where for example, an apparent variation with sex in some ability is actually due to some other variable.
• Blocking, where the fact that data are consistent with a prevailing schema prevents evaluators from noticing other factors that have affected performance.
• Illusory correlation, where people perceive a causal link between rare events such as incompetence and being female, simply because both events are rare.
• 
  
• At an institutional level
• Committed leaders and leaders who are ready to legitimize woman leaders.
• Objective performance criteria.
• 
  
• For women – how can they increase the chances of being perceived as competent?
• Be where women are well represented, which isn’t a lot of help to physicists, mathematicians, engineers and computer scientists.
• Be impersonal, friendly and respectful. This minimizes the negative effects of being perceived as competent.
• Build power
• Seek information.
• Become an expert.
• Get endorsed by a legitimate authority.
• Negotiate
• Overcome internal barriers. One of the effects of gender schemas is that women may attribute lack of reward of their efforts to their lack of ability. A better understanding of gender schemas and how they work.
• (For practical suggestions on how to accomplish the above, see  Suzanne Doyle-Morris’s book, Beyond the Boys’ Club.)

The STRIDE programme at the University of Michigan is an example of a comprehensive, successful intervention.

Useful resources:
1. Virginia Valian’s ‘Tutorials for Change'

2. The University of Michigan’s STRIDE Faculty Recruitment Presentation, available from the STRIDE website

3. The report of the US National Academies 'Beyond Bias and Barriers: Fulfilling the potential of women in science and engineering' National Academies Press (2007).

4. WISELI (Women in Science and Engineering Leadership Institute), University of Wisconsin-Madison Reports and Publications – A particularly intriguing report listed here is ‘Interventions That Affect Gender Bias in Hiring: A Systematic Review’ by Isaac, Lee and Carnes  Academic Medicine 84, 1440 (2009).This report analysed 27 previous studies of interventions that affect gender differences in the evaluation of job applicants. The authors found that the studies showed a negative bias in evaluations of women for positions in areas traditionally or predominantly held by men and also that the assessments by men and women rarely differed. Interventions that were effective in mitigating the effects of unconscious bias were:
-    Providing assessors with clear evidence of job-related competencies, with the proviso that additional evidence of ‘communal qualities’ was provided for women.
-    A commitment to the value of credentials before reviewing the applicants.
-    Women’s presence at greater than 25% of the applicant pool.
Two studies found unconscious resistance to anti-bias training. And, according to the abstract:

‘Explicit employment equity policies and an attractive appearance benefited men more than women, whereas repeated employment gaps were more detrimental to men. Masculine-scented perfume favoured the hiring of both sexes.’ (My emphasis).