Some resources on implicit bias

Some of the comments to this post brought up the topic of implicit gender bias, and I thought the time might be ripe for a couple of pointers on the subject. Although I'm changing the context a little (from ethics training to search committees), the comments on reducing bias remain the same.

The first is that, in general, but with considerable caveats,* BOTH women and men are biased against women's applications, papers and such. The issue of gender bias is NOT just a problem of men being biased against women.

In fact, let's be clear - in all of these stories about gender, there are some men who may be more respectful of women's work than some women, as well as the other way around. Gender is complicated, and not at all homogeneous in terms of how it impacts people's behaviour. So let's not just pin it on men, letting women who engage in this off scott-free, hey?

However, it does seem that no matter who is doing the interpretation, women in this context regularly get the short end of the stick. In fact, I now have conflicting views on the usefulness of using initials rather than first names in submitting work for review. On one hand, scholars in women's studies have integrated first names in citations and references to highlight the fact that women are doing research, in contrast to the practice of using initials in citations which results in people presuming the maleness of authors. On the other, I just read somewhere (can't remember where, will have to track it down, maybe someone else's blog post? :-S Uh oh, the blogosphere now perpetuates unfounded information, what a shock) that people who submit papers and CVs with only their initials (presumably in some disciplines) are now presumed to be female and trying to hide it. (Can anyone help me with the source? Did I make it up? :-S ) Either way, women seem to be caught coming and going - if they use their first names they may be biased against by folks who were studied in older set of research, and if they don't they may be biased against by folks who are trying to catch out the older research. As it were. More evidence that gender is complicated - it's not simply a question of deciding "this is bad, so I'll do the opposite," because quite often the opposite also is bad for many women.

The second big point is that both men and women are more biased against women when they lack time to think through their decisions, particularly in hiring decisions. One of the best ways to counteract implicit bias is to encourage people to take enough time with their decisions, really think through each step and not rely on snap decisions. That's why in the Harvard Implicit Bias assessments they ask you to click your first thought, not take too much time over thinking through it.

The Women in Science and Engineering Leadership Institute (WISELI) is an ADVANCE-grant funded organization at the University of Wisconsin-Madison, and it has some good tools on reducing bias in terms of faculty searches. In addition former ADVANCE co-PI Jo Handelsman has some good manuals (available for free online!) on how to reduce the impact of unconscious bias in mentoring and such. Links here:

• WISELI: Reviewing Applicants - Research on Bias and Assumptions (pdf)
• WISELI: Searching for Excellence and Diversity: A guide for search committee chairs (pdf)
• Other links collected by WISELI
• A portal for other ADVANCE-funded organizations is available through the Virginia Tech ADVANCE. I should also say that my experience in the hiring process at VT was particularly positive because of their ADVANCE program - they provide candidates with information about family-oriented policies for faculty, and had me meet with a person who was not part of the search and therefore could be asked about all the climate questions, balance questions, harassment questions and so on in confidence. It was great!
• Entering Mentoring: A Guidebook, available as a pdf here, or for purchase here.

I look forward to hearing some of your thoughts about this topic - consider the comments open (she said somewhat tentatively, but still with full power to delete trollish or otherwise stupid and unhelpful comments).

* Please forgive some of the gross generalizations here, and the unclear theorizing behind what we understand "gender" to be, and also put this all in the context of science and engineering work. In addition, at some point we should talk about what we mean by "biased" as the term is not at all as easy to define as many of us may think. Thanks. :-)