As job market season enters full swing, many of us – job candidates and search committee members both – are concerned about how to mitigate the effects of implicit biases. Voluminous empirical research shows that impressions of candidates can be impacted not only by coarse identity features such as their apparent gender and race, but by minutiae from the way they sit to how much they weigh to what they wear. Research also suggests that no matter how well-intentioned and diligent we are, those of us assessing candidates cannot just think or will ourselves out of the problem. Insisting to yourself that you will assess candidates impartially and ignore irrelevant features does little to help, and may even backfire. When we actively suppress our biases, they re-emerge with a vengance once we become less vigilant or when we are under stress. Our lizard brains are remarkably insouciant when it comes to listening to and taking directions from our mister-bossy-pants conscious, reflective minds, it turns out. Furthermore, we just don’t have enough data to know how to minimize the effects of implicit bias by brute force; we may have various intuitions about whether bias kicks in more during APA interviews than skype interviews, or whatever, but there isn’t enough science out there to say, and the phenomenon has turned out to function counterintuitively many times before. (For a bibliography of psychological and philosophical literature on implicit bias, see here.)

What is to be done?  Well, I don’t think any of us really has a sufficient answer, although there are some concrete tools out there that help to focus our attention on relevant rather than irrelevant traits. (There are some nice resources here, although I can't get the links to the online tool links to work, unfortunately.)  But recognizing that good intentions are not a solution is surely important, at least in fostering necessary epistemic humility.  It seems to me that people are persistently and dramatically over-confident both about their ability to consciously compensate for bias, and in their intuitions about how to avoid it in the first place. See, for example, this recent thread, in which several people simply asserted that they had various techniques that enabled them to filter out noise and bias. Many of us have already played around with this, but taking some of the online tests bouncing around the web can be a useful eye-opener. (Thanks once again to Bryce Huebner for helpful discussions.)

Thus ends my stint as guest-blogger. Many thanks to all who participated in what I think were some excellent discussions over the last week and a half, and many thanks again to Brian for inviting me to do this.  And good luck to all job candidates!