This article is part of an ongoing blog series, titled Inequality in STEM: a Dive Into the Data. In this series, we cover recent research exploring and quantifying inequality in STEM. We'll discuss different aspects of inequality, including barriers to career advancement and a chilly social climate, as well as the efficacy of various interventions to combat bias. Our goal with these pieces is to provide clear summaries of the data related to bias in STEM, giving scientific evidence to back up the personal experiences of URMs in STEM fields.
Humans are biased, including against particular groups of people, and often subconsciously. Biases may have been useful for our evolutionary ancestors – for instance, allowing for quick, stereotyped responses to potential threats or prey. Unfortunately, they are not so useful for social or scientific progress in the modern world. For instance, biased beliefs about the abilities of underrepresented groups in science can lead to inequality in hiring, promotions, admissions, funding, and more. This is obviously unjust at the expense of members of these underrepresented groups. Perhaps less apparently, bias can also impede scientific innovation and discovery – diversity has been shown to enhance productivity, creativity, insight, and critical thinking.
Neuroscience, as one of the STEMM (science, technology, engineering, math, and medicine) disciplines, is not immune to the ill-effects of bias. While trends toward increasing diversity in STEMM are encouraging, full-time academic faculty members in these fields are still disproportionately male (Fig. 1) [1,2], and non-Asian men and women of color are drastically underrepresented amongst tenure-track faculty relative to their representation in the US population as a whole .
Given that diversifying the STEMM academy is both just and beneficial to scientific progress, it’s hard to argue against efforts to promote diversity in hiring. However, early studies of efforts to do just that yielded discouraging results: some of the most intuitive strategies for reducing bias (e.g., promoting “colorblind” selection processes or pressuring managers to meet diversity benchmarks) can actually lead to increased discrimination [4,5]. Apparently, a desire to address the problem is not enough.
In light of this issue, two groups of researchers recently sought to develop and rigorously test evidence-based interventions for counteracting natural human biases, with the specific goal of decreasing the bias against women in the academic STEMM faculty hiring process. In each experiment, STEMM departments within a large public university were randomly assigned to receive either a targeted intervention or no intervention (the control group). (Both groups still received any Human Resources training required by their universities.)
One intervention, developed by Molly Carnes, Patricia Devine, and colleagues, was based on the idea that implicit bias is like a bad habit: it can be broken with a combination of awareness of one’s own behavior, concern about its negative effects, and targeted strategies to reduce the behavior [6,7]. Patricia Devine recently spoke about this concept during an NPR interview, which is available for streaming online if you’re interested. The researchers combined this strategy with best practices drawn from the fields of adult education and intentional change of addictive behaviors to create a 2.5-hour training session they presented to faculty within each intervention department. Each session began with a review of evidence supporting the importance of diversity, as well as the pervasiveness of gender bias and its negative effects. This was followed by three modules: 1) a review of the bias-as-habit premise and supporting research, 2) a focus on common forms of bias in academic settings and some consequences of each, and 3) evidence-based strategies for counteracting bias (for instance, increasing opportunities for interaction with role models who defy typical stereotypes, or explicitly seeking out more individualized information about a job applicant if they come from an underrepresented group to prevent stereotypes from dominating hiring decisions). At each stage of the workshop, faculty were given discussion prompts and other interactive tasks, like role-playing, to help them apply the concepts they were learning. They also produced written statements of commitment to act to address gender bias and were given written review materials to take home. A similar type of training had previously shown promising results in diminishing implicit racial bias in undergraduate students .
The other intervention, developed by Jessi Smith and colleagues , was similarly based in the well-established psychological framework of self-determination theory , which emphasizes humans’ need to feel a sense of competence, relatedness to others, and autonomy in order to generate internalized motivation to grow or change (in this case, to overcome gender bias in applicant evaluation and selection). A faculty peer gave a short workshop and written guidelines each intervention department’s search committee (the group of faculty responsible for hiring new faculty). Like the Carnes et al. intervention, these materials covered evidence on the benefits of diversity, common manifestations and effects of gender bias in academia, and effective strategies for counteracting bias. However, the groups in this study were smaller, and there was more focus placed on the challenges of recruiting diverse candidates to rural, low-salary institutions (this was particularly relevant to the site of the study, Montana State University). Additionally, in an effort to improve recruitment of female candidates, all applicants invited to interview with intervention departments were also provided short, confidential sessions with a faculty “Family Advocate” to discuss the university’s work-life policies and resources.
In the end, both studies found that departments that received the interventions were more likely than control departments to hire women in their subsequent faculty searches (Fig. 2 , see also Table 1 in ). These studies, like all scientific endeavors, were limited in a number of ways. Each was performed at a single institution, limiting both statistical power and our confidence that the same interventions would be effective elsewhere. Neither study sought to address the more dramatic underrepresentation of people of color in STEMM, and it is unclear whether the tested interventions would be effective in this context. Finally, the complexity of each of the interventions prevents any analysis of which, if any, of the intervention components were most effective at reducing gender bias in hiring. Nonetheless, their results provide encouraging evidence that carefully designed, evidence-based interventions can successfully counteract implicit bias in academic job searches.
1. SEH doctorate holders employed in academia by type of position, sex, and degree field: 1973-2010. National Science Foundation. https://www.nsf.gov/statistics/seind14/content/chapter-5/at05-15.pdf.
2. Women in the academic pipeline for technology, engineering and math (2013). Association of American Universities Data Exchange. http://aaude.org/system/files/documents/public/reports/report-2013-pipeline.pdf.
3. Women in science and engineering statistics. National Academies of Sciences, Engineering and Medicine Committee on Women in Science, Engineering and Medicine. http://sites.nationalacademies.org/pga/cwsem/PGA_049131.
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