Our lunch discussion series (Thursdays at noon in CHB 339) continues! Contact me (hdnelson at uw.edu) or Teresa (tmheard at uw.edu) if you’d like to join our email list or access the schedule, or if you have a topic suggestion.
This week, we talked about a recently published study (Handley, Brown, Moss-Racusin, Smith; PNAS 2015, 112, 13201-13206) investigating how people react to evidence of gender bias. The authors showed that men view studies demonstrating gender bias less favorably than women do, a finding which has important implications for anyone interested in combating bias in STEM fields.
Objectivity is valued in scientific experiments, but should also be a priority in the context of how science is conducted. Women are under-represented in most STEM fields at most levels, and several studies have demonstrated bias against women scientists in various contexts (references 10-17 in the paper). The authors wondered how the general public and the STEM community view this evidence, since acknowledging the existence of gender bias is an important first step toward reducing its effects and working toward a more diverse and open scientific community.
The authors suggest that men may evaluate the evidence of gender bias more negatively than women. Conceptual frameworks like social identity theory provide a basis for this hypothesis: in general, people hold favorable views of groups to which they belong and seek to defend these groups against external threats, and members of privileged groups usually try to maintain the status quo. For men, the dominant gender group in most STEM fields, evidence of gender bias against women can be seen as a threat because it may lead to favoring women over men. Male STEM professors, who are especially invested in this system and who benefit more from gender bias in their fields, are expected to react more strongly to this perceived threat. Confirmation bias, in which people react favorably toward information that matches their beliefs (but unfavorably toward information that conflicts with their views), could also play a role in perceptions of bias. I realized that my own negative reaction toward this editorial (which claims there is no gender bias against women in academic hiring) is a clear example of this motive.
The authors’ experiments, which involved asking men and women to read the abstract of this study (demonstrating hiring bias against women in STEM fields) and rate its importance and quality, support the above assertions. On average, men rated the abstract less favorably than women, and the difference was more pronounced for STEM professors vs. non-STEM professors or the general public. Other parameters (such as the gender of the abstract’s supposed author and the author’s institutional affiliation) were also varied, which revealed some other potentially interesting trends (explored in the Supporting Information), but overall, the average man viewed the abstract less favorably than the average woman.
They also repeated the experiment with a different abstract (for a study showing that graduate students view conference abstracts more favorably when they believe the author is male than female). In this case, participants read either the real abstract for the study that did find gender bias, or a modified version for a study that found no gender bias. Here, men evaluated the abstract showing bias less favorably than women did, but women evaluated the abstract showing no bias less favorably than men did. Importantly, this experiment shows that men do not evaluate all research more critically than women, which could have been an alternative explanation for the previous results.
Another interesting implication of this experiment is that the authors cannot conclusively state which group’s evaluation of the research is more accurate, since there’s no way to have an objective, non-gendered control experiment. It’s possible that the average man in the study underestimates the importance and quality of research related to gender bias, but it’s also possible that the average woman overestimates these factors. However, since men as a group have more power in STEM fields than women, their potential underestimation of bias seems more dangerous than women’s potential overestimation of bias.
The authors acknowledge several other limitations of their study, including the fact that they used participants’ immediate judgments of an abstract to measure their perception of the study (rather than well-thought-out, critical evaluations of the full study) and their lack of experimental investigation into the root causes of their results (however, it is necessary to first demonstrate that this phenomenon exists). They propose to further explore the sources of the observed trends; other interesting follow-up studies could investigate how these trends vary in different STEM fields (are male biologists more receptive to studies demonstrating bias than male physicists?) or how people respond to studies showing bias in other areas, such as race.
I thought that this study added an interesting layer to the discussion of gender bias in STEM. We can keep trying to demonstrate that gender bias exists, but if the studies demonstrating this aren’t taken seriously, we still won’t get anywhere. Showing that women are underrepresented in STEM fields and that some of this inequality comes from bias (which is often implicit and subtle) is not enough to solve this problem. We also need to convince others (especially members of privileged or dominant groups) that this problem exists and is important. But these conversations can be difficult to have without making others feel attacked or threatened. I really don’t think that all men are sexist, or that my male colleagues in STEM should feel guilty, but it can be easy to exaggerate or misinterpret ideas like the findings of this study (which definitely did not say that all men are sexist!). A topic that’s come up in both of our lunch discussions so far is how to cultivate allies among more privileged groups (as well as how to be an ally to underrepresented or less-privileged groups). This paper demonstrates that it’s difficult but essential to help others understand the value of researching bias. Being open-minded when it comes to discussions of bias is the first step toward acknowledging and countering its effects.