Invisible Women: Data Bias in a World Designed for Men

The inequity of women being loaded with less valued work is compounded by the system for evaluating this work, because it is itself systematically biased against women. Teaching evaluation forms are widely used in higher education and they represent another example of a situation where we have the data, but are simply ignoring it. Decades of research35 in numerous countries show that teaching evaluation forms are worse than useless at actually evaluating teaching and are in fact ‘biased against female instructors by an amount that is large and statistically significant’.36 They are, however, pretty good at evaluating gender bias. One of these biases is our old friend ‘men are the default human’, which shows up in objections to female lecturers straying away from a focus on white men. ‘I didn’t come out of this course with any more information except gender and race struggles, than I came in with,’ complained one student who apparently felt that gender and race were not relevant to the topic at hand: US confederation.37

Falling into the trap we encountered in the introduction, of not realising that ‘people’ is as likely to mean ‘women’ as it is to mean ‘men’, another student complained that, ‘Although Andrea stated on the first day she would teach a peoples [sic] perspective it was not illustrated how much was going to be focused on first nation and women’s history.’ Incidentally, it’s worth taking the implication that this lecturer focused almost exclusively on ‘first nations and women’s history’ with a pinch of salt: a friend of mine got a similarly unhappy review from a male student for focusing ‘too much’ on feminism in her political philosophy lectures. She had spoken about feminism once in ten classes.

Less effective male professors routinely receive higher student evaluations than more effective female teachers. Students believe that male professors hand marking back more quickly – even when that is impossible because it’s an online course delivered by a single lecturer, but where half the students are led to believe that the professor is male and half female. Female professors are penalised if they aren’t deemed sufficiently warm and accessible. But if they are warm and accessible they can be penalised for not appearing authoritative or professional. On the other hand, appearing authoritative and knowledgeable as a woman can result in student disapproval, because this violates gendered expectations.38 Meanwhile men are rewarded if they are accessible at a level that is simply expected in women and therefore only noticed if it’s absent.

An analysis39 of 14 million reviews on the website RateMyProfessors.com found that female professors are more likely to be ‘mean’, ‘harsh’, ‘unfair’, ‘strict’ and ‘annoying’. And it’s getting worse: female instructors have stopped reading their evaluations in droves, ‘as student comments have become increasingly aggressive and at times violent’. A female political history lecturer at a Canadian university received the following useful fredback from her student: ‘I like how your nipples show through your bra. Thanks.’40 The lecturer in question now wears ‘lightly padded bras’ exclusively.

The teaching evaluation study that revealed women are more likely to be ‘mean’ also found that male professors are more likely to be described as ‘brilliant’, ‘intelligent’, ‘smart’ and a ‘genius’. But were these men actually more in possession of raw talent than their female counterparts? Or is it just that these words are not as gender neutral as they appear? Think of a genius. Chances are, you pictured a man. It’s OK – we all have these unconscious biases. I pictured Einstein – that famous one of him sticking his tongue out, his hair all over the place. And the reality is that this bias (that I like to call ‘brilliance bias’) means that male professors are routinely considered more knowledgeable, more objective, more innately talented. And career progression that rests on teaching evaluations completely fails to account for it.

Brilliance bias is in no small part a result of a data gap: we have written so many female geniuses out of history, they just don’t come to mind as easily. The result is that when ‘brilliance’ is considered a requirement for a job, what is really meant is ‘a penis’. Several studies have found that the more a field is culturally understood to require ‘brilliance’ or ‘raw talent’ to succeed – think philosophy, maths, physics, music composition, computer science – the fewer women there will be studying and working in it.41 We just don’t see women as naturally brilliant. In fact, we seem to see femininity as inversely associated with brilliance: a recent study where participants were shown photos of male and female science faculty at elite US universities also found that appearance had no impact on how likely it was that a man would be judged to be a scientist.42 When it came to women, however, the more stereotypically feminine they looked, the less likely it was that people would think they were a scientist.

We teach brilliance bias to children from an early age. A recent US study found that when girls start primary school at the age of five, they are as likely as five-year-old boys to think women could be ‘really really smart’.43 But by the time they turn six, something changes. They start doubting their gender. So much so, in fact, that they start limiting themselves: if a game is presented to them as intended for ‘children who are really, really smart’, five-year-old girls are as likely to want to play it as boys – but six-year-old girls are suddenly uninterested. Schools are teaching little girls that brilliance doesn’t belong to them. No wonder that by the time they’re filling out university evaluation forms, students are primed to see their female teachers as less qualified.

Schools are also teaching brilliance bias to boys. As we saw in the introduction, following decades of ‘draw a scientist’ studies where children overwhelmingly drew men, a recent ‘draw a scientist’ meta-analysis was celebrated across the media as showing that finally we were becoming less sexist.44 Where in the 1960s only 1% of children drew female scientists, 28% do now. This is of course an improvement, but it is still far off reality. In the UK, women actually outnumber men in a huge range of science degrees: 86% of those studying polymers, 57% of those studying genetics, and 56% of those studying microbiology are female.45

And in any case, the results are actually more complicated than the headlines suggest and still provide damning evidence that data gaps in school curriculums are teaching children biases. When children start school they draw roughly equal percentages of male and female scientists, averaged out across boys and girls. By the time children are seven or eight, male scientists significantly outnumber female scientists. By the age of fourteen, children are drawing four times as many male scientists as female scientists. So although more female scientists are being drawn, much of the increase has been in younger children before the education system teaches them data-gap-informed gender biases.

There was also a significant gender difference in the change. Between 1985-2016, the average percentage of female scientists drawn by girls rose from 33% to 58%. The respective figures for boys were 2.4% and 13%. This discrepancy may shed some light on the finding of a 2016 study which found that while female students ranked their peers according to actual ability, male biology students consistently ranked their fellow male students as more intelligent than better-performing female students.46 Brilliance bias is one hell of a drug. And it doesn’t only lead to students mis-evaluating their teachers or each other: there is also evidence that teachers are mis-evaluating their students.

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