Several studies conducted over the past decade or so show that letters of recommendation are another seemingly gender-neutral part of a hiring process that is in fact anything but.47 One US study found that female candidates are described with more communal (warm; kind; nurturing) and less active (ambitious; self-confident) language than men. And having communal characteristics included in your letter of recommendation makes it less likely that you will get the job,48 particularly if you’re a woman: while ‘team-player’ is taken as a leadership quality in men, for women the term ‘can make a woman seem like a follower’.49 Letters of recommendation for women have also been found to emphasise teaching (lower status) over research (higher status);50 to include more terms that raise doubt (hedges; faint praise);51 and to be less likely to include standout adjectives like ‘remarkable’ and ‘outstanding’. Women were more often described with ‘grindstone’ terms like ‘hard-working’.
There is a data gap at the heart of universities using teaching evaluations and letters of recommendation as if they are gender neutral in effect as well as in application, although like the meritocracy data gap more broadly, it is not a gap that arises from a lack of data so much as a refusal to engage with it. Despite all the evidence, letters of recommendation and teaching evaluations continue to be heavily weighted and used widely in hiring, promoting and firing, as if they are objective tests of worth.52 In the UK, student evaluations are set to become even more important, when the Teaching Excellence Framework (TEF) is introduced in 2020. The TEF will be used to determine how much funding a university can receive, and the National Students Survey will be considered ‘a key metric of teaching success’. Women can expect to be penalised heavily in this Excellent Teaching new world.
The lack of meritocracy in academia is a problem that should concern all of us if we care about the quality of the research that comes out of the academy, because studies show that female academics are more likely than men to challenge male-default analysis in their work.53 This means that the more women who are publishing, the faster the gender data gap in research will close. And we should care about the quality of academic research. This is not an esoteric question, relevant only to those who inhabit the ivory towers. The research produced by the academy has a significant impact on government policy, on medical practice, on occupational health legislation. The research produced by the academy has a direct impact on all of our lives. It matters that women are not forgotten here.
Given the evidence that children learn brilliance bias at school, it should be fairly easy to stop teaching them this. And in fact a recent study found that female students perform better in science when the images in their textbooks include female scientists.54 So to stop teaching girls that brilliance doesn’t belong to them, we just need to stop misrepresenting women. Easy.
It’s much harder to correct for brilliance bias once it’s already been learnt, however, and once children who’ve been taught it grow up and enter the world of work, they often start perpetuating it themselves. This is bad enough when it comes to human-on-human recruitment, but with the rise of algorithm-driven recruiting the problem is set to get worse, because there is every reason to suspect that this bias is being unwittingly hardwired into the very code to which we’re outsourcing our decision-making.
In 1984 American tech journalist Steven Levy published his bestselling book Hackers: Heroes of the Computer Revolution. Levy’s heroes were all brilliant. They were all single-minded. They were all men. They also didn’t get laid much. ‘You would hack, and you would live by the Hacker Ethic, and you knew that horribly inefficient and wasteful things like women burned too many cycles, occupied too much memory space,’ Levy explained. ‘Women, even today, are considered grossly unpredictable,’ one of his heroes told him. ‘How can a [default male] hacker tolerate such an imperfect being?’
Two paragraphs after having reported such blatant misogyny, Levy nevertheless found himself at a loss to explain why this culture was more or less ‘exclusively male’. ‘The sad fact was that there never was a star-quality female hacker’, he wrote. ‘No one knows why.’ I don’t know, Steve, we can probably take a wild guess.
By failing to make the obvious connection between an openly misogynistic culture and the mysterious lack of women, Levy contributed to the myth of innately talented hackers being implicitly male. And, today, it’s hard to think of a profession more in thrall to brilliance bias than computer science. ‘Where are the girls that love to program?’ asked a high-school teacher who took part in a summer programme for advanced-placement computer-science teachers at Carnegie Mellon; ‘I have any number of boys who really really love computers,’ he mused.55 ‘Several parents have told me their sons would be on the computer programming all night if they could. I have yet to run into a girl like that.’
This may be true, but as one of his fellow teachers pointed out, failing to exhibit this behaviour doesn’t mean that his female students don’t love computer science. Recalling her own student experience, she explained how she ‘fell in love’ with programming when she took her first course in college. But she didn’t stay up all night, or even spend a majority of her time programming. ‘Staying up all night doing something is a sign of single-mindedness and possibly immaturity as well as love for the subject. The girls may show their love for computers and computer science very differently. If you are looking for this type of obsessive behavior, then you are looking for a typically young, male behavior. While some girls will exhibit it, most won’t.’
Beyond its failure to account for female socialisation (girls are penalised for being antisocial in a way boys aren’t), the odd thing about framing an aptitude for computer science around typically male behaviour is that coding was originally seen as a woman’s game. In fact, women were the original ‘computers’, doing complex maths problems by hand for the military before the machine that took their name replaced them.56
Even after they were replaced by a machine, it took years before they were replaced by men. ENIAC, the world’s first fully functional digital computer, was unveiled in 1946, having been programmed by six women.57 During the 1940s and 50s, women remained the dominant sex in programming,58 and in 1967 Cosmopolitan magazine published ‘The Computer Girls’, an article encouraging women into programming.59 ‘It’s just like planning a dinner,’ explained computing pioneer Grace Hopper. ‘You have to plan ahead and schedule everything so that it’s ready when you need it. Programming requires patience and the ability to handle detail. Women are ‘naturals’ at computer programming.’
But it was in fact around this time that employers were starting to realise that programming was not the low-skilled clerical job they had once thought. It wasn’t like typing or feeling. It required advanced problem-solving skills. And, brilliance bias being more powerful than objective reality (given women were already doing the programming, they clearly had these skills) industry leaders started training men. And then they developed hiring tools that seemed objective, but were actually covertly biased against women. Rather like the teaching evaluations in use in universities today, these tests have been criticised as telling employers ‘less about an applicant’s suitability for the job than his or her possession of frequently stereotyped characteristics’.60 It’s hard to know whether these hiring tools were developed as a result of a gender data gap (not realising that the characteristics they were looking for were male-biased) or a result of direct discrimination, but what is undeniable is that they were biased towards men.