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.
Multiple-choice aptitude tests which required ‘little nuance or context-specific problem solving’ focused instead on the kind of mathematical trivia that even then industry leaders were seeing as increasingly irrelevant to programming. What they were mainly good at testing was the type of maths skills men were, at the time, more likely to have studied at school. They also were quite good at testing how well networked an applicant was: the answers were frequently available through all-male networks like college fraternities and Elks lodges (a US-based fraternal order).61
Personality profiles formalised the programmer stereotype nodded to by the computer-science teacher at the Carnegie Mellon programme: the geeky loner with poor social and hygiene skills. A widely quoted 1967 psychological paper had identified a ‘disinterest in people’ and a dislike of ‘activities involving close personal interaction’ as a ‘striking characteristic of programmers’.62 As a result, companies sought these people out, they became the top programmers of their generation, and the psychological profile became a self-fulfilling prophecy.
This being the case, it should not surprise us to find this kind of hidden bias enjoying a resurgence today courtesy of the secretive algorithms that have become increasingly involved in the hiring process. Writing for the Guardian, Cathy O’Neil, the American data scientist and author of Weapons of Math Destruction, explains how online tech-hiring platform Gild (which has now been bought and brought in-house by investment firm Citadel63) enables employers to go well beyond a job applicant’s CV, by combing through their ‘social data’.64 That is, the trace they leave behind them online. This data is used to rank candidates by ‘social capital’ which basically refers to how integral a programmer is to the digital community. This can be measured through how much time they spend sharing and developing code on development platforms like GitHub or Stack Overflow. But the mountains of data Gild sifts through also reveal other patterns.
For example, according to Gild’s data, frequenting a particular Japanese manga site is a ‘solid predictor of strong coding’.65 Programmers who visit this site therefore receive higher scores. Which all sounds very exciting, but as O’Neil points out, awarding marks for this rings immediate alarm bells for anyone who cares about diversity. Women, who as we have seen do 75% of the world’s unpaid care work, may not have the spare leisure time to spend hours chatting about manga online. O’Neil also points out that ‘if, like most of techdom, that manga site is dominated by males and has a sexist tone, a good number of the women in the industry will probably avoid it’. In short, Gild seems to be something like the algorithm form of the male computer-science teacher from the Carnegie programme.
Gild undoubtedly did not intend to create an algorithm that discriminated against women. They were intending
