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 to remove human biases. But if you aren’t aware of how those biases operate, if you aren’t collecting data and taking a little time to produce evidence-based processes, you will continue to blindly perpetuate old injustices. And so by not considering the ways in which women’s lives differ from men’s, both on and offline, Gild’s coders inadvertently created an algorithm with a hidden bias against women.
But that’s not even the most troubling bit. The most troubling bit is that we have no idea how bad the problem actually is. Most algorithms of this kind are kept secret and protected as proprietary code. This means that we don’t know how these decisions are being made and what biases they are hiding. The only reason we know about this potential bias in Gild’s algorithm is because one of its creators happened to tell us. This, therefore, is a double gender data gap: first in the knowledge of the coders designing the algorithm, and second, in the knowledge of society at large, about just how discriminatory these AIs are.
Employment procedures that are unwittingly biased towards men are an issue in promotion as well as hiring. A classic example comes from Google, where women weren’t nominating themselves for promotion at the same rate as men. This is unsurprising: women are conditioned to be modest, and are penalised when they step outside of this prescribed gender norm.66 But Google was surprised. And, to do them credit, they set about trying to fix it. Unfortunately the way they went about fixing it was quintessential male-default thinking.
It’s not clear whether Google didn’t have or didn’t care about the data on the cultural expectations that are imposed on women, but either way, their solution was not to fix the male-biased system: it was to fix the women. Senior women at Google started hosting workshops ‘to encourage women to nominate themselves’, Laszlo Bock, head of people operations, told the New York Times in 2012.67 In other words, they held workshops to encourage women to be more like men. But why should we accept that the way men do things, the way men see themselves, is the correct way? Recent research has emerged showing that while women tend to assess their intelligence accurately, men of average intelligence think they are more intelligent than two-thirds of people.68 This being the case, perhaps it wasn’t that women’s rates of putting themselves up for promotion were too low. Perhaps it was that men’s were too high.
Bock claimed Google’s workshops as a success (he told the New York Times that women are now promoted proportionally to men), but if that is the case, why the reluctance to provide the data to prove it? When the US Department of Labor conducted an analysis of Google’s pay practices in 2017 it found ‘systemic compensation disparities against women pretty much across the entire workforce’, with ‘six to seven standard deviations between pay for men and women in nearly every job category’.69 Google has since repeatedly refused to hand over fuller pay data to the Labor Department, fighting in court for months to avoid the demand. There was no pay imbalance, they insisted.
For a company built almost entirely on data, Google’s reluctance to engage here may seem surprising. It shouldn’t be. Software engineer Tracy Chou has been investigating the number of female engineers in the US tech industry since 2013 and has found that ‘[e]very company has some way of hiding or muddling the data’.70 They also don’t seem interested in measuring whether or not their ‘initiatives to make the work environment more female-friendly, or to encourage more women to go into or stay in computing’, are actually successful. There’s ‘no way of judging whether they’re successful or worth mimicking, because there are no success metrics attached to any of them’, explains Chou. And the result is that ‘nobody is having honest conversations about the issue’.
It’s not entirely clear why the tech industry is so afraid of sex-disaggregated employment data, but its love affair with the myth of meritocracy might have something to do with it: if all you need to get the ‘best people’ is to believe in meritocracy, what use is data to you? The irony is, if these so-called meritocratic institutions actually valued science over religion, they could make use of the evidence-based solutions that do already exist. For example, quotas, which, contrary to popular misconception, were recently found by a London School of Economics study to ‘weed out incompetent men’ rather than promote unqualified women.71