But if the data gap of what women actually want is fairly easily fixed by, well, asking women, there’s another more chronic gap: data on the female body itself. Boler developed her first product – Elvie, a smart pelvic-floor trainer – after realising that poor pelvic-floor health in women was ‘a massively hidden epidemic’: 37% of women suffer from pelvic-floor issues; 10% of women will need to have an operation at some point because of prolapse (where your organs start dropping through your vagina). This rises to 50% of women over fifty.
‘There’s a sense of injustice,’ says Boler. ‘It’s a big issue for women and it should be a normal part of how women look after their bodies. But you need to have information and data in order to do that.’ And when Boler was first researching the issue, that data simply didn’t exist. ‘We were trying to design a product which fits in the vagina, and so we needed to answer simple questions like, what size, how does it vary by age, by race, after children – all the usual questions. And there just was no data there at all. [. . .] Fifty per cent of the population have a vagina,’ she continues, ‘and yet there’s hardly any journal articles about this part of anatomy. Three years ago I found about four articles done decades ago.’ One of them was ‘literally by a guy who basically made a kind of plaster cast, like a mould inside the vagina, and concluded that there were four shapes: a mushroom and a cone and a heart . . .’ she trails off laughing.
Problems with pelvic-floor health are often preventable, and the evidence base for pelvic-floor training is ‘very strong’, Boler tells me. ‘It’s the number-one line of defence and it’s recommended under the NICE [National Institute for Health and Case Excellence] guidelines in the UK.’ But when she started looking at the technology in hospitals, ‘there had been no investment. It was so outdated, it was very unreliable and not even very valid.’ The current treatment for prolapse (to insert a mesh into the vagina) is the subject of an ongoing scandal in the UK, as hundreds of women have been left in severe, debilitating pain, by what they describe as ‘barbaric’ treatment.12 In Scotland, a woman just died.
Ida Tin, founder of menstrual-tracking app Clue, encountered the same problem when she started trying to find an alternative to traditional contraception. ‘Menstruation is listed as one of the vital signs of the body,’ she tells me. ‘The same as: do you have a heart rate, are you breathing, what’s your body temperature. It’s a really strong indication of your health.’ And yet ‘it’s also an area where there is so much taboo and misinformation.’ As for family planning, Tin points out that ‘there’s been very little innovation since the pill came out in the 1950s. I mean, in the history of technology that’s a really long time.’
Tin set up Clue because she wanted to ‘enable women to take control of their own body and lives’, but the motivation was also personal. She’d tried the pill, but, like many women, she’d had side effects. ‘And I hadn’t had any children so an IUD wasn’t ideal. So I’d been using condoms for fifteen years.’ In frustration, Tin started looking at patent databases, but ‘everything was about putting hormones into the body’, she tells me. ‘And I felt it was a very non-data-driven approach to this problem. It made me a little bit provoked, like: why is it that nobody has given this some serious effort and consideration? It’s a pretty basic need for humankind.’
When she had the idea for the menstrual-tracking app there were only a couple of period-tracking apps available. ‘And they were very first-generation products – basically a calendar that can count to twenty-eight. And if only our biology were that simple’, she laughs. After a decade of being in the sector, Tin says, the science is still riddled with gaps. ‘There really is a lack of data,’ she tells me. Menstruation has been ‘not just overlooked, but borderline actively ignored. We do a lot of work together with science institutions because there are really a lot of blank areas on the academic map. Like, what’s even considered a normal bleeding pattern for an adolescent woman? That’s one of the things we’ve been working on with Stanford. Science just doesn’t know what’s normal.’
Given the male domination of VCs, data gaps are perhaps particularly problematic when it comes to tech aimed at women. ‘If you don’t have good data,’ explains Tin ‘it’s harder to open people’s minds that something might be an issue if they don’t encounter it themselves.’ Boler agrees. ‘We did talk to some VC investors who didn’t believe [Elvie] was an interesting proposition,’ she tells me.
The other problem women face when it comes to getting investment is ‘pattern recognition’.13 A corollary of ‘culture fit’, pattern recognition sounds data-driven, but it’s basically just a fancy term for looks-similar-to-something-that-has-worked-in-the-past – where ‘something’ could be white-male-founder-who-dropped-out-of-Harvard-and-wears-hoodies. Genuinely: I dated a guy who was working on a start-up and he referenced this uniform when he was talking about getting funding. Hoody-based pattern recognition is real. And this emphasis on recognising a typically male pattern may be exacerbated by the common belief that tech is a field where inborn ‘genius’ (which, as we’ve seen, is stereotypically associated with men14) is more important than working hard (hence fetishising Harvard dropouts).
It all feels rather catch-22ish. In a field where women are at a disadvantage specifically because they are women (and therefore can’t hope to fit a stereotypically male ‘pattern’), data will be particularly crucial for female entrepreneurs. And yet it’s the female entrepreneurs who are less likely to have it, because they are more likely to be trying to make products for women. For whom we lack data.
Still, some do manage tob break through. Tin and Boler got their funding (Boler in part from Woskow). And now these specific data gaps are starting to be filled. Before they launched, Chiaro had over 150 women test their pelvic-floor trainer, Boler tells me. ‘But we now have data on over a million workouts and we have a lot of measurements around pelvic-floor health which just haven’t existed before.’ This, she says, is the ‘exciting thing about wearable tech: giving people better information about their bodies so they can make more informed decisions’.
*
But while Boler’s and Tin’s products may give women better information about their bodies, the same can’t be said for all new tech, wearable or otherwise. In the tech world, the implicit assumption that men are the default human remains king. When Apple launched its health-monitoring system with much fanfare in 2014, it boasted a ‘comprehensive’ health tracker.15 It could track blood pressure; steps taken; blood alcohol level; even molybdenum (nope, me neither) and copper intake. But as many women pointed out at the time, they forgot one crucial detail: a period tracker.16
This was not to be the only time Apple completely forgot about at least 50% of their users. When Apple launched their AI, Siri, she (ironically) could find prostitutes and Viagra suppliers, but not abortion providers.17 Siri could help you if you’d had a heart attack, but if you told her you’d been raped, she replied ‘I don’t know what you mean by ‘I was raped.’18 These are basic errors that surely would have been caught by a team with enough women on it – that is, by a team without a gender data gap.
Products marketed as gender-neutral that are in fact biased towards men are rife across the (male-dominated) tech industry. From smartwatches that are too big for women’s wrists,19 to map apps that fail to account for women’s desire for ‘safest’ in addition to ‘fastest’ routes; to ‘measure how good you are at sex’ apps called ‘iThrust’20 and ‘iBang’21 (and yes the in-built assumptions of what constitutes good sex are exactly what the names imply), the tech industry is rife with examples of tech that forget about women. Virtual reality (VR) headsets that are too big for the average woman’s head; a ‘haptic jacket’ (a jacket that simulates touch) that fits snugly on a male body, but on a female reviewer’s body ‘could have fit over a puffy winter coat’; augmented-reality glasses whose lenses are too far apart for a woman to focus on the image, ‘or whose frames immediately fall off my face’. Or, as I know from my experi
ence of going on TV and giving public lectures, mic packs that require either a waistband or substantial pockets to attach to. Out goes pretty much every dress ever designed.
Defaulting to male seems particularly endemic in sports tech. Starting with the most basic, the calorie count on treadmills is perfect for practically no one, but it will be more accurate for your average man because its calculations are based on the average male weight (the default setting for calorie count on most exercise machines is for a person who weighs eleven stone). And although you can change the weight setting, that still leaves a calculation based on an average male calorie burn. Women generally have a higher fat and lower muscle distribution than men as well as different ratios of various muscle fibres. What this means at a basic level is that even after accounting for weight difference, men on average will burn 8% more calories than a woman of the same weight. The treadmill does not account for this.
There’s no reason to think that things improved much with the advent of wearables, either. One study of twelve of the most common fitness monitors found that these underestimated steps during housework by up to 74% (that was the Omron, which was within 1% for normal walking or running) and underestimated calories burned during housework by as much as 34%.22 Anecdotally, Fitbits apparently fail to account for movement while doing the extremely common female activity of pushing a pram (yes of course men push prams too, but not as often as the women who do 75% of the world’s unpaid care). Another study, which unusually did manage to include almost 50% female participants, found that fitness devices were overestimating calorie burn by significant amounts.23 Unfortunately, they failed to disaggregate their data so it is impossible to know if there were any sex differences.
Tech developers even forget women when they form the potential majority of customers. In the US, women make up 59% of people over the age of sixty-five and 76% of those living alone, suggesting a potential greater need for assistive technology like fall-detection devices.24 The data we have suggests that not only do older women fall more often than men, they also injure themselves more when they do.25 Data analysis of a month’s worth of emergency department visits in the US found that of the 22,560 patients seen for fall injuries, 71%, were women. The rate of fracture was 2.2 times higher in women, and women had a hospitalisation rate 1.8 times that of men.26
And yet despite women’s arguably greater need (as well as research indicating that women tend to fall differently, for different reasons, and in different places), gender analysis is missing from the development of this technology. In one meta-analysis of fifty-three fall detection device studies, only half of them even described the sex of participants, let alone delivered sex-disaggregated data;27 another study noted that ‘Despite extensive literature on falls among seniors, little is known about gender-specific risk factors.’28
The Proceedings of the 2016 International Conference on Intelligent Data Engineering and Automated Learning points out that ‘a notable motivation for elders to reject fall-detection devices is their size’, suggesting mobile phones as a solution.29 Except this isn’t really a solution for women because as the authors themselves note, women tend to keep their phones in their handbags, ‘where fall-detection algorithms will likely fail because they are trained to detect falls through acceleration sensors close to the body trunk’.
In acknowledging this, the authors are unusual. Whitney Erin Boesel, a researcher at the Berkman Center for Internet and Society at Harvard, is a member of the ‘quantified self’ community, which promises ‘self-knowledge through numbers’. These numbers are often collected via passive tracking apps on your phone, the classic being how many steps you’ve taken that day. But there’s a pocket-sized problem with this promise: ‘Inevitably some dude gets up at a conference and [says] something about how your phone is always on you,’ Boesel told the Atlantic.30 ‘And every time I’ll stand up, and I’ll be like, “Hi, about this phone that is always on you. This is my phone. And these are my pants.”’
Designing passive tracking apps as if women have pockets big enough to hold their phones is a perennial problem with an easy solution: include proper pockets in women’s clothing (she types, furiously, having just had her phone fall out of her pocket and smash on the floor for the hundredth time). In the meantime, however, women use other solutions, and if tech developers don’t realise women are being forced into workarounds, they may fail in their development.
A Cape Town-based tech company fell into this trap when they developed an app to help community health workers monitor HIV-positive patients. The app ‘fulfilled all the usability requirements; it was easy to use, adaptable to local language’ and solved a very specific issue. More than this, the community health workers were ‘excited at the prospect of using it’.31 But when the service was launched, it proved to be a flop. Despite several attempts to solve it, the problem remained a mystery until a new design team took over the project. A team that happened to have a woman in it. And this woman ‘took only a day to discover the problem’. It turned out that in order to more safely complete their daily commute into the townships where their patients lived, female health workers were concealing their valuables in their underwear. And the phone was too big to fit in their bras.
Gender affects the kinds of questions we ask, says Margaret Mitchell, senior research scientist at Google. Limiting AI developers to one gender, she told Bloomberg News, puts companies ‘in a position of myopia’.32 Gayna Williams, ex-director of user experience at Microsoft, agrees.33 In a blogpost titled ‘Are you sure your software is gender-neutral?’ Williams explains that all product design begins by deciding which problem needs solving. And that is all a matter of perception: what problem were NASA scientists solving when they decided to give Valerie, their space-navigation robot, breasts?34
On the topic of sexy robots, even if men do identify a problem that affects us all, that doesn’t mean that without female input they will come up with the right solution. When, in ‘retaliation’ for women denying him the sex to which he believed he was entitled, Alek Minassian mowed down and killed ten people in Toronto with a rented van, the New York Times published a column headlined ‘The Redistribution of Sex’, which argued that sex robots could be the answer to the plight of men who can’t convince women to sleep with them. Feminists might argue that the solution is, instead, to challenge male sexual entitlement.
When it comes to the tech that ends up in our pockets (I’m ever hopeful), it all comes down to who is making the decisions. And like the world of venture capitalists, the tech industry is dominated by men. Margaret Mitchell calls this the ‘sea of dudes’ problem.35 Over the past five years she’s worked with around ten women and ‘hundreds’ of men. Across ‘professional computing’ as a whole in the US, 26% of jobs are held by women compared to the 57% of jobs women hold across the entire US workforce.36 In the UK, women make up 14% of the STEM workforce.37
As well as a rash of sexy robots, the sea of dudes leads to products like the ‘enormous robot research prototype called PR2’ that computer scientist and co-founder of a robotics company Tessa Lau encountered when she worked for robotics research lab Willow Garage. It weighed ‘hundreds of pounds – it’s much larger than a smaller woman – and it has two big arms. It looks really scary. I didn’t even want one of those things near me if it wasn’t being controlled properly.’ When I interviewed her a couple of years ago, roboticist Angelica Lim told me a similar story about the robot she encountered at a conference in Slovenia, which would come and shake your hand if you waved at it. When she waved at this 5’8’ robot on wheels (the average American woman is 5’4’) the robot slowly turned towards her, put out its hand, and then came ‘barrelling towards me, fast’, making her jump backwards and shriek.
Contrast these examples to the virtual-reality headset trialled by tech journalist Adi Robertson.38 The headset was meant to track her eyes, but it didn’t work for her – until an employee asked if she was wearing mascara. ‘When it got recalibrated perfectly a fe
w minutes later, I was surprised – not by the fact that it worked, but by the fact that anyone had thought to troubleshoot make-up. Incidentally,’ she writes, ‘this was one of the only VR start-ups I’ve ever covered with a female founder.’
Most VR companies aren’t founded by women, however, and so the VR experience often comes with an in-built male bias. Like much of the online world, VR gaming seems to have a sexual harassment problem – and this problem is something VR’s mainly male developers are routinely forgetting to account for.39
When author and gamer Jordan Belamire tried the VR game QuiVr in multiplayer mode, she was sexually assaulted by another user called BigBro4 42.40 ‘Virtual’ makes it sound like it isn’t real – but it felt real to Belamire. And no wonder. VR is meant to feel real, and it can be so successful at tricking your brain that it is being explored as a treatment for PTSD, phobias, even phantom-limb syndrome.41
To be fair to the male designers of QuiVr, they had an excellent and proactive response to Belamire’s blog.42 They immediately redesigned their ‘Personal Bubble’ setting (in which other player’s hands disappear if they come close to your face) to cover the entire body and so make such groping impossible. But as they themselves noted, while they had thought ‘of the possibility of some silly person trying to block your view with their hands and ruining the game’, they hadn’t thought of extending the fading function to the rest of the body. How, they asked, ‘could we have overlooked something so obvious?’
Invisible Women: Exposing Data Bias in a World Designed for Men Page 18