Instant Genius - Does data discriminate against women? – Caroline Criado Perez

Episode Date: September 11, 2019

When Apple launched its health tracker app HealthKit in 2014, they promised users the ability to track everything from their blood pressure to their copper intake – but not their periods. This seems like a startling oversight, but Apple aren’t alone in failing to consider women’s needs. For example, it wasn’t until 2015 that the EU required new cars to be tested on a female crash-test dummy. Caroline Criado Perez, whose book Invisible Women (£16.99, Chatto and Windus) has been shortlisted for the 2019 Royal Society Science Book Prize, calls this the gender data gap, and it appears in everything from public policy to medical research. In this episode of the Science Focus Podcast, we talk to Caroline about the gender data gap and how it causes everything from mild inconvenience to potential fatality. She speaks to BBC Science Focus online assistant Sara Rigby. Listen to more episodes of the Science Focus Podcast which we think you will find interesting: Why aren’t there more women in science? Is racism creeping into science? – Angela Saini Is religion compatible with science? – John Lennox Is body positivity the answer to body image issues? – Phillippa Diedrichs What makes me ‘me’? – Aoife McLysaght Inequality in science – Angela Saini Follow Science Focus on Twitter, Facebook, Instagram and Flipboard Hosted on Acast. See acast.com/privacy for more information. Learn more about your ad choices. Visit podcastchoices.com/adchoices

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delivering digital precision with analogue warmth. So you can experience exceptional sound at home. Music just as the artist intended. Visit name audio.com. to learn more. And so the gender data gap is not a conspiracy. It's not a result of, you know, people being evil and hating women and wanting us to die in car crashes or to die from heart attacks that are misdiagnosed. You know, it'd almost be easier if that were the case, you know, if there was an evil actor here that we could sort of tackle. The issue is that it's all of us,

all of us not realizing that we're defaulting to male because it happens so naturally. because it happens without us noticing it. You're listening to the Science Focus podcast from the BBC Science Focus magazine team. With the UK's best-selling science and technology monthly, available in print and in several digital formats throughout the world. Find out more at ScienceFocus.com will look out for us in your app store.

Hello and welcome to the Science Focus podcast. I'm Alexander McNamara, online editor at BBC Science Focus magazine. When Apple launched its Health tracker health kit in 2014, they promised users the ability to track everything, from their blood pressure to their copper intake, but not their periods. This seemed like a startling oversight, but Apple aren't alone in failing to consider women's needs.

For example, it wasn't until 2015 that the EU required new cars to be tested on a female crash dummy as well. Caroline Creado Perez, whose book Invisible Women has just been shortlisted for the 2019 Royal Society Science Book Prize, calls this the gender data gap, and it appears in everything from public policy to medical research. She speaks to our online assistant, Sarah Rigby, about the gender data gap and how it causes everything from mild inconvenience to potential fatality.

What is your book Invisible Women about? Invisible Women is about the gender data gap. And the gender data gap is a way of talking about the fact that the vast majority of information that we have collected in the world historically and currently has been based on male bodies and typical male lifestyle patterns. And the book is really about documenting both sort of why this happens, how it happens, and then also the impact of it. And the impact is at its heart that the world has just not been designed for women and that

women suffer all sorts of consequences as a result of this. And these consequences range from things that can seem rather trivial, like not being able to reach a top shelf or shivering in an office because it's too cold because it's set at a male temperature norm. But it can also be incredibly serious and potentially fatal. So, for example, dying in a car crash because cars have been designed to be safe for the average man, not the average woman. So the phrase you use a lot is the default male. So could you explain that of it, please? So what the default male is about is basically that as a society,

we have been conditioned to think of men when we think of human beings. We think of women when we specify women. But if we don't specify the gender, we tend to picture men. And this has been documented in all sorts of studies, from sort of draw a scientist studies to draw a person studies, where people just repeatedly are far more likely to draw men than to draw women. And the reason this is important, and the reason that I try to really highlight it in the book,

is that we have to understand that this is a bias that we all suffer from. And so the gender data gap is not a conspiracy. It's not a result of, you know, people, being evil and hating women and wanting us to die in car crashes or to die from heart attacks that are misdiagnosed. You know, it would almost be easier if that were the case, you know, if there was an evil actor here that we could sort of tackle. The issue is that it's all of us, all of us not realizing that we're defaulting to male because it happens so naturally, because it happens without us noticing it. And so it means that we don't even realize that the data gap is

happening. So that's really what that's about. It's about trying to, first of all, explain why we keep not collecting data on women, why there's so little awareness of it, but also to try to get people to the point where they aren't feeling defensive about it, where they're prepared to sort of join together and try and change it. Because I think that that is often a problem when talking about discrimination, people often do get quite defensive because they feel it's an attack on them. And what I'm trying to say with this, to say with this book is that actually we all do this, we all need to get better at it, and that it really matters. You know, if you care about women dying, you care about the gender data gap. Often though, it sounds like it's not just

because the data isn't collected, but because the data isn't separated into men and women. Is that right? Yes. So a lot of the time we aren't collecting data on women. But sometimes we are collecting data on men and women, but we're just not sex disaggregating it. And the problem there is that all sorts of findings get hidden and lost. And this can be a big problem, for example, in the medical world, where there have been a lot of quite significant sex differences found in the prevalence in course of most major diseases, in the way men and women respond to certain medication. So it's actually really important in the medical world to be able to look at data for men

versus women and be able to tailor health policy and medical research and development around those two different responses. But it's just something that hasn't been happening very often. And so as a result, it's difficult to be able to tailor medication in a sex-specific way. And this is still going on. So just the other day, I read a paper about looking at depression as a risk factor for Alzheimer's. Now, Alzheimer's is a disease that is more prevalent in women. And we don't exactly know why. It's partly because women tend to live slightly longer than men, but that doesn't explain all of it. And so there was this study trying to do a meta-analysis of research that had gone before to try and understand if they were. sex-specific links between depression and Alzheimer's. And out of all the studies that they looked at, you know, huge number of studies, only three of them had enough data and only seven of them had sex disaggregated their data. So there was this whole analysis done. And at the end of the

analysis, all they were able to say was we weren't able to find anything. The data we did have looked like there might be sex-specific responses, but there just isn't enough data to be able to tell. Absolutely. And medical research is one area in which the lack of data around women can be deadly. Can you tell us a bit about that, please? Well, it sort of plagues the whole medical world. There are all sorts of really shocking examples where a failure to collect sex data results in. huge health disparities for women. I mean, the most common one probably is cardiovascular health, where women are 50% more likely to be misdiagnosed if they have a heart attack, which obviously can be fatal. And that's for all sorts of reasons. That's partly because of public health information, not teaching women how to recognise a heart attack or to even know that they're

likely to get a heart attack. You know, so much, this goes back to the default male, so much public health information uses a man. You know, if you Google heart attack, you will find pictures of men. You won't find pictures of women. And so that sort of leads women to not realize they're having a heart attack so they don't go to a doctor. But then when they get to the doctor, the same thing is happening. The doctors aren't recognizing what typical female symptoms might be or that women are likely to be having heart attacks. They're less likely to send them for tests. Even if they do send them for tests, these tests have been developed around the way heart attacks may mechanically progress in men. And there are potential.

differences. Again, because the data is so bad, it's hard to know exactly what those differences are, but there is research suggesting that there are differences. And so you have women being sent home having been presented with chest pain, which is seen as a typical heart attack symptom, and they've been sent for a test, and the test has found no blockages, and they've been sent home. And actually, women's heart attacks may not present with blockages in the same way. And so these women have had heart attacks, but have been sent home with undiagnosed chest pain. And of course, that can be fatal. And there's all sorts of other examples.

One example that I thought was so interesting that really highlights why you should be sex disaggregating your data in the medical field was that for years, researchers didn't, well, doctors didn't understand why when they transplanted muscle cells, sometimes these cells would regenerate and sometimes they wouldn't. And they thought that they were just unpredictable. And it wasn't until years later that somebody really. realized they're not unpredictable at all. It's that female cells regenerate and male cells don't. Similarly, another study I came across I thought was incredibly interesting, but also very

tantalizingly frustrating was a cell study that exposed male and female cells to estrogen to see whether they would be able to use the estrogen to fight off a virus. The female cells were able to use the estrogen and fought off the virus. The male cells were not able to use the estrogen. Now, the problem with that is that the vast majority of clinical studies have been done exclusively in male cells, male animals, male humans. And so you hear about a study like that and you think, how many medications for women have we missed out on simply because at the cell trial stage, they didn't work for men. And then you add that to the fact that women are much more likely to experience adverse

drug reactions, experience much more severe adverse drug reactions, are more likely to be hospitalized. And then, you know, you start feeling quite angry about why this continues to happen. Do you think that knowing this, women should therefore preferentially search out, you know, female GPs and female experts? Do you think that's more likely to get them the medical help they need? Well, it's hard to say because, you know, a doctor is only so good as the data that actually exists. And the data just really isn't there a lot of the time for women. We don't have enough data on women. So a female doctor, may be better in that she may have experienced the kinds of issues that you're facing. You know, she's more likely, for example, to have experienced endometriosis or to have experienced the menopause. I mean, for example, just on Twitter yesterday, I was informed of

a patient who tried to book an appointment to see her doctor to talk about the menopause and was told the male doctors don't do that, which I'm pretty sure is illegal. But there is some evidence to suggest that, yes, you may be better off of a female doctor. And certainly there's evidence to suggest that, well, to demonstrate that the presence of women in a clinical research team means that the data is more likely to be both collected on women and sex and gender analyzed. So, yes, there is a gender element here, but that comes with a proviso that we just don't have the data that is needed to be able to treat women as well as men, no matter what the sex of the doctor is. So medical care is something that women can expect to need at some point in their life. So that's an instance when they might experience this gender data gap for themselves. So where else might in their daily life women experience the gender data gap?

Well, pretty much as they go about their daily life, they are constantly experiencing it. So, for example, if you get onto some public transport and you can't reach the handrail because it's set too high and you fall over, that is a result of the gender data gap because it has been set according to a height that can be easily reached by the average man. There are examples from, for example, the way that public transport has been designed in the way that it gets you around the city has been designed. to accommodate typical male travel patterns more easily than typical female travel patterns, even though women are more likely to use public transport than men. So what I mean by that is that men typically tend to drive and are more likely to do a twice daily, very simple commute in and out of work. Women, because of their unpaid care work responsibilities, so women do 75% of the world's

unpaid care work, tend to do a more complicated type of travel, which is called trip chaining. And that means they are doing things like dropping the kids off at school before they go into work, maybe picking some groceries up on the way home. But because of the way we've set out public transport to facilitate the commute rather than this more complicated travel pattern that involves sort of going into suburbs and around the city rather than just sort of in and out, women have much more difficulty traveling. They're having to change more often. They're having to take more circuitous routes. How does that connect to the gender data gap? Basically, because the way that we've collected data has not accounted for the differences

between the way men and women travel. And this is partly a case of simply not collecting the data on the women. But interestingly, it's also about the way that the data is segregated. So traditionally, data will be separated out into employment, will be one big block, travel for employment. and then things like travel for education, travel for escorting older people or younger children, travel for shopping, travel for various different things. And the problem with that way of segmenting the data is that women's unpaid care work is split up amongst lots of different sections.

And so it seems like employment really is the main reason people, the single most important reason that people are travelling. But if you segment the data so that women, unpaid care work travel isn't separated up and combined with, you know, shopping for leisure rather than shopping for the groceries for the family, it was discovered that that was the single biggest reason that women use public transport and also it was almost as big as the number of trips were almost as many as the number of trips made for employment. So because we collect and segment data in a way that doesn't account for

the way women are traveling, we've designed a transport system that doesn't account for it. So that's another way. A very common way would be, as I mentioned at the beginning, air conditioning and offices. So the formula for determining the temperature in an office was figured out in the 60s, and it was based on the basal metabolic rate of a 40-year-old male. But it turns out the metabolic rate of a woman performing office work is a fair amount lower. And the result of that is that offices are on average about five degrees too low for the average woman, which explains why women are often to be found wrapping themselves up in cardigans while men are just sort of walking around in shirt sleeves. What other examples are there? Is that enough? Do you want more

examples? One that I found particularly surprising was your example of snow clearing. Right, well, that's sort of connected to the travel issue again. So this was in a town called Carl Skoga in Sweden, where they were doing a gender audit of all their local government policies. And someone suggested, you know, as a joke, actually, they should look at the snow clearing because the idea was, you know, ridiculous to think that snow clearing could be sexist. Of course it can't be sexist. only of course it turned out that the snow clearing was sexist. And the reason for that was about these differences between the way men and women typically tend to travel. So the snow clearing had the snow clearing schedule had been designed to clear the major road arteries first and then the local roads and pavements.

And you know, as I said, men are more likely to drive in and out of work using major roads. And so that was privileging their travel over the type of travel that women are doing on local roads and suburbs, dropping the kids off at school, using public transport, then going to work. And so they thought, well, this doesn't really make that much sense because actually it's probably easier to drive a car through three inches of snow than it is to walk or push a buggy. So we should just change that. And so they switched it around and they sort of thought, well, this won't cost us any more money and it seems fairer. what they didn't realize was that it would actually end up saving the city money. And the reason for that is that they were right. It is easier to drive a car through three inches of snow than to push a buggy or to walk.

And the vast majority of people being admitted to accident and emergency for single person accidents were pedestrians. And the vast majority of those pedestrians were women. And the cost of the admissions to accident and emergency. during winter months was something like three times the cost of winter road maintenance. So this was not a negligible money saving. It was a fairly substantial money saving. And it was just done by collecting data, collecting sector segregated data, and then designing policy around that data, rather than as had been done originally and what was sort of often done was, you know, they'd just sort of thought, well, this works for us, you know, the original

planners who would have been men, you know, best will in the world design the schedule thinking this is what makes sense. And it did make sense for them. But that's sort of the reason that you need to collect data because you can't possibly know if you're a sort of homogenous group what the needs of all your citizens are going to be. And I just really love that example because they just went into it so clueless thinking, A, this isn't going to, you know, make it have any job. gender impact, then discovering it did have a gender impact, and then on top of that, that they had been losing all this money year after year for no good reason. In STEM, there's a particularly big problem of the default male, as you mentioned earlier.

Most people would imagine that a scientist is male by default. So how might that affect women in the STEM industries? Well, I mean, it makes it harder for them to get jobs for a start. there is a lot of discrimination in hiring and promotion in STEM that is hugely related to this idea of what a typical scientist, what a typical engineer looks like. And the problem that the tech industry has is that it seems to be very reluctant, weirdly, you know, for an industry that is so enthralled to the idea of data and big data and data analysis and doing everything by data, somehow when it comes to hiring and promoting

they're not that interested in all the data because all the data is telling them that it's not good enough to say that you're meritocratic. You have to actually put procedures in place. In fact, more than it not being good enough, there is a very strong evidence to suggest that simply thinking of yourself as meritocratic makes you less meritocratic. So companies that have meritocracy as part of their ethos tend to be more likely to hire white men, which is sort of fascinating as a sort of psychological analysis. But, you know, so that's part of the problem. But then the problem extends far beyond the female workforce because this is a problem for all of us, because the tech industry is essentially creating the future for us.

They are, they are the future. They are coding the algorithms that are running our lives. They're creating the products that we are all using and that are integrated into pretty much every aspect of our lives now. And if they are all being designed by, you know, majority middle-class white men from America, that means that the gap between middle-class white men from America and everyone else is only going to get worse. And there are already lots of really concerning examples about this of things like, you know,

job adverts for tech jobs being less likely to be shown to women because algorithms haven't been designed to account for male versus female behavior online. Examples like there was a really interesting study, I think, from the University of Washington looking at algorithms trained on Google images and finding that they massively amplified the bias. So, if you, the data set, for example, had 33% of pictures of women involved stoves. And so the algorithm learnt that stove equals women. But it made it so much worse. Because the thing about machine learning is that it's amplifying the bias in the data that

you feed it. And so by the time the algorithm was finished, it decided that 68% of the pictures featuring stoves were of women. lots of examples of men being labeled as women. Other examples include sort of voice recognition software that don't recognize women's voices. A very famous one, of course, is this isn't voice recognition software, but Apple's healthcare health tracker app that included a copper tracker but failed to include a menstrual tracker. And, you know, that, again, I really like as an example because it is so clearly

just not having remembered that periods are a thing that happen. I don't think that Apple wanted to stick two fingers up to its female customers. It obviously wants to be able to sell its product. This was a feature of having not had a diverse enough team. So none of those examples might sound particularly worrying on their own. But when you start thinking about all the areas that tech is making headway into, for example, medical diagnoses or hiring. I mean, 72% of CVs in the US already don't reach human eyes.

And these algorithms are being trained on incredibly biased data. So, yes, women in STEM face a lot of discrimination and a lot of problems when it comes to the default male. But so do we all. because by failing to have enough women in STEM, you're creating, your coding inequality into the future, and that's incredibly worrying. So if I were working for a tech company designing, for example, driverless cars or a voice recognition system or something like that, what could I do to ensure that I don't inadvertently create a gender data gap? Well, the first thing you need to do is make sure that your team is diverse because it's not just a question.

of collecting data. You have to move a step back from that and question what are the questions that I'm asking? Because you need to know what the right questions are. You need to know what you need to collect data on. And you just aren't going to remember or know about all the things that you need to collect data on. You won't know about various discriminations that women face. So that's the first thing is before doing anything, make sure that your team is diverse so that you know that you're asking the right questions and collecting data on the right things and then asking the right questions of that data. And then the second thing is to collect sex disaggregated data. And those are basically the two key takeaways that I really want everyone to take from this book. that diversity is an integral part of the design process.

It is not a tick box that is an optional extra that makes you look good at the end of the year on your CSR report. It's actually fundamental to creating products that are going to serve everyone equally. And then the collecting sex disaggregated data, because ultimately that is going to be at the heart of making sure that you are designing products, but also policies. you know, this is incredibly relevant for governments as well, that you're creating, anything that you're creating is going to be fair and equitable and actually serve the people that you intend it to serve. Right. And with STEM in academia,

it sounds like female academics tend to get a very different workload to their male colleagues. Could you tell us a bit about that, please? Yeah. So, well, basically, women are much more likely to get heavier teaching. And they are also more likely to be given sort of all sorts of admin roles, whereas men are more likely to be left to get on with their research. And there are a number of problems with that.

First of all, that the way teaching is evaluated is not gender equitable at all. because it's evaluated based on student feedback. And all the research that has been done on these kinds of evaluation showed that students are sexist. And they are giving totally, you know, putting totally different expectations on their male versus their female instructors. To the extent that when a study was done

where the same person was delivering a course, this was an online course, but for one set of students, it was presented as a male instructor, and for another set of students, it was presented as a female instructor, the evaluations were just wildly different, right down to how promptly

their marking was handed back. The woman was handing it back much later. That was obviously impossible because it was the same person doing it. And so they were handed back at exactly the same time. So women get much lower evaluations. They get marked much more

harshly for talking about anything other than white men. They have much higher expectations of sort of being nice and open and warm, but also, you know, that's a double edge soared, because if you're warm, then you're not seen as professional. So they're basically a

very, very unscientific way of evaluating teaching. And women have a much harsher time of it, and yet women are given much more of this work. But then on top of that, of course, the way that academia is structured means that we don't really value teaching anyway. You know, the thing that gets you your jobs is research. And women are just left with much less time to do this research because of the teaching expectations for which they are lower marked and because of the admin roles that they expected to take on, and which again, they are penalised more for if they don't do it because of this expectation that women are pro-social and will do lovely things for everyone. And of course, I'll take on, you know, head of department this year. No problem at all.

So there's those issues of not having time to do the research that they need to do. And then, of course, even worse than that, even if they do have time to do the research, their research is, again, judged by different standards. So they get less credit for it often, particularly if they are co-authors. so men get much more credit for co-authoring a paper than women do to the extent that the advice for women in economics, for example, is don't co-author papers with men because they will be given all the credit. So it's basically very, very difficult for a female academic to get ahead. She is absolutely not on a level playing field with her male colleagues. So overall, not just in STEM, but basically everywhere the gender data cap pops up, what can we do?

How can we make sure that this doesn't happen again in the future? And can we backtrack and try and fix any of the problems it's already brought up? Yeah, we absolutely can. And the answer is very simple. It's collect sex disaggregated data and then design whatever it is you're designing. So, for example, the teacher evaluation system that I was talking about that is being rolled out as we speak in Britain is clearly not based, is not an evidence-based policy. So it would be very easy to look at the data that has in fact already been collected on this that shows this is not a good way of evaluating instructors and scrap it and come up with a way to evaluate instructors that is based on evidence. and that goes for everything.

So cars which have historically been designed using a male car crash test dummy, very, very easy to fix that. Just create the female car crash test dummy and start using it in all the tests. And then you will have cars that we can say whether or not they're safe for women as well as men. So none of this is that difficult. It will require efforts and it will require funding. but it's not a difficult thing to do. It just requires will. And that really is what has been lacking.

It wasn't until I read this book that it occurred to me how strange it is that I've never been able to reach the top shelf in any kitchen. So what was it? What were the things that while researching this book surprised you the most? It's really difficult to answer that question because after a while you do become kind of desensitized to it

because all of the examples are so shocking, particularly ones where women are dying. I mean, I think the car crash test dummies is perhaps the most sort of blatantly awful because, you know, how on earth did that happen? You know, where have we, how have we got to a stage where we are thinking it's okay to not test cars on female car crash test dummies?

I should qualify that by saying that, in the EU, for example, they belatedly realized women existed in 2015 and introduced what they call a female car crash test dummy. It's not actually an anthropometrically correct female car crash test dummy that has not yet been designed. So it's really just a scale down male dummy. And it's only used in one out of the five regulatory tests and only in the passenger seat. You know, when you read something like that, it's very hard no matter how desensitized you are, not to be shocked. Because at that point, you've got to the question of this isn't that this isn't even that they forgot you know this is they finally realized that there's a problem and their solution is just unbelievable how how do they

come up with regulations that only included a scale down mail dummy in one regulatory test out of five and only in the passenger seat that's just bonkers um so I guess maybe that one but but more than that I think the thing that really shocked me was, and it's kind of connected to this, is the excuses. Because again, I'm not so angry when it's just about having forgotten that women exist. I mean, it's kind of insulting. But, you know, I know that women do it too. You know, that I know that I do it. So it's a societal conditioning, and you can't really get angry about that.

You can want to change it and try and do things to change it. But that's not anyone doing it deliberately. when it comes to people making excuses, justifying why they're not including women. So saying things like, well, we didn't test on women in the past, and so we can't start now because we wouldn't have comparable data. That's enraging. Similarly, justifying it by saying,

oh, women are too complicated to measure, which is the number one reason, by the way, that I came across when I was researching the book. It popped up in the economy, why we don't collect economic data on women, why we don't collect workplace data on women, why we don't collect travel data on women, why we don't collect medical data on women. I mean, that comes up again and again and again.

Women's bodies are just too complicated to measure, which is clearly a nonsense, you know, because those complicated bodies are the ones that you're going to have to be dealing with in real life. And so you need to research them. There's this sort of staggering lack of curiosity about the female body. you know, as scientists, I would expect scientists to be excited about this whole area that we know so little about, you know, the new frontier in science. And yet, again and again, you get this pushback of saying, oh, women will mess up the test results. So, you know, we can't include them at this stage. That is absolutely enraging because if you are doing research, you must know what.

the result of this is, which is that women ultimately can be dying. One example that I came across was this heart medication that at a certain point in a woman's menstrual cycle would be more likely to trigger a heart attack. That's the kind of thing you need to know before you release medication onto the market. So I would say it's the excuses that I've found the most shocking rather than particularly any one example. Right, I see. And this book has now been out for a few months. What has the reaction to it been?

The reaction's been pretty overwhelming. It's been incredible to watch people really take it to heart and change things that they're doing as a result, you know, change things in their work, things in their home life. I'm getting sent lots of pictures of women queuing in toilets, queuing in toilets, queuing for toilets. And also being sent lots of examples from all sorts of people

about things that they've changed as a result. So one example that a guy who's an Alzheimer's researcher sent me last week, which really made my day as well as really, irritating me, was that he said that he had realized, having read the book, that he kept being sent clinical data sets where white men were literally coded in as the baseline with female and African American, for example, being coded as effects. And he has now, you know, changed that in his data set. So he's recoded it. And he's actually gone ahead and unpicked sort of 200 pages worth of code that he had already done in order to fix

this. So that was incredible, but also, you know, despite everything, despite everything I know, it still can be so shocking to see something as blatant as that as we are literally coding white men as the baseline in clinical data sets. You know, this is something that's happening now. And it really just shows you how this is sort of hardwired. us. I don't believe that the people who put together these clinical data sets are evil misogynists working, you know, in white supremacists. Well, I hope they're not, but I sort of assume that they're not, you know, this is just an unthinking. Yeah, of course, white men are the baseline. That's just normal. And I find, you know, I find that fascinating and horrifying. But also, it's incredibly

gratifying to know that there are people working in the field who are now seeing it and changing it. That was Caroline Creado-Perez, talking about the gender data gap. Invisible Women is out now. If you desire for more science as being woefully underrepresented, why not check out the latest issue of BBC Science Focus magazine, where we go on the hunt for Planet 9. There is of course much, much more inside, but if you just can't wait to get hold of a copy, then check out our previous episodes of the Science Focus podcast. If you like this one, you might enjoy what does it mean to be a man, where we talk to psychologist Gary Barker about toxic masculinity. As always, it would be great if you'd be great if you

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