 All right, so thank you very much to the AITS for giving me the mic for half an hour to Try to educate everybody about institutional bias Yes, so first I was wondering who's coming to this talk Who are you? Are there more men or more women? So with a round of applause the men the men who are you yeah And the women with the round of applause where the women oh wow You guys don't really care so much about institutional bias, huh? I Guess I wouldn't either if I was a man All right, so are you all academics? We're the non-academics. Oh Wow, you're very brave for coming here. Thank you very much for coming So in a math doc usually when I give a math doc the audience assumes that what I'm explaining on the board is correct and and that I'm leading the people towards understanding a theorem and Usually I also encourage people to ask questions whenever they have some here. It's not exactly a math doc and So I'm going to ask to thank you keep your question for the end You can jot it on a piece of paper and then ask it at the end It's very likely or maybe it could happen not very likely that I'm going to make a mistake if you spot a mistake just write it down ask the question at the end and Yeah, anyone can make mistakes. I guess among the mathematicians have any of you already made mistakes? Yes, yes, we make mistakes and then and then once we make a mistake we we correct it because Because this is a this is math you can't hide the mistakes. I'm getting thirsty You can't hide the mistakes and this is what's so great about the About our job right because you can rely on things that you think are true for sure Anyway, so I'm trying to treat the subject as a mathematician So institutional bias I looked up the definition I Choose Oxford reference Some colleagues of mine sometimes they look up my stuff in Wikipedia. It's frowned upon by the French people Yeah, they so I went Oxford references looks looks, you know something you can trust, right? So they say a tendency for the procedure in practice of a particular institutions to operate in ways Which results in certain social groups being advantage or favored and other being disadvantaged or devalued? This need not meet to be the result of any conscious prejudice or discrimination But rather of the majority simply following existing rules or norms Institutional racism and institutional sexism are the most common examples, okay? So, you know if I want to put like some more concrete example Requiring to requiring to have balls in a literal sense. That's going to be sexist discrimination requiring some heights is Going to be requirements of having some height requirements for a given job. That's going that's not necessarily sexist But that will lead to a big chunk of one of the gender being disadvantaged Right and so so that's the definition for today And yes, so I'm going to make an assumption That's today's assumption and and you know some people You know historically Historically we have thought that it wasn't the case. We have thought that the distribution were not the same some people, you know it used to be that we thought that Women were just stupider than men right and then that's that's changed a little bit over time now some people think that We have the same We are stupider and more or less and same average, but we don't have like the same distribution of Stupidity or intelligent, right? So and but but today today. I'm just I'm really going to assume that That actually I have one and only curve Let's say it's relatively well Supported by facts, you know if people have been looking at Parkour soup now or yeah, it's it's it's relatively well documented But you know if you really object to that you can come and talk to me at the end you can discuss So right so this slide shows It's it's comes from the journée parité so journée par été is one day we organize With some people to discuss gender imbalance in math in France And so Laurence Bros came and gave a talk and where she studied she's a she's a statistician and also the the president of Femme Mathématique or some anyway and so here she she showed us this Graphic do you see she would admit so professor twenty six I'm section 25 The friends has a different section for a different pure math is 25. It's going to be my lens today There's fantastic math pure math that's done outside of the section 25 But today my lens is going to be on section 25 And so this is the professors like me and but it's not the whole population of of Mathematicians 25. There's also a master of the conference and Wait, the master of the conference there are more or less no so they're Here The graphic oh my hand is not very steady the graph is more or less like this Right and you can do the math to see where the green Line is going right so so that this is this is like this is the the junior people and this is the The senior people in the French system. I mean that this is like the first position and then the second position so Yes, and that so so of course we are kind of puzzled by this graph and It kind of looks like an equilibrium. It hasn't changed in like 20 years and things happen in 20 years I mean I mean 30 20 years 30 years. No 20 years. Yes. Yeah 20 years. Okay Yeah, and and I have to so of course we're wondering why why is that and We're trying to so so the people the people who thinks some people say like I tell can tell you where because because this is where This is where the PR 25 are end of story It's a way of saying you're not there because you're stupid error, but not It's a polite way of trying but you know we're still anyway So that's that though. That's one explanation, but since today we have this assumption We will leave that explanation. So yeah, and it's also not at all due to bad male behavior in fact, I think that Mathematicians are better behaved than many people in other fields In my in 2018 I wrote a piece on the glass ceiling this phenomena and I started my piece by by relating some incident about a very Famous older eminent colleague of ours who was giving a talk Very fancy talk and then a grad student at the end comes in and she would like a selfie And so the guy except for the selfie and tells the student I will think of you in the shower tonight And so and my journalist friend read my piece and she told me come on in there in my field The older eminent colleague would have groped her and everybody would have laughed And yes, so yes, if you've seen last years Movie picture a scientist You should definitely see that watch this movie and you can see that in fields where they're Very pyramidal structure in big labs and money stuff It's much worse for the women. It's much worse and I Can see some like confused looks Is she telling us that we can group the student? No, this is not what I'm saying. This is not what I'm saying Yes, that no I was saying that too. I think we are treated very very well For most of the time except for maybe say a one percent You know whoever's been Chair for the apartment is like, yeah, definitely one percent of our colleagues are crazy Yeah, we know that I'd say one percent. Maybe if you've been in that you maybe you want to say two percent Yeah, we'll stick to one percent for my for my for my presentation So this is the problem when you have a one percent Creep in a population where you only have six percent women We're basically walking targets for the creep it's not you know, there's nowhere to go and And and and you know, especially if you're the younger one, he's gonna find you right and Right, but you know, I know they tell other people like yeah We should do something against those people and trying to bet is very difficult There's nothing you can do something. It's even done very legally, you know, just going around and insisting that This new women's theorem in fact are not so good But so there's not much you can do it one thing that you could do it is just you know Take it as a fact of life and include it in your model That's much easier than trying to find and you know to to fight and to get some like Ideal situation that you're not gonna reach you know, you need to accept that who's one percent difficulty and you know and a very easy way to to fix that with Then you know, we lose that you know, you kind of thought it doesn't it doesn't become such a problem anymore But yeah, some some kind of herd immunity, I guess Okay When did I start? 40 yeah, okay. You tell me if you're bored Maybe I'll go quicker. Okay. What do I want to say next? Okay. Yes, so today I wanted to discuss hiring in France because I show you the graph where we managed to hire 6% Professor in 25 and the rest are male So I'm going to talk about hiring committee in France and maybe grad effect. I don't think I'll have time for that We'll see maybe I get to the ground thing so So and I'm particularly like qualified to discuss the hiring thing in France because I arrived in 2007 Oh Yes, oh before that No, I won't yeah before that. I'm gonna say that initially I if you if you read my if you read my my abstract It might not you know, I changed my talk because last week. I was at a very inspiring conference That was called data shape and that was studying clouds of points so now after being in this conference everything's become a cloud of points and And so so I changed my talk Initially, I wanted to discuss there's there are good models of this phenomenon I showed before it's called the glass ceiling and Last last last year last 2016 a journey parity Igor Koshchemsky gave a really nice account on this glass ceiling model and there's simulations and anyway It was it's a nice piece of thing. I but you know clouds of points. I Think are so awesome that I see everything like clouds of points. Anyway So why why I can discuss hiring committees because in 2007 I arrived in France and And and hiring committees. I was never invited to a hiring committee I knew that they happened and I knew that they were counting really heavily towards bonuses But I was not invited until 2014 where they put quotas So so I got I got invited to be on those hiring committees and Yeah, when you call you're when you're being called in a job because of quotas It does feel a bit shitty Right and there's no denying that But then you know, you're also grateful that you get to learn how the system works you get to learn how how was that happening and so so So of course, you know, I'm good stud student. I'm good girl. I'm just wondering what am I supposed to do? so the task The task is a It's pretty straightforward. You have a hundred applicants hundred candidates That I can see as cloud of points. It's actually by color cloud of points I'm gonna take another color That doesn't look either feminine or masculine anyway, you have a cloud of points and You examine their files and then you select ten of them Or maybe nine or some number of them that you want to audition and Those when you're auditioned and Then your rank Or maybe you rank anyway, you're basically rank and basically Nowadays the first one gets to job. It's not but you know, right? So But you know According to what do you rank the people I? Went around asking how according to what am I supposed to rank the people I think that I'm supposed to optimize something But I'm not sure what Yeah, I think you're because you know once you hire somebody you feed them into University like an ecosystem and this person is going to prove stuff. Maybe theorems maybe ideas students What else, you know money Carbon many stuff is gonna so so bitch and Right, so so I went around and asked people I'm in this committee I want to do a good job So that the France is not looking at me and saying oh my god Because of the code as we have this really bad person now in those committees, right? So I wanted yeah Right, so I went around asking and they they wanted the scientific excellence is is is a answer I got a lot who has the best theorems Who has the best theorems you need to rank to who has the best potential who's gonna be the best for this particular department? But so you know and as an external committee usually you get asked scientific scientific excellence So scientific what is scientific excellence anyway? What does it mean to be a scientifically excellent and most of the people are well, you'll know when you see it This is terrible. I was there. I didn't see it You know, it's like the the king walking around naked. Anyway, I was there. I didn't see it This is yeah, I didn't really see it and this is this is where cognitive distance guns kind of kicks in because so so To me so to me candidates are a cloud of points in our end And so you want to put an order our end Is an abelian group and if you have an order relation on your abelian group? It means that For all ab in our end either a is less than or equal to b or b is less than or equal to a So that's a total order relation on your group And you will you also want that for any c in our end a plus c is less than if if a is less than or equal to b then a plus c is less than or equal to b plus c right although Yeah, well did you yeah But that's you know, they're not really candidates. So so those numbers are really numbers which you represent the CV of a candidate. Yes And this this is this is this is where you know, there's no best order There's no like one order than necessary. That's better than the other. What's going to constitute an order. That's good and And then when you have people that disagree on a candidate When are you going to decide that those candidates are in fact equivalent? so Yeah, I guess you can to apologize this space of order you get a compact house dwarf metric space and Have an R in action And you get an RN invariant probability measure in it So you could decide that two candidates are equivalent if the measure of the order that decides them to be either Great or equal or you know comparable Most of my colleagues and mathematicians never even thought about the concept of order on the end which I thought you know That's yeah, I Anyway, so scientific excellence Scientific excellence. So in fact, you don't really know only the CV, you know, you know, you know a little more You know a little more in fact It's not it's not it's not a cloud of points. It's a moving cloud of points that depends on time Because and it doesn't it doesn't yeah, right that depends on time, but choose be told it depends on position, right? You have your candidate that's moving on some graph and and as he moves on the graph he is He's he's X I X I 1 X I 2 etc change depending on where he is on the graph here see it's written. There is an animation I'll show you an animation what I mean Right This is what I mean Great. So this is your particles moving on the graph You go somewhere your stocks go up you prove a theorem your stocks go up You You get a grant your crop your stocks go up you need a creep your stocks go down You apply for a job that you don't get your stocks go down right You spot the proof spot a mistake in your proof. Well, that'll debatable, you know You stop might get up or down defending on on on You know on what you do with the with the with the proof. So anyway You're here you walk on your graph and you know your graph can can have something that's called deep pockets Yeah, you have your base point here I mean I call it E because my graph is all Cayley graphs or groups But anyway, you can have deep pockets, you know, you go your particle you go around and you get you get stuck Somewhere where it's very difficult to get out It's a graph that's mean me. There's one edge and then a big chunk here is very difficult to get out If you have a few creeps your stock keep getting down You can't get out But anyway, that can happen or you can hit some like fantastic place That's going to makes your theorem Fantastic in your stock skyrocket So anyway, how do we how do we? How do we? How do we analyze all this when we're in this hiring committee and we look at like this data and We're not allowed to like input all this data into some computer algorithm because we've been told it's bad that we cannot do we have to like rely on our our Expertise and knowledge of the field to decide what is scientific excellent So Really you're faced with the problem that you have this data you have this moving kind of points You have the you have what you what you know is the past trajectory starting from The t is equal to zero is the time of PhD and then this is the time of Application and you have another one. That's some other cloud of moving points here and you want to compare them But really you're not really comparing them what you're doing if you're hiring somebody you're going to spend the rest of your life with You're not going to just look at the CV or doing some projection do I really want to spend the rest of my life with this person? Right. So so so you're doing some projection But based on what? Right based on based on things, you know, so you're going to try and project What's going to happen here in the next ten years because that's going to be your new colleague and you're going to project based on Maybe other trajectories that you've seen and that we resemble So this is you know start resembling some person. You really don't like You can project it really low, but you know your neighbor doesn't have the same projection So it's going to find another projection for this candidate and Yeah, so my cloud of points X I and Y I they're bi-colored and If one color is missing you'll have no way to project Because you don't know you have the resembles make nothing you end up with a file. You don't know idea how to judge it So you tend to not projected forget about it Move on to something you can't project. I mean it's human You're choosing somebody you're going to live the rest of your life with We're at least they let the nest ten years and So yeah, and so to do this projection. We rely on what our colleagues say letter of records and recommendations some very unevenly distributed markers like like hiring committees and We're editorial boards and Our own assessment of the CV that has been shown by some Randomized double-blind study, which is the golden standard for For for those kind of study has been shown by randomized double-blind study that our own assessment favors men Even if we're women we're men we favor men We like men Yeah, I like men So Right, what am I saying now? Fine. All right So I'm still here in my problem of Projection by now we have those those those those five candidates I Guess I guess they're here somewhere here and we're trying to compute their projection and And and and there was also no way to check and see that what we've done is Actually correct because if the people we don't hire they don't have a job. They basically disappear from the system There's no way of checking but running a bad AI algorithm where you're trying to pick a dog from a wolf But then you only feed the algorithm dogs that are sticking on their humans laps So you're never going to really learn what is a dog and what is a wolf you're going to recognize the dogs because they're humans They're nearby and and we don't we can't check if what we're doing is sensible or not because that's it you know we we've no way of checking and Yet we're still doing it With no way of checking that we do the right thing. We're still doing it and We iterate the procedure. That's that's the best thing about the French system. We iterate the procedure twice and And so and and and we get the following numbers So this is that so this is MCF is the first position that people the first Used to be that they would get this position as PhD plus zero one now. There's inflation. So they get their PhD at much later And then and then once they get this PhD at much later stage. So this again, I took it from Laurence Brose Slides you should you should come back to the journée parité 5th of July in just years going to be a lot of fun Laurence is going to present The continuum equilibrium. That's probably you know, she's going to present the the COVID-19 Disasters where whatever the COVID-19 Numbers on those graphs anyway, so that so this is all this is all from 2000 2019 I think 2019 yes, so So right so when I got to when I when I when I arrived in France, I was here so there was 31 without me would be 30 and And I you know Yeah, it's pretty flatter to be in this select little pool of people and I didn't I guess I didn't have much sympathy for I didn't really look at the numbers, but if you look at the numbers you can see that 668 is roughly one-and-a-half time this right One-and-a-half so if you if you it kind of tells you that over time roughly two-third of the rank B men become rank A and 55 and 31 What's the ratio anyone we can tell the ratio of? Time five or divided by five, so it's much less likely if you're a woman that you're going to become Right a so so people offer all sorts of explanation Women are stupider again Now there are other also other explanation like they like babies a little too much They don't You know anyway, I've yeah So so and and when it's cloud of points you can say yeah Maybe you don't we started with the same distribution, but then but then you know it could be that When when we when we did this first so here here before here You're you're basically equal when you start the universe the poor parkour soup is basically even and then something happens something happens that that's 19% women only get here, so we do some selection We could be selecting only the stupider women to get here. This is why they never become Professor, but I don't think this is what we're doing. It's very unlikely that this is what we're doing, right? But you know, there's no real proof. There's no telling what you're doing Because since you know the the distribution here at this stage can be anything Right, but if you move yes No, I said that you had a question you had to wait for the end of the talk. Can you do that? Yeah, you can do that. I can tell I know it's difficult. I know it's difficult. All right I'll I'm promise I'm gonna get back to you and your question at the end of the talk I'll even keep the slide to come back. All right, so once you move away from the cloud of points You see that those hundred and fifty five points are actually people they're women that I managed to meet in those years and I started working with them and And and seeing how they're very competent Yeah, I think there are a lot of them that I met they're actually much more confident than me And and yeah, so so and and and and some of them waste so much time and energy in applying and getting rejected That's you got to wonder If You got to wonder is it really efficient as a system? I mean, I'm not saying men that happen to men too I know some very competent men that keep applying and never get their promotion That's yeah, I know a lot of them too but We shouldn't treat that as a some zero game women are Are usually statistically a vulnerable population Because of what I told you before just out of the sheer minority, but not only we have all sorts of statistical disadvantages So the fact that We can see those women struggling and not Succeeding in the way they should We could just treat it as the cannery in the coal mine Remember the coal miners when they're going down they take a cannery It's a little burl that that sinks and that's very Vulnerable to gas emission and it's gonna die before the rest of the coal miners are even Sick because of the gas so they have time to leave and I think that this numbers Could be should be treated as a symptom of a problem in the way we In the way we select and hire people so this is Alex and Alex is a storm that killed 10 people in the 30th of September in 2020 and destroyed 400 buildings and It's basically due to global warming And I think this really freaks me out global warming is absolutely scary and But we spend This We spend 100 times more money for military than we do for clean energy and research and development Basically, we're not treating it as a as a priority and Of course, you know, you could wonder what you can do as a scientist The scientists you can I don't know what to do. I can keep studying The I can keep talking about it and studying the models And going back to the model when what happens when you have encounters Suppose you have a popular population that's going to have some encounters and that's two two basic strategy either you cooperate And so if two cooperative people Meet they share one half of the dough so they get one half So if you a cooperative person meets a jerk the jerk gets everything and the cooperative person gets zero The jerk gets everything two jerks meet. They're gonna fight get some damage and I get some damage and And so you lose something basically this this is the loss So you can wondering you can wonder what's the best? What's the best strategy? Is it what's going to happen? You can see that if you only have cooperative people and you put a jerk in it It's gonna have a huge advantage because everybody's giving him food So you can reproduce and then take more importance if you have only jerks The cooperative guys and getting nothing but at least it does not getting hurt So it still have an advantage on the other and you can compute that you can compute the equilibrium Which I'm not gonna do right now But it's a l1 exercise and you can also see that if you put more penalty on on fighting Then you will have a population that took more cooperative But the question is what what happens for the full system? The full system is much better off if everybody cooperates as a unit you're much better off is if everybody cooperates so So right well, that's yes right and My point is that the a and b division that we have in France is not fostering cooperation at all It's typically something that will not foster cooperation. So if we want to do something for our planet burning and and Going to hell basically because of a system that has been favoring men so So so badly what we should do is is we should We should spend all our energy into thinking about this problem of Of clean energy and what we can do, but we should go at it With it with a good strategy with a 50% women's strategy not with a lame-ass 6% strategy where you basically miss out on half of your brains, and you don't even include them in in those important research and That's all I wanted to say. Thank you And I'm back to your question You want the numbers? Yes, again. Yes, I was wondering if this bias Also exists in the other disciplines. I mean the very precise one that you mentioned here passing the Passing from the associate professor rank. Yeah, professor. So I so you can yeah, you can yeah, it's it's more or less everywhere It's more or less everywhere. Yeah, I mean chemistry for instance. Yeah. Yeah, it's everywhere Yeah, I didn't look the exact numbers, but it's basically everywhere and what's very interesting is that if you look at very feminized Fields If you look closer It's even thicker than in math because you know you can you can look at the thickness of your ceiling just by comparing the ratio of the 19% and the 6% But you know if you have if you had instead of 19% if we have say 99% and Then as professor we had some you know 80% even 60% you could tell that there would be the glass ceilings pretty thick too. Yeah Sorry for making you wait until the end of the talk So Thank you for your talk and I guess now it's question time So if anyone else has another question or I know there's online questions as well So does anyone have a question in the room? Do you have I would be interested if you know how many women out of 155 Passing habitation dirigees des recherches are truly candidates. I mean because it could be also another yes in the sense that we are not Feeling that they Want to do this or Yeah, so yeah, I don't have the I don't have the exact numbers. They're really they're difficult to find Yeah, I at some point I thought I wanted the exact numbers and then I decided I was not so interested anymore in the Exact numbers because once I you know you start knowing the people and you're like, oh my god You would be so good as a professor. I don't know why you're not and And so then then you know, then it kind of goes over the numbers Somehow, yeah, but you know, it's a good question to my god. I mean Laurence blows probably We had this question and then you know, then you're like Maybe they don't get the ability they don't do the habitation because they don't want to bother because they know that he's not gonna Helped he's not gonna it's going to do anything for you and You know, I don't think we treat the runway very well And and so so by not treating them very well Especially when they you know become older It's really not like fostering collaborations as it should Like for instance in my case I got here and I was like kind of lonely when I was in the US I had so many girlfriends. It was awesome and then I get here and I think yeah There's some women But they're not anywhere near me and then a lot of them are on base So I can't go around and whine about my job to them Right So there's no that yeah, I can't whine about my job to anybody. That was yeah, that was kind of lonely Yeah, I don't know did I answer your question? Yeah, well, I'm sure yeah Yes No, no, it's definitely not just hiring committee what happened in hiring committee is pretty interesting because Before the quotas it was it was counting a lot towards bonuses if you weren't hiring committee You know and you do those prim I was in there So I could see like if you have your hiring committee your stocks go up a lot And now because of the quotas hiring committee don't pay anything for your stocks anymore So it's basically volunteer work for us French women But you know, it's very interesting volunteer work nothing wrong with volunteer work I think volunteer work is is yeah, but it yeah, it's we yeah But it's it's something it's it's a Commonly studied thing then when women start to exceed to something it devalues the thing we start getting It's a low lose situation. That's okay. That's okay. It doesn't you know The job is still great. I'm still very happy to to do mathematics. It's it's a great job Yeah, so I don't know I so yeah, I don't have like a silver bullet or anything But you know we could there there's these there's many things we could do for instance one thing that we could do and I think and I would be free What we could do is take all those women the 155 women and put them here That would be horrifying You know, I think it would I think that'd be a fine job. They'll be doing a great job. I think and It wouldn't cost us anything because the way things go you just don't adjust the passage You wouldn't be basically free, but if you do that. Oh my god the men In the rank B. There's no we can do that to the men. Oh, yeah, that would be yeah I don't know but it's an interesting. I think we should try this idea. It's free. It's cheap. It doesn't I don't know It's the same as the CNRS or not? Yeah, I looked at the CNRS. I don't remember the CNRS. Yeah, yeah, I don't remember the CNRS Yeah, I think that Yeah, the numbers of yeah, yeah Yeah, the numbers are lower. So This is the thing is like it takes so much time to look up this numbers and And again, it's basically volunteer work So, so yeah, it takes a lot of time and Lawrence Bros does it really well once every four years So I think I am very grateful to Lawrence for doing this work And I hope that you will come on journée parité 5th of July just you To yes, yes The comparisons with what happens in all the countries No, I haven't made any no because I'm actually totally not a statistician. I'm a geometric group theorist. So so so I I I kind of do this thing when I suddenly I think you know this this this obvious thing and nobody sees it And so yeah, I've I've only compared with my own experience. So I was a professor in the US and Yeah The system is a bit different. I think that basically is the same kind of idea. I don't know Yeah, I Yes, yes Thanks, you thank you for the talk. I just had a question a lot of the reasons why there aren't as many women in STEM fields In research are pretty inherent to society I mean, there's not there's no one that comes up to and says that you're not smart enough because many of us are It's just the way we're raised in the way we see things as a society What do you think we can do to I mean get changed in this field because I do not see well, you know Yeah, I mean there's here's an idea you could put a lot of money to hire women mathematician And once women Mathematicians see that there is a lot of money a lot of jobs to be made there. They'll come But as long as they see that if they get a job then never go off to the other all the way to the top They're never gonna come. I mean most of us we study mathematics because we're kind of smarter than everybody else around us, right? It's a little bit what happens, right? So so we don't get into mathematics thinking yeah, that's great I'm going to be like Remain this average Not very well-paid mathematician Most of us when we're young we're just we're so we were smart. We want to go to the top. We want to conquer the world Why would you go to a job where you know you're not going to? To to go the way. Yeah, I know. Yeah, so Yeah Yeah, yeah, I think that if you hired massively a bunch of women to do math But you will find a big amount of women to do math without be fair And also would that be fair for the men in the sense where I Believe that my male colleagues are just as deserving it's just that I also think that my female colleagues are just as deserving I Don't know why Well, so, you know fairness is very relative. I mean We life is you know world is not fair and we we benefit a lot from the world not being fair You know, we are here We eat as much as we want We can walk around without getting shot or mugged So yeah I don't know this shows that We've been pretty unfair to women and we you know the world goes on and World is unfair to some people and it's not a big disaster Yeah, it would be maybe unfair to some men, but maybe They'll benefit, you know me what happens if you put those hundred and fifty five women in charge of being professor What will happen? You know, maybe they'll just Do something to be fair to the men You know, is it very hard to imagine that we if we were in charge we would be fair to the men too Why is it so hard to imagine? Yes, yes, but maybe it was good that hiring committee loses value We it was good because it was not so and by the way What would happen when you put women in charge? Well, that was nothing nothing I can go and see here, right? So this is when we were in charge Yeah, no, what did I say 2014? That's what I said. Yeah, we get human girls So I have a comment from Alicia Dickens Stein online So she's saying about your table the table you you we discussed It would be interesting to discrige it information according to age and Generation because this would also mean that more young women are hired lately So she's not saying that this is the case But it would be interesting to know the year of hiring and the age of the candidate at the time. Yes. Yes Yes, I thought yeah, I need indeed think I it would be very interesting to to do more work on that Yeah, yeah, I think so too all of those tables. I are online in the scene. Yes. Yes Checked by age generation. They have all the numbers. Oh really? Yeah, okay Yeah, I have to say that each time I do some job like this write an opinion piece and I have to do this research It's really unpleasant. I mean doing killer grass and walks and killing so much more fun Yes, yes Okay, so Somehow I would like to ask what is specific to math and for example in medicine Maybe 30 years ago You had essentially no women and now it's more balanced. Yeah, so I mean, I don't know what it means for math if we should be this is great because we are Unable to do anything or if it's an optimistic Point of view saying that things can change in our discipline. So why we cannot do it. So I don't know Yeah, I don't I don't know. It's yeah, I think it's an interesting question. Why is it that some fields managed to to Get almost to equality and they treat their women really badly the doctors. I think they're so bad We've seen we've heard also the horror stories. Yeah, yeah, yeah, no, I yeah, I don't know how they managed that Yes But in medicine you have 70% of studies and women's In medical profession, but in university you have only 10% of woman professor in medicine So the last sitting is to hear and very Yeah, and so and those those those those curves those flat curves by Lawrence was don't say anything because they've been Fluctuation of total population of mathematician. We are shrinking the section 25 the pure math is shrinking That's what I'm saying. We're the cannery in the coal mine. Listen to that We're shrinking pure math is shrinking and it's it's it's kind of terrifying because it's it's shrinking as We refuse to see that our earth is warming We don't want to do the the intellectual work of I don't know trying to understand trying to save herself from Burning I guess. Yeah Thank you Well, just as an introduction, I'm a physicist and And I work in biology also and there are lots of statistics I mean the CNRS has done statistics there was a program called integer and There are many statistics in all different fields and the more it's really striking that this ratio a to b The more women they are in biology is very striking. There are more women and the ratio is worse So the question is not the number in fact math has a very low number But the ratio is better. Maybe Even though it's not very good and in physics and sort of in between But the problem is no here the question is why not more women start to do to go into the field and there's a lot of work I'm into a lot of groups like women and science women in physics and women optics because I work in optic and The question is those people and it's a lot of women scientists going to the schools I go I spent weekends in schools to talk to both boys and girls to tell them that they should do science and There are a lot of women who do that and that's well that's to try to increase the number of of both boys and girls, you know to address these problems we need more people to do science and you have to go and do this work and Frankly, there are more women who do this work talk to the young people going to schools even great schools any kind of school high schools and You know, there are lots of groups of women who do that and please, you know If if everybody was into science math physics or other fields would do that spend, you know Go on Saturday mornings go for an hour of driving to schools that don't have not like around here where you have Already scientists around you, but good other other parts of France or of around Paris Well, there are not so many scientists around them go and talk to them and and then you'll have more people doing science and more Girls because when they're younger, they're not as biased. They don't know yet. That's gonna be hard They don't care yet I mean, this is something that you care about after a certain age But when you're young you just want to do this because it's fun because people tell you that it is fun And so I think we should work on this because if there are more people they will also be more girls if you start You know and go do this work So that's just Thanks. Yeah. Yeah, no, it's a very good comment I think I think there are initiatives to go and bring the math to the to the to the school girls Is are usually very successful? But it's kind of yeah, I did it once like I probably won't do it I mean, I'm stretched thin to what I can do in terms of work I mean, I'm barely surviving my inbox is flooded by I don't know why I'm any males because I prepared to stock I mean, I'm surviving. That's all I'm doing. So I cannot Imagine adding something like this. It's like beyond my imagination. Plus. I would think it's all I I mean How can I go and they come and study math? You won't graduate to rank a but yeah come Yeah, no, yeah, so so but there are some like fantastic women that are doing that and in fact a journée parité where I invite you to come We'll have the presentation of Those those initiatives. Yes, and and we hope that that You know people give money to this initiative so that women can actually Make a living out of doing that or you know get something gets get something out of it If you pay something that usually yours if you get money for something your stocks go high Go up and then yeah Yeah, but I'm sorry, but I wasn't seeing how many women should do that. My point was men should do it more I mean, I'm not saying you should do it I'm saying, you know people in this room who are men and science should do it as well because there are too many women I agree I have it takes me a lot of my time and I also have a lot of work and Frankly, I would like that this work is shared between both men and women and I You think you think yeah, I don't know. I was wondering yeah I was wondering is it efficient if a guy goes into some room and say your girls you should come and study math Will treat you well If I can add to that actually like and my lab we're organizing this Like two days for high schoolers to come and I mean they're all girls and they're coming except then in my lab But there's many of us Well, they had to find two new girls every year to do it and every year it's a struggle to find two new girls Even though we're 500 people lab To grow PhD students. So I mean this year it is me, but next year. I don't know who I will find to do it And you know the thing is it it really feels like a woman problem And I think that can be disheartening Because if it's a common problem if we agree to that then why should I be the only one planning? I mean sure I should probably do the talking like to the students so that they see that there are figures that are relevant But surely to order stuff you can do it and like to like plan stuff You can help me and we can all do it together and then it it makes like more of a team effort And it like gives the feeling that it is a common problem and not just a Go girls we're gonna like have more women in the lab because we're women and we want more friends So I think it makes it in I think it would be good for more men to get involved in those things without like necessarily like stealing the spotlight of like the presentations, but We like if we consider this to be a real problem then everybody should like pitch in I think And I will take this opportunity because we need to move on a little bit to you again