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Keynote: Programming Diversity - Ashe Dryden40:24 with Ashe Dryden
It's been scientifically proven that more diverse communities and workplaces create better products and the solutions to difficult problems are more complete and diverse themselves. Companies are struggling to find adequate talent. So why do we see so few women, people of color, and LGBTQ people at our events and on the about pages of our websites? Even more curiously, why do 60% of women leave the tech industry within 10 years? Why are fewer women choosing to pursue computer science and related degrees than ever before? Why have stories of active discouragement, dismissal, harassment, or worse become regular news?
In this talk we’ll examine the causes behind the lack of diversity in our communities, events, and workplaces. We’ll discuss what we can do as community members, event organizers, and co-workers to not only combat this problem, but to encourage positive change by contributing to an atmosphere of inclusivity.
- Educate about the lack of diversity and why it is a problem
- Examine what is contributing to both the pipeline issue as well as attrition
- Isolate what is and isn't working
- Inspire direct action by examining our own behavior and learning more about the people around us so we can empathize better
[MUSIC] 0:00 Hello, how's everybody doing? So I my name is Ashe Dryden. 0:04 I'm especially excited to be here. I actually haven't told Berman this story. 0:10 A friend of mine sent me the Blend Conference website and said 0:15 look at This conference, look at how amazing the diversity of speakers is. 0:18 And literally the same day, Berman emailed me 0:23 to ask to speak, so it's perfect, absolutely pefrect. 0:26 So, 0:27 so my name is Ash Striden, I'm a 0:28 programmer, and before we get started, I'm a Ruby 0:30 programmer, and we have something special in the Ruby 0:33 community that I've been trying to share with everyone. 0:35 Today is Friday. 0:39 Friday is a very special to a lot of 0:40 people because it signals the start of the weekend. 0:42 And we do this thing called the Friday Hug. 0:46 Now I'm hoping that, I'm hoping that you would all participate with me. 0:49 But basically 0:53 it's you've reached the end of a really long week, a 0:53 lot of us work alone, and so why not hug the Internet? 0:56 And so if you wouldn't mind standing up. 0:59 [BLANK_AUDIO] 1:01 And it's very easy. It's the easiest dance you'll ever do. 1:07 And basically it's just you extend your arms to hug the Internet just like that. 1:10 And I'm gonna do a quick video of you. >> [LAUGH] 1:14 >> Alright? It's fun, it's fun. 1:19 Ready? 1:20 [LAUGH] Awesome. Thank you. 1:24 [SOUND] Who is afraid of audience participation? 1:31 Right? Everybody does it. 1:40 Yes, me. [LAUGH] Alright. 1:42 So, like I said, my name is Ashe Dryden, the E is silent. 1:46 I'm pretty much Ashe Dryden everywhere on the internet. 1:50 I'm a Ruby programmer. 1:52 That's my current favorite community. But I, I've 1:54 been, in the programming community and in tech for about 12 years. 1:57 I really love what I do. 2:00 But I've noticed my entire career the lack of diversity in tech. 2:02 It used to be I was the only woman in the room and it's slowly grown from there. 2:07 Being here is actually kind of amazing because I, 2:11 the first day I actually sat at a table with 2:14 just one other woman, and then looked up after a 2:17 little while, and realized that the entire table, with exception 2:19 of one was all women so, so that's awesome. 2:22 And something I get really excited about. 2:25 [LAUGH] Exactly. 2:27 So what is diversity? 2:30 A lot of times when we talk about 2:31 diversity in tech we talk about specifically gender. 2:32 We talk about women. 2:35 Where are the women in tech? We don't see them. 2:37 Where are they? 2:38 And the things that I talk about 2:39 specifically are about all different kinds of diversity. 2:42 Diversity to me is more than gender. 2:45 It's more than just woman and men. 2:47 There's an entire spectrum of gender that a lot 2:49 of us don't either don't know about or don't recognize. 2:51 So it's important to me that we recognize that. 2:55 As well, diversity covers various backgrounds, experiences, and lifestyles. 2:57 And it's not always visible, there are a lot of things about us that 3:01 make us different and unique that people 3:04 don't necessarily, can see from the outside. 3:06 Things like a disability, physical 3:10 and mental health, our economic class, especially the class 3:13 that we grew up in, and our education background. 3:16 So there's all different kinds of diversity, and 3:19 these are the largest ones: sexuality, age, ability, race. 3:22 And so when we're looking at diversity and talking about diversity, just 3:27 so we have the same context, this is what I'm talking about. 3:30 So, just so everybody's on the same page, I have a few vocabulary terms. 3:34 Intersectionality is the study of all of these different traits. 3:38 Whether we're born into them or we acquire them through life. 3:42 Kind of intersecting and changing the way that we go through life. 3:46 So if we look at those bubbles that we had 3:50 before, intersectionality is the pancaking of all of these things. 3:53 So what does that mean? 3:57 If we look at the United States, in the US, women earn 80.9% of what men do. 3:58 Now this sounds, this is actually better and worse. 4:04 This is much better than we have been doing in the last 30 4:07 years, but this is actually down by about 5% the last five years. 4:10 And this is for controlling for all other factors, 4:14 including education, time in and out of the industry. 4:17 So now let's look at the intersectionality point. 4:20 If you add in just one factor, Latina women make 59.3% of 4:23 what the white men do. And that's damning to me. 4:30 That is so scary to me. 4:33 I'm very lucky to have been born a white person that can make a lot closer to what 4:34 men make but this i s absolutely horrible to 4:40 me, and it's something that I'm trying to fix. 4:43 So just this one trait, just one additional trait of 4:45 race, dramatically changes the amount that you can earn in life. 4:48 Another one, the unemployment rate 4:53 in the US, depending on who you choose to trust, is 7.5%. 4:55 But for the blind, it's 70 to 75%. 5:01 That's scary. 5:04 I mean the vast majority of people 5:05 go through their lives then not being employed. 5:07 Or, or very poorly employed, they're being paid very little. 5:10 So the next term is privilege. 5:15 And if you've heard me speak before you know 5:16 that this is something I bring up a lot. 5:18 Privilege is basically an unearned advantage we 5:20 get for the person that we are. 5:22 So if we take a look at my privilege, I'm a white person. 5:25 That is the most privilege that you can get in the United States. 5:28 There's a lot of stuff that I get with that. 5:31 I'm also perceived to be straight so that gives me a lot of 5:34 leeway when I go into different 5:38 communities and different areas of the country. 5:39 And it also means that those two things The community 5:42 regards me as normal or default. 5:45 So when we're talking about things like, if 5:48 you've ever had to sign up for, a dinner 5:50 at work, or they are having a Christmas 5:54 party and they ask you your husband or wife. 5:56 There are a lot of people who can't get married in the United States. 5:59 Might not necessarily have a husband or wife, does that mean I have 6:02 a domestic partner, or some who I can't marry that I can't bring them? 6:05 It makes it just a little bit uncomfortable. 6:08 So what kinds of things do we get for having privilege? 6:11 We have access to things like, better education, we got better education. 6:14 We have access to technology at an earlier age. 6:20 We tend to be paid much better. 6:22 We have assumed competency. 6:25 This is something that's extremely important in tech. 6:27 There are a lot of times when I go to conferences when people will try 6:30 and pry out of me exactly how much I know to see if I measure 6:33 up to the bar of competent that they have. 6:37 The quality of social and professional networks that we 6:40 have; this is something that's really important growing up. 6:42 The opportunities that you have leaving college or leaving high school. 6:45 The opportunities you have for internships or jobs. 6:49 Your parents may have helped you along your career path. 6:52 Seen as a skill-set instead of a trait. 6:56 This is the difference between being a geek, and being a girl-geek. 6:58 Yeah. 7:04 You can easily fit into and identify with the subculture. 7:06 So as geeks, we have this stereotype where we are 7:10 white men, we live in the basement of our mom's house. 7:13 We drink Mountain Dew and we eat Doritos. 7:17 Now we know that a lot of those things aren't 7:19 true, but that's something that scares a lot of people away. 7:20 We also have this stereotype where we're awkward, we're shy. 7:24 We are very introverted. 7:29 And a lot of people are kind of scared away by that. 7:31 A lot of people don't feel that they can identify 7:33 with people that are gamers, or that play board games. 7:35 And that's something that just makes people 7:39 feel different enough that they feel like, 7:40 you know, maybe that's Something that if I did, I wouldn't really enjoy myself. 7:42 Would I really have any people around me that 7:46 I would be able to have, be friends with? 7:49 That I would be able to do things with outside of work? 7:51 So the next one is stereotype threat. 7:55 I'm sure most people have heard the term stereotype before. 7:57 But stereotype threat is a the fear of being 8:00 able to confirm a stereotype about their social group. 8:04 So how many people know XKCD? Pretty good amount. 8:07 So this is a web comic. And this is one of the more shared ones. 8:12 This, it's one of my favorites. So on one side you have two men and 8:16 a board, and the one man says to the other man, wow, you suck at math. 8:20 And on the other, it's a woman and a man, and 8:24 the man says to the woman, wow, girls suck at math. 8:26 Cuz you're confirming the stereotype. 8:29 Women are bad at math and therefore, you know, I, I now 8:31 have this woman who's proved to me That woman are bad at math. 8:34 I now have this anecdata, which is what 8:37 something a lot of people like to say, anecdata. 8:40 And it's, you know it's funny because studies have shown 8:41 that prompting a group with a race and gender and saying, do you know that, that 8:45 women or black people don't do as well on this test, as white people or men do? 8:50 Going into that test, they do worse. 8:56 If you, if you prompt them and say did you know that, that women 8:59 or people of color do just as well as their male or white counterparts. 9:02 They actually do better than white people and men. 9:06 It's kind of amazing. 9:09 So it's something that we have internalized 9:10 inside of ourselves, that we're so worried. 9:12 So worried about confirming the stereotype that 9:15 it actually kind of gives us stage fright. 9:17 Impostor syndrome. 9:21 This is something that actually affects, all people, not necessarily just people 9:22 that are in marginalized groups, but 9:26 it's being unable to internalize your accomplishments. 9:29 So you've probably worked with somebody before that said I'm stupid, 9:33 I can't get this. I'm never going to be able to get it. 9:35 I'm so far behind. 9:38 I, I mean you know I'm not doing as well as I would like to be doing. 9:40 Meanwhile they're an amazing designer and you've, 9:44 you know, you haven't worked with a better 9:46 designer before but they just can't seem to 9:48 take that in and believe that for themselves. 9:51 And while this does effect everyone it's especially pronounced for, for 9:54 groups of people who have negative stereotype that exist about them. 9:58 So if we go back to the two people 10:01 at the whiteboard, if somebody says women are bad at 10:02 math, I'm much more likely to believe as a 10:06 female mathematician that I'm going to be bad at math. 10:08 And kind of use that as an excuse for being bad at math. 10:11 Even know it's not scientifically true. 10:14 We're also less likely to apply for certain jobs. 10:16 Especially in groups where competency has to be proven. 10:20 Less likely to submit a talk to conferences. 10:23 Less likely to even attend conferences. I love going to conferences. 10:26 It's something that I, I look forward to year round. 10:30 I love meeting new people and talking to new 10:33 people and learning new things and I can't imagine 10:36 not feeling like I could attend a conference because 10:40 I'm not good enough or I'm not smart enough. 10:42 So the last term is marginalized. 10:44 And marginalized, if you think of the old college 10:48 ruled notebook paper we used to have in school. 10:51 That pink line? 10:53 This is basically if you push certain groups of people past that pink line. 10:55 We ignore their wishes or their needs, and 10:59 just go with whatever is between the larger line. 11:03 And society teaches us to do this to everyone within marginalized groups. 11:07 And I'm sure that you're probably sitting there thinking, well, 11:10 I'm different because I'm logical and I'm rational, I don't see 11:13 race, I don't see gender, I'm different than other people. 11:16 Which is interesting because a lot of us, a lot of us 11:20 believe that, but scientists and STEM professors do this to each other. 11:23 The people that we regard to be the most logical and the 11:26 most rational are, rational in our societies do this to each other. 11:29 There's this famous study that was done at Yale. 11:34 And they gave two groups of professors resumes. 11:37 Two different 11:40 resumes actually let me take that back. 11:41 The resumes were the same, except for the first name. 11:44 All of the qualifications were the same, the education background 11:48 was the same, the only difference was the first name. 11:50 One resume had the name John, and the other one had the name Jennifer. 11:53 Only difference. 11:56 They said on a scale of 1 to 7, who would you rather hire? 11:58 What would you pay each one of these people, and give 12:03 us some words to describe This person were you to hire them. 12:06 The groups came back and remember the resumes are 12:11 exactly the same with the exception of the first name. 12:14 John on the scale of one to seven got a four and Jenifer got a 3.3. 12:17 They 12:21 asked how much would you pay each one of these people. 12:23 Jenifer got paid 87% of what John got paid. 12:26 These groups of people, these professors at Yale University, were mixed gender. 12:31 So, there people from all genders and they still rated Jennifer worse. 12:38 Exact same resume. 12:43 When asked to describe the difference 12:45 between John and Jennifer, John was driven. 12:47 He was a strong team leader. 12:50 He was someone you could believe in and trust. 12:51 Jennifer was a bitch. 12:54 She was out for herself. Exact same resume. 12:55 And like I said, even women were rating these resumes this way. 13:01 So all of us have these bias in us, we just don't realize. 13:06 And it's really hard to look past them. 13:09 So, taking a step back, how diverse is the tech industry? 13:14 So if we look at tech compared to the United States 13:19 population, you have a graph that looks kind of like this. 13:21 Now if you're in the back and you can't 13:25 see, there's a top bar and the, on the top it's blue and then the lower is black. 13:26 Blue is the tech industry and the black is the US population. 13:31 And you can see some of these are pretty close to parody. 13:34 Men are doing better than women by quite a bit. 13:38 Women are about half of what they should be 13:41 but more curiously this racial divide is really scary. 13:44 The hispanic population which makes up, I wanna say, 13:48 13.5% of the US population. 13:51 The Hispanic population in Tech is under 4%. 13:54 For 13:57 the black community it's about 11% of the US population. 13:59 And 3%, 3%, 4% of Tech. So it's a huge difference. 14:04 So, let's kind of drill down. 14:11 And I'm going to preemptively apologize, a lot of these examples are using 14:13 women specifically just because that's where a 14:17 lot of the research dollars come from. 14:19 So, women make up 24 percent 14:22 of the industry but we're only 3 percent open 14:24 source, that includes [UNKNOWN] percent of open source contributors. 14:27 Which is interesting if you think about how many 14:32 companies are requiring that programmers have opensource contributions now. 14:35 Woman also make up only 17% of CS 14:40 graduates and that number is going down dramatically. 14:43 So if you're asking that a woman have both a CS degree and an opensource 14:46 contribution, that person doesn't exist. 14:49 That's why your not having, having any women apply for your jobs. 14:52 So it's just not us. 14:57 Although we do have a lot of issues here, the 14:59 diversity problem is global, and it seems to be growing unfortunately. 15:02 So let's kinda take a look at what the rest of the world is doing. 15:07 India has about 8% of CS grads as women. 15:12 The US has 17, as I said. The UK has 18.2. 15:15 France is 20, Brazil is 20, South Africa is 25. 15:19 And you can see these are kind of trending toward the same thing. 15:22 And now you might be thinking well, 15:25 maybe women just aren't interested in programming then. 15:27 I mean that's possible right? 15:29 Like maybe, maybe they are not going for 15:30 CS degrees because they just don't want them. 15:32 Which is interesting if you think about the fact 15:35 a woman wrote the first compiler and programming language. 15:37 [LAUGH] So 15:39 obviously we are interested. 15:41 And women actually used to make up the vast majority of people in tech. 15:44 Because typing was a woman's job. And computers involved typing. 15:48 The word computer actually comes from the job 15:53 title that women used to have, to compute things. 15:55 So we have women to thank for that. 15:58 The word bug comes from a woman, it's pretty cool. 16:00 And then, maybe you're thinking, 16:02 well, maybe women just aren't biologically predisposed to programming. 16:05 Which is, which is interesting because I know definitely that I have 16:11 not been eaten by a cheetah because I'm a very good programmer. 16:15 So, 16:19 so there's no physical or biological difference and science has proven this. 16:21 And The nice thing about this is overwhelming as this problem is 16:27 to me is that this means that it's purely a social construct. 16:31 This means that we can solve this problem, this is something that you know we aren't 16:36 going up against genetics. We aren't going up against what is inside 16:41 of women that we can't necessarily change, or inside 16:46 of people of color that we can't necessarily change. 16:49 This is something that we can all work on together. 16:51 So, one of the countries that I left out before, is Bulgaria. 16:54 [BLANK_AUDIO] 16:58 I like that I heard wow. 17:01 [LAUGH] So Bulgaria has 73% of their CS grads are women. 17:02 And this is actually on part with a 17:09 lot of Easter European and Western Asian countries. 17:12 It's kind of amazing. 17:16 So, so obviously it's not a biological difference. 17:17 Women are doing pretty good there. So what is the difference? 17:20 What is, what is Bulgaria doing that's different? 17:22 And, the interesting thing is 17:25 Bulgaria is very big on promoting sciences technology, mathematics. 17:26 They really want to see more people 17:34 in engineering, because the country needs it badly. 17:36 So they give kids toys that promote these kind of skills. 17:38 They push all of their kids in middle 17:42 and high school towards these kinds of disciplines. 17:44 So we can be doing this. 17:47 We just have to realize that those are avenues we can work on and 17:48 then kinda work towards it. So, why does diversity matter? 17:51 It matters to me. 17:57 To me it's, it's a very big moral problem for me. 17:58 I want to see as many people in Tech as possible, it's something that I love. 18:01 I really didn't feel like I knew who I was until I was in Tech. 18:04 I had something that I was very good at and I found a community of people 18:08 that loved doing what they were doing, they 18:11 were passionate about what they did, they loved being 18:13 around other people that did the kinds of things 18:17 that they did, they love solving problems I mean 18:19 this is the perfect job if you're the kid 18:21 that likes playing puzzle games when you're a kid, right? 18:23 Like Tetris, hello. 18:26 So perfect. 18:28 So, so, why does it matter not only from 18:31 a moral standpoint, but to the rest of the world? 18:33 Now diversity matters to businesses, how many people have jobs? 18:37 Pretty good amount if you don't have jobs, I'm sure 18:42 there are other people that can help you out with that. 18:44 So, the vast majority of the people here, work for a living. 18:46 And, in the United States in general, probably work for a living. 18:50 And, diversity matters to businesses. 18:53 We see that as racial and gender workforce 18:55 diversity increases, we have an increase of sales revenue. 18:58 Number of customer's market share and profits relative to competitors. 19:02 So that's pretty huge. 19:07 Diverse teams are also able to solve complex problems better and faster. 19:10 That's something that's kind of important in programming and design. 19:14 I mean, how many people have realistic deadlines at work, right? 19:17 A little bit important. 19:20 And groups exposed to minority viewpoints have more, are more 19:24 creative and they're stimulated by 19:28 persistent exposure to minority perspectives. 19:30 So there's a lot of things 19:34 going here. 19:35 We're able to make better decisions and generate more innovation. 19:35 We, our, our economy relies on our technological innovation right now. 19:39 This is really the only sector that we're really growing and making progress. 19:45 And we're not doing it at nearly the rate that the 19:48 rest of the world, rest of the world is, pardon me. 19:50 So the financial success and viability of our industry and our economy directly 19:54 relates to how diverse our workforce is. 20:00 And how diverse the people that are in positions 20:03 where they're able to innovate and solve problems are. 20:06 Diversity also matters a lot to society. 20:10 There are a lot of issues as I 20:13 mentioned before with unequal pay in the United States. 20:14 [BLANK_AUDIO] 20:17 And, this problem, this rift, hasn't gotten better 20:22 enough over the past 20 years. This rift seems to be growing. 20:28 Like I said, the, the rate that women are paid 20:34 compared to men actually fell in the past five years. 20:36 Women are employed more in the workforce than men and yet we still get paid less. 20:40 Doesn't really much sense. 20:45 We can also create class mobility. 20:48 Not only in, in the United States, but elsewhere in the 20:50 world, what class people are born into tends to be the 20:52 class they die in, tends to be the class their children 20:55 are born in, tends to be the class their children die in. 20:58 If we are able to pay people to work in 21:01 an industry where we get paid a stupid amount of money. 21:04 Like I mean really the first paycheck I got 21:07 I was getting paid more then my dad's current job. 21:10 That's a stupid amount of money. We're getting paid so well we can have. 21:13 People that are able to pull themselves out 21:19 of their class do better for their children, give 21:21 their children better educations and more opportunities than 21:23 they haven't been afforded in their family for generations. 21:27 The wage gap in stem, which is science technology 21:32 engineering and math, is actually much smaller in all fields. 21:34 And I mentioned before that women make 80.7% of what men do. 21:39 In tech, women earn 87% of what men do. 21:43 That's a big difference, 7%. 21:47 So why the lack of diversity? 21:49 There are a few different issues at play here. 21:51 One is pipeline. 21:54 These are the people that are coming into the industry. 21:54 There are a lot of cultural cues about Who belongs in tech? 21:58 Who, who can be seen as a geek? 22:01 How many movies can you think of where the geeks are women? 22:04 Where the geeks are people of color? 22:07 Usually they're awkward white male single straight movies and 22:09 in TV for basically as long as we can remember. 22:15 The different is in toys and games for boys and girls. 22:18 We give boys Legos we give girls dolls. 22:21 Promote your imagination take care of children. 22:24 There are no famous role models 22:28 that represent us. 22:29 We have people like Mark Zuckerberg Bill Gates Steve Jobs. 22:30 What can you see is the same between all these people? 22:34 We need to have more people that are being promoted to these areas that represent 22:37 the different group of people that can 22:41 show people that's something that I can do. 22:43 We need to increase the access to technology. 22:47 Boys get their first computer, on average at about age 11 and girls, 22:51 at the age of 14. 22:54 Now I can tell you as awkward of a teenager as I was, that I 22:56 could of done a lot if I had a computer for an extra three years. 23:00 There's a lot that I could of learned and, and done for myself. 23:03 And we have this issues where there a lot of women in the industry now that are 23:06 trying to make up time that their male peers 23:09 have you know, have already made up long ago. 23:12 There are lower computer ownership rates and broadband 23:17 adoptions amongst African American and Hispanic households. 23:20 But they adopt smartphones at much higher rates. 23:24 Now, think to yourself, how many of the web applications or websites that you have 23:27 put together Have a mobile website or mobile site, mobile app, pardon me. 23:33 Now, what is the experience like? Are you able to do as much, laughing yes. 23:40 Are you able 23:44 to do as much stuff as you are on the desktop version? 23:45 Are you able to are you able to have the same experience? 23:50 Access to quality education. 23:54 High school. 23:57 The quality of your high school education is 23:58 one of the greatest indicators of your earning potential. 23:59 Schools in poor neighborhoods have lower quality math and science programs. 24:03 There's a 25% difference in readiness of African American and 24:07 Hispanic students as compared to white students when it comes to 24:12 mathematics and sciences. 24:14 Because the quality of education that they're 24:17 getting from their schools is so much degraded. 24:18 Access to healthcare. 24:22 People of color people with disabilities, LGBTQ people 24:23 all suffer with less access of quality healthcare. 24:26 Think of all the people who are unable to leave their 24:30 current jobs because it means that they're going to lose their healthcare. 24:33 Or that they can't afford COBRA. 24:37 For some 24:39 people this might mean, having medication that actually enables them to live. 24:39 I have a friend that has epilepsy, and if he doesn't take his 24:44 medication every day, it's likely that he could have a seizure and die. 24:47 Is that something that you're willing to give up? 24:50 That you're willing to risk your life to change your job? 24:52 Women are more likely to be caregivers for both children and adult dependents. 24:57 This means that women have less free time to go out and go to 25:02 meet ups, go to conferences that they're less 25:05 likely to be able to contribute to open source. 25:07 The second area is attraction. 25:12 Like I said, the lack of role models. 25:13 We're less likely to see people 25:15 representing us in companies, at conferences. 25:16 This conference excluded, for the most part. 25:20 We have this idea of the geek stereotype. 25:23 We have people who we can't identify with that are in these roles. 25:26 And it's interesting there was a study done 25:31 that said that the sense of belonging really matters. 25:33 And it's interesting because it affects both people of 25:37 any gender and any race that bringing, if you have 25:41 a college class and of mixed gender and mixed race 25:45 and you bring somebody in that meets the geek stereotype. 25:49 So this is somebody who is socially awkward they're white they're male they, 25:52 they don't carry themselves very well they may be very introverted 25:57 and you have them teach a class to these students the students 26:01 are much less likely to continue through the end of that program 26:05 computer science or design programs if they don't identify with that person. 26:08 Attrition, 56% of women leave tech within 10 years. 26:12 I, [LAUGH], I am so happy every day that I continue to be in this industry. 26:19 I've been in the industry for 12 years. 26:23 But I feel like this is a clock that is ticking against me everyday. 26:24 This attrition rate is twice the rate of men. 26:29 This includes for time in and out of the 26:32 industry, leaving to have children, leaving for health reasons. 26:35 That's a really scary number to me. 26:39 We leave for reasons like harassment, 26:41 whoops, I'm missing some slides, harassment. 26:44 People in marginalized groups are twice as likely to be harassed or mistreated. 26:49 And then you'll say things like I've never 26:54 seen someone get harassed, and this is actually interesting. 26:55 When you take the, the likelihood of say, a white person 26:59 being in a crowd, or at a meet-up, where the vast 27:04 majority of the people or all of the people are white, 27:07 how likely are you to see somebody say something that's racially charged. 27:09 Pretty 27:14 unlikely. 27:14 We need to be putting ourselves in positions where we're able 27:15 to see this stuff and help put a stop to it. 27:17 Things like discrimination, we have a 27:20 difference in pay, advancement, and job offers. 27:22 Men are 2.7 times more likely to be promoted 27:24 than women to high-ranking C-level or senior management positions. 27:28 And women are just as likely to be at the rung right below them. 27:33 So what do we do about this? 27:38 Congratulations, you've been promoted to my army. 27:40 Just pretend that you all get one of those 27:43 little, the little stars that Woody had in Toy Story. 27:44 The change starts with us. 27:49 This is really hard to do, and I can't do it by myself. 27:51 There are a lot of really awesome people that are working on this problem. 27:53 And I know that everybody here cares about their coworkers 27:56 and they care about the people that they see at conferences. 27:59 And they want to see everybody treated equally, so I'm 28:02 asking for your help. Education is the trojan horse to empathy. 28:05 People will not go after this information if it doesn't affect them. 28:09 We need to be talking about these issues with people. 28:13 Telling people the statistics. 28:15 Telling people about people that have left the industry and why. 28:17 Get to know people that are different than us. 28:21 I play this game where I go to a conference especially because like I'm 28:23 pretty extroverted up here but I have a hard time when I'm out with a 28:26 bunch of people and I don't know them. 28:30 Where I won't necessarily put myself out there to talk to them. 28:31 So I pick a color, and I talk to all of the people 28:34 wearing that color at a conference gives you a really good mix of people, 28:37 like you're, you're obviously not discriminating 28:41 if you say, okay, today I'm gonna 28:42 go and to talk to everybody who's wearing blue, or everybody who's wearing plaid. 28:44 That is like the bonus mode. >> [LAUGH] 28:47 >> Bias and discrimination are often subtle. 28:53 I was actually 15 or 16 the first time I found out that my grandmother rather 28:56 the time that I found out that my 29:00 grandmother was a programmer, which is really awesome. 29:01 Like, I, I had heard about all of these 29:04 women in the 60s and 70s that were programmers and 29:07 was like oh man, it would be awesome to 29:10 talk to them and see what their experiences were like. 29:11 My grandmother's like, we'll I'm a programmer. 29:13 And I was like hey, that's a really great source of information. 29:15 So my grandmother was a programmer started being a programmer in the 80s. 29:18 She worked in an insurance company. 29:22 And she told me this awesome story about how when she 29:25 worked there this insurance company all of the programmers were women. 29:28 I was like, sweet I'd love to work in that atmosphere. 29:31 I've been a programmer for 12 years and worked with two women. 29:33 That's really sad. 29:36 She's like yeah so, so we all worked right in the middle of the office. 29:37 Everybody else, their offices were around ours. 29:41 And we 29:43 worked in the middle of the office in a cage, that was locked from the inside. 29:43 I was like okay, that's a little weird. 29:49 And she said, so they put up the cage because 29:52 Men would come and touch us while we are programming. 29:55 And I was like, oh, like. 29:58 Thankfully, things have changed so I don't have to work in a cage. 30:01 I, I am not, I'm not a monkey in a zoo, which is very nice. 30:05 But that's reallly scary. 30:09 And, and that's the way that things used to be. 30:10 You know harassment was much more obvious, much more deliberate. 30:13 It was much more societally accepted. 30:17 You know the difference between you know, you know the difference. 30:19 You know it's not okay to like say, slap 30:23 someone on the ass walking down the hallway, right? 30:26 Like, that's something you should, you probably know you shouldn't do, hopefully. 30:28 If not, you know now, please don't do it. [LAUGH] But 30:31 a lot of the things that we face today are very subtle. 30:35 I give talks to a lot of conferences and I was at a conference a 30:38 few months back, and I got up and say stage on set, hi my name is 30:41 Ashe Dryden a programmer, I love what I do, I've been doing it for 12 30:43 years, and at the after party, four people asked me if I was a project manager. 30:47 Now were they doing something mean? 30:51 For sure not, no. 30:53 They didn't mean anything mean by that but the kind of constant you know we assume 30:55 that women are are in a very specific role so you must fit 31:00 into that role the kind of constant reminder that you are different than me. 31:03 That expect you to be in a different position than me, was just aggravating. 31:08 So by the time the fourth person comes up to me, 31:12 and I'm frustrated, am I allowed to get upset at this person? 31:14 I mean, there, I have reason to be upset. 31:18 But this person doesn't know. So it's very hard, it's very subtle. 31:20 We have 31:25 to learn to apologize. 31:25 If you realize that these issues are subtle. 31:27 Think about, like, accidentally stepping on someone's toe. 31:31 You didn't mean to do that. And your first reaction is, oh, I'm sorry. 31:36 I didn't mean to step on you. 31:39 Like, ha ha, it's fine. I'm sorry. 31:40 And walk away, right? 31:42 And that's perfectly fine. 31:43 But when it comes to these issues that are really hard for us to talk about. 31:44 And really hard to think about us making these mistakes. 31:48 Maybe hurting somebody's feelings or hurting somebody's chances at something. 31:51 It's, it's really difficult to not get defensive or ashamed and kind of run away. 31:54 So, I have this awesome recipe for an apology, 31:59 and there's this recipe online that I've kind of mirrored. 32:02 It's how to make ice. Which I love that that exists. 32:05 I'm asking you this, get an ice cube tray, put water in it, stick it 32:08 in the freezer, so like that's pretty, pretty easy, that makes a lot of sense. 32:12 So, apologies are the same way, three steps, what I did was wrong, I'm sorry. 32:16 If you say the word but, stop and start over, cuz that's not an apology. 32:21 And then try and make it up by not doing it again. 32:26 And I screw this up all the time. 32:28 I talk about this stuff all the time. 32:30 I consider myself to be very well educated on these, in these issues. 32:31 And I make stupid mistakes all the time. 32:35 And, let me tell you, it's so much easier to recover and re-earn 32:37 the goodwill that people had for you at first. 32:42 When you say you are right, I am sorry versus what 32:44 are you talking about, I didn't do that, so very big difference. 32:47 We can advocate for change. 32:52 I'm very lucky if I have the privilege to be able to stand 32:53 in front of a large group of people and talk about these issues today. 32:56 There are lot of people that should be talking about the 32:59 issues that effect them, they don't have the opportunity that I do. 33:02 You're in a very lucky 33:05 position where you're able to talk to your coworkers, your boss, the conferences 33:07 you go to, the people who organize 33:10 meet-ups and talk about these issues openly. 33:12 The more people who are exposed these kind of issues 33:15 whether it's at a conference or on Twitter, anywhere that 33:18 they see that this kind of behavior is unacceptable or 33:22 oops I made a mistake and let me fix it. 33:25 They'll realize that this is something we can all work on together. 33:28 It also 33:31 helps to be able to educate bystanders. 33:32 I don't want everybody to have to be able to touch the stove to realize it's hot. 33:34 This is actually something I use quite a lot on Twitter. 33:40 If you follow me on Twitter you know that 33:42 I get into a lot of heated discussions on Twitter. 33:43 And it doesn't always go well, but most of the time I start out with this. 33:47 That's not cool. 33:52 What you just said isn't okay. Like, here is why. 33:53 Let me help you fix this, and it's just enough for people to, to 33:56 like resettle themselves and be like, oh right, yeah, that, I don't, I don't 34:01 necessarily understand why it's not okay and I'm willing 34:05 to talk to you about that, but I'm sorry. 34:07 Have hard conversations with people. 34:11 It takes a lot of education for people to start coming around to these issues, 34:13 and very few people, like I said, 34:17 are gonna seek out this information for themselves. 34:18 And through this, we can influence change in our communities and our workplaces. 34:21 And my personal goal is to see more companies that have women programmers, 34:25 women designers, women that are in more positions in power. 34:30 I would love to see more companies that 34:34 are hiring more people of color and retaining them. 34:36 Retaining people is so much harder than recruiting. 34:39 We can increase education and access 34:43 Through helping facilitate events for marginalized people. 34:46 Black Girls Code is a really awesome one and that one's all over the United States. 34:48 There's a group called Latino Start-ups, Black 34:52 Entrepreneurs, Girl Develop It, Pi Ladies, Rails Bridge. 34:55 They're everywhere. 34:59 If you're looking for one in your city, come 34:59 talk to me and I'll help you find one. 35:00 Volunteer at local schools and groups. 35:03 Things like Girl Scouts, Boy Scouts, Boys and Girls Clubs. 35:05 And focus on groups that don't normally have access. 35:08 Please don't go to schools in rich neighborhoods and ask them if 35:11 you can teach them programming or teach them how to do Photoshop. 35:14 They don't need it. 35:17 They already have those resources available. 35:18 We can commit financial resources. 35:20 If you are currently donating money to your 35:23 university and you went to an Ivy, stop. 35:25 They have a lot of money and they've gotten a lot of money from you already. 35:29 Think about donating that money to a scholarship fund 35:33 a technical college a women's college an historical black college. 35:36 There are a lot of communities that could use 35:40 this money a lot more than Yale and Harvard can. 35:41 Work with colleges and 35:45 universities and ask them what they're doing to 35:46 help students who've had less exposure to technology. 35:49 To begin help remove the bias from schools and universities. 35:51 There's a really awesome instance of this college called Harvey Mudd. 35:56 They doubled the number of female CS grads in one year by asking one question. 36:00 And that was, have you programmed before? 36:05 It's a very simple question but they found that 36:09 separated women. 36:11 Or separate, I'm sorry, separating people who 36:12 hadn't programmed before into a separate room 36:14 where they didn't feel as threatened by the people who knew more than them. 36:16 Gave them so much more confidence to be able to ask questions. 36:19 To not feel stupid because they didn't know, that other people have been 36:22 doing this for ten years and that's perfectly fine to not know yet. 36:26 They're very simple things that we can, enact to create great change. 36:30 We can change our workplaces. 36:35 Think about what the about page of your website at work looks like. 36:37 If yours is pretty good, take a look at 100%men.tumblr.com. 36:40 [LAUGH] This is unfortunately a who's who's list 36:46 of people in silicon, or companies in silicon valley. 36:50 And large companies elsewhere in the United 36:53 States where their about page's all men. 36:55 And about 95% all white men. 36:57 We can start changing our culture. 37:01 Think of your culture like a garden. You want to value variety and prune weeds. 37:03 Sometimes, you have to fire people to be able to get great people in. 37:08 That might mean firing the best person on your team. 37:15 That's really hard to do I, I definitely don't don't envy 37:18 you for having to do that but sometimes you have to. 37:22 Vocally supports diversity statements. 37:25 Conferences like this make diversity important. 37:28 Attend events and job fairs for marginalized communities. 37:31 Do not only go to predominantly white men 37:35 universities and try and recruit people. 37:40 You're going to get the same kind of recruits. 37:43 Check your job listing language and requirements. 37:46 There's another awesome Tumblr called Tech Companies Who Only Hire Men. 37:49 And this is job requirements that list words like 37:52 he, this guy will, this man will, this dude will. 37:56 That's a very subtle signal that women neces, aren't necessarily welcome. 38:00 Change 38:05 or benefits, or at least know what they are. 38:05 Do you have same sex partner benefits, 38:08 domestic partner benefits, benefits for unmarried partners? 38:10 You have trans inclusive healthcare. 38:13 California was the first state to require trans inclusive healthcare on all plans. 38:16 What is your maternity and paternity leave policy? 38:21 Do you offer flex care for things like dependent care or medical appointments? 38:26 Change the way you're interviewing. 38:30 Your interview should model the way that people work everyday. 38:32 If you don't require that people white board problems 38:35 everyday at work, maybe don't require it in the interview. 38:39 Equal pay. 38:43 Do a regular payroll audit and make sure that 38:44 people are being paid what they should be being paid. 38:47 The wage gap greatly expands the longer somebody's in the industry. 38:50 Women automatically start out getting paid 10% less than men. 38:55 And that grows to 20% by the end of ten years. 39:00 Offer things like mentoring and career goal attainment. 39:05 Ask people do you want to be in a different position? 39:08 Do you want to cross train on a different team? 39:10 Do you want to be in some kind of lead architect role or creative director role? 39:13 What can we do to help you get there? 39:18 So, kind of wrapping up, this is a lot of stuff, I know. 39:21 It's very overwhelming. 39:25 The problem seems humongous and insurmountable. 39:26 I talk to people every day about what 39:32 the difference of one mentor has made for them. 39:34 One person who offered them a chance on their computer 39:37 at home, cuz they didn't have a computer at home. 39:41 Or they were able to get a scholarship at the university they 39:43 wouldn't otherwise have been able to afford, or just going to a meetup 39:46 group and somebody was willing to help them install programs that they 39:50 wouldn't have been able to figure out how to do on their own. 39:54 So, this requires everybody involved. Like I said, you've been deputized. 39:57 Thank you pre-emtively and I would love to talk 40:04 to you tonight about what we can accomplish together. 40:11 Thank you. 40:13 [NOISE] 40:18
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