1 00:00:05,400 --> 00:00:08,520 Speaker 1: Hey, this is Emily and you're listening to stuff Mom 2 00:00:08,640 --> 00:00:23,360 Speaker 1: never told you. Today is the continuation of our conversation 3 00:00:23,480 --> 00:00:27,160 Speaker 1: on Silicon Valley sexism. So if you haven't already, make 4 00:00:27,200 --> 00:00:29,760 Speaker 1: sure to check out part one of the episode that 5 00:00:29,800 --> 00:00:32,720 Speaker 1: came out just two days ago. And in that episode 6 00:00:32,720 --> 00:00:35,960 Speaker 1: you'll hear just how big of an issue sexism in 7 00:00:36,040 --> 00:00:41,760 Speaker 1: Silicon Valley and tech fields large really are, including the 8 00:00:41,920 --> 00:00:44,520 Speaker 1: startling fact that women are leaving the world of tech 9 00:00:44,560 --> 00:00:47,640 Speaker 1: at twice the rate that their male counterparts are. You'll 10 00:00:47,840 --> 00:00:50,960 Speaker 1: learn more about the brave women and whistleblowers who are 11 00:00:51,000 --> 00:00:56,160 Speaker 1: calling out tech companies on their gender discrimination and harassment issues, 12 00:00:56,240 --> 00:01:00,840 Speaker 1: including Ellen Pow and Susan Fowler at Uber. You'll also 13 00:01:01,080 --> 00:01:04,800 Speaker 1: hear some of the underlying reasons behind tech, which is 14 00:01:04,840 --> 00:01:09,160 Speaker 1: really a very young industry. It's still struggling so obviously 15 00:01:09,480 --> 00:01:13,520 Speaker 1: with gender inclusion, things like the genius fallacy and the 16 00:01:13,640 --> 00:01:16,160 Speaker 1: meritocracy paradox. So if you haven't heard that episode yet, 17 00:01:16,200 --> 00:01:18,960 Speaker 1: go back there listen to it. It's full of good stuff. 18 00:01:19,160 --> 00:01:22,040 Speaker 1: So today we want to take it back a second 19 00:01:22,120 --> 00:01:25,600 Speaker 1: and ask the question that is on some people's minds, 20 00:01:25,840 --> 00:01:29,560 Speaker 1: which is why does diversity in tech even matter. Only 21 00:01:29,640 --> 00:01:32,560 Speaker 1: nineteen percent of students and computer science programs are women, 22 00:01:32,720 --> 00:01:34,680 Speaker 1: so she would really even care that a quarter of 23 00:01:34,800 --> 00:01:37,800 Speaker 1: US computing and mathematic jobs, a fraction that has actually 24 00:01:37,880 --> 00:01:40,800 Speaker 1: slightly fallen over the past fifteen years, are held by women. 25 00:01:41,200 --> 00:01:43,320 Speaker 1: Some might even argue that we don't even really need 26 00:01:43,400 --> 00:01:47,039 Speaker 1: culture change, that those kinds of cultural changes are actually quote, 27 00:01:47,120 --> 00:01:50,640 Speaker 1: social engineering run amok. Some people might even say women 28 00:01:50,640 --> 00:01:52,920 Speaker 1: are showing up more in tech spaces because they're not 29 00:01:53,000 --> 00:01:56,360 Speaker 1: really born being good at tech, or that's not something 30 00:01:56,360 --> 00:01:58,960 Speaker 1: that women want to be doing. And by some people 31 00:01:59,160 --> 00:02:03,600 Speaker 1: bridget we have horse are talking about Google Guy, the 32 00:02:03,640 --> 00:02:08,480 Speaker 1: guy behind the viral manifesto released at Google last month 33 00:02:08,520 --> 00:02:11,440 Speaker 1: that we absolutely have to talk about. I almost don't 34 00:02:11,440 --> 00:02:14,519 Speaker 1: want to say his name, calling him Google Guy. That's 35 00:02:14,520 --> 00:02:18,560 Speaker 1: his name, forever and ever exactly learned it. And before 36 00:02:18,600 --> 00:02:20,359 Speaker 1: we continue, I just have to also go on the 37 00:02:20,400 --> 00:02:22,520 Speaker 1: record is saying Google has paid me money in the 38 00:02:22,520 --> 00:02:27,120 Speaker 1: past for my gender inclusive training public speaking for their 39 00:02:27,160 --> 00:02:32,519 Speaker 1: women at Google. Yeah, and I'm not sorry about it. 40 00:02:33,200 --> 00:02:35,080 Speaker 1: So let's talk about what went down at Google last month, 41 00:02:35,120 --> 00:02:37,800 Speaker 1: because it really did send shock waves through an already 42 00:02:37,880 --> 00:02:42,600 Speaker 1: dramatic conversation. So basically, Google guy, as we're now calling him, 43 00:02:42,760 --> 00:02:45,960 Speaker 1: wrote this, I guess you would call it a manifesto 44 00:02:46,520 --> 00:02:48,079 Speaker 1: if that's the way you want to call it. That's 45 00:02:48,120 --> 00:02:50,680 Speaker 1: what he called it is. Basically his argument is that 46 00:02:50,840 --> 00:02:53,560 Speaker 1: places like Google are creating quote safe spaces, and those 47 00:02:53,600 --> 00:02:56,960 Speaker 1: spaces are actually discriminating to promote women and minorities over 48 00:02:57,040 --> 00:03:00,800 Speaker 1: white men, the poor, poor, disenfranchise as white men. And 49 00:03:00,840 --> 00:03:02,400 Speaker 1: this is not a new argument. It is not a 50 00:03:02,400 --> 00:03:04,680 Speaker 1: new argument. But I think it's interesting is how you 51 00:03:04,720 --> 00:03:07,359 Speaker 1: can tell from the from the manifesto, he thinks it's 52 00:03:07,400 --> 00:03:09,760 Speaker 1: the newest thing. Like he's blowing the lid right off 53 00:03:09,760 --> 00:03:12,440 Speaker 1: this thing, these things that he's like at the forefront 54 00:03:12,480 --> 00:03:15,760 Speaker 1: of this, he think he's a genius. He might be 55 00:03:15,800 --> 00:03:17,799 Speaker 1: caught up in the genius fallacy. If if I knew 56 00:03:17,840 --> 00:03:19,760 Speaker 1: this guy in real life, I might say that. But yeah, 57 00:03:19,760 --> 00:03:21,720 Speaker 1: and this is the same argument that's been brought to 58 00:03:22,000 --> 00:03:24,920 Speaker 1: affirmative action in the past when it comes to college acceptance. 59 00:03:25,240 --> 00:03:30,240 Speaker 1: This idea that you know anything beyond nature, right, what's 60 00:03:30,400 --> 00:03:33,880 Speaker 1: naturally occurring in the marketplace when it comes to achievement, 61 00:03:34,120 --> 00:03:38,440 Speaker 1: hiring promotion is what we should value and we shouldn't 62 00:03:38,520 --> 00:03:42,160 Speaker 1: look at how nurture might be impeding not only the 63 00:03:42,200 --> 00:03:45,000 Speaker 1: pipeline of women in tech, but also the promotion of 64 00:03:45,040 --> 00:03:47,440 Speaker 1: women in tech. Basically, what he's saying is anything that 65 00:03:47,520 --> 00:03:51,760 Speaker 1: we do to mess with what's naturally occurring too. I 66 00:03:51,760 --> 00:03:55,760 Speaker 1: don't know, advance women and minorities in tech because there 67 00:03:55,760 --> 00:03:59,320 Speaker 1: are benefits of diversity in tech is social engineering and 68 00:03:59,400 --> 00:04:02,160 Speaker 1: it's bad, right And his his What I think is 69 00:04:02,200 --> 00:04:04,600 Speaker 1: so messed up about that argument is it totally overlooks 70 00:04:04,600 --> 00:04:06,760 Speaker 1: the fact that we live in a gosh dang society, right, 71 00:04:06,800 --> 00:04:09,600 Speaker 1: Like people are not competing with each other in a 72 00:04:09,680 --> 00:04:13,480 Speaker 1: vacuum where no other kinds of factors, underlying factors are 73 00:04:13,520 --> 00:04:15,880 Speaker 1: at play. We're in a society and in the world 74 00:04:15,880 --> 00:04:19,040 Speaker 1: that he describes, all things are equal. We're in a vacuum. 75 00:04:19,040 --> 00:04:20,960 Speaker 1: We don't live in a society. Everyone is the same 76 00:04:21,040 --> 00:04:23,240 Speaker 1: and that's I mean, hello, is not the case. And 77 00:04:23,279 --> 00:04:27,839 Speaker 1: what shocking is if you look at how people treat babies. 78 00:04:28,080 --> 00:04:31,719 Speaker 1: I'm talking fifteen month old boys versus fifteen month old girls. 79 00:04:32,360 --> 00:04:34,960 Speaker 1: A viral video came out recently that I stumbled across 80 00:04:34,960 --> 00:04:39,040 Speaker 1: on Facebook that show when an adult is playing with 81 00:04:39,200 --> 00:04:41,840 Speaker 1: a little baby who they think is a girl, they're 82 00:04:41,839 --> 00:04:45,239 Speaker 1: more likely to give that little baby toys that are dolls, 83 00:04:45,320 --> 00:04:49,400 Speaker 1: toys like soft, plushy toys, not the kind of toys 84 00:04:49,839 --> 00:04:53,039 Speaker 1: like connects and legos and things that maybe not a 85 00:04:53,040 --> 00:04:54,880 Speaker 1: fifteen month old should be playing with Legos, but you 86 00:04:54,960 --> 00:04:58,200 Speaker 1: get the idea, not the things that help baby brains 87 00:04:58,200 --> 00:05:01,960 Speaker 1: develop spatial awareness, us the kinds of skills that set 88 00:05:01,960 --> 00:05:04,479 Speaker 1: them up to kick but in tech. So if you're 89 00:05:04,480 --> 00:05:08,360 Speaker 1: saying that it's pure nature and nurture has nothing to 90 00:05:08,400 --> 00:05:12,359 Speaker 1: do with men's proclivity for the sciences and stem fields, 91 00:05:12,360 --> 00:05:15,560 Speaker 1: not just advancement we can like, which we can talk 92 00:05:15,600 --> 00:05:19,520 Speaker 1: about bias and hiring and promotion too, then I think 93 00:05:19,560 --> 00:05:22,480 Speaker 1: you're missing the bigger, more influential point, which is nurture 94 00:05:22,640 --> 00:05:25,200 Speaker 1: and how nurture and how the society we live in 95 00:05:25,720 --> 00:05:28,320 Speaker 1: sets up winners and losers totally. And I think they've 96 00:05:28,320 --> 00:05:31,360 Speaker 1: even done studies that show that we start treating people 97 00:05:31,400 --> 00:05:34,280 Speaker 1: differently along the lines of gender in the womb, that 98 00:05:34,279 --> 00:05:37,360 Speaker 1: that stuff is happening already, you know. And I think 99 00:05:37,680 --> 00:05:39,919 Speaker 1: this idea that all kinds of people have been treated 100 00:05:39,960 --> 00:05:42,120 Speaker 1: the same throughout life is just bunk. And his manifesto 101 00:05:42,200 --> 00:05:45,080 Speaker 1: doesn't even acknowledge that exactly. And here's the thing, he 102 00:05:45,279 --> 00:05:51,280 Speaker 1: was very careful to appear reasonable. That's my of all 103 00:05:51,279 --> 00:05:53,320 Speaker 1: the things that are my least favorite thing about Google guy, 104 00:05:53,520 --> 00:05:55,400 Speaker 1: that's the one I hate the most. I'm trying to 105 00:05:55,480 --> 00:05:58,200 Speaker 1: not like someone. He reminds me so much of someone 106 00:05:58,240 --> 00:05:59,800 Speaker 1: I know, so I'm trying not to I'm trying not 107 00:05:59,839 --> 00:06:02,359 Speaker 1: to like go off on this guy who I hate 108 00:06:02,760 --> 00:06:04,760 Speaker 1: as a proxy for him. But screw what I'm doing 109 00:06:04,760 --> 00:06:07,680 Speaker 1: it anyway. Basically, it's this idea that oh, if I 110 00:06:07,720 --> 00:06:12,240 Speaker 1: appear very rational and like like, I'm just presenting the 111 00:06:12,360 --> 00:06:15,920 Speaker 1: data in a very rational way, don't attack me. These 112 00:06:15,960 --> 00:06:18,800 Speaker 1: are the facts. But he doesn't actually rely on a 113 00:06:18,800 --> 00:06:21,880 Speaker 1: lot of facts, and he spews a lot of really racist, 114 00:06:21,920 --> 00:06:25,320 Speaker 1: messed up stuff with no grounding and facts. There are 115 00:06:25,320 --> 00:06:27,280 Speaker 1: actually the kinds of things that have been you know, 116 00:06:27,480 --> 00:06:30,800 Speaker 1: circulated on you know, racist corners of the Internet since forever. 117 00:06:30,960 --> 00:06:33,200 Speaker 1: So these are not new ideas. There are actually a 118 00:06:33,200 --> 00:06:35,360 Speaker 1: lot of his points are not actually grounded by data 119 00:06:35,440 --> 00:06:38,160 Speaker 1: or facts, but he's pretending as if they are in 120 00:06:38,200 --> 00:06:43,039 Speaker 1: this attempt to seem really reasonable and you know, measured exactly, 121 00:06:43,160 --> 00:06:48,600 Speaker 1: he disguises what is basically of racist, misogynistic rant in 122 00:06:49,200 --> 00:06:52,120 Speaker 1: this really approachable packaging of I just want to raise 123 00:06:52,120 --> 00:06:54,000 Speaker 1: the alarm bell on this. I just don't want to 124 00:06:54,000 --> 00:06:57,600 Speaker 1: be a part of a culture at Google where you know, 125 00:06:57,760 --> 00:07:00,479 Speaker 1: women and minorities voices are louder and the only right 126 00:07:00,600 --> 00:07:03,000 Speaker 1: voices here, and that's concerning to me. And I should 127 00:07:03,000 --> 00:07:06,000 Speaker 1: be able to dissent without being eaten alive. And he 128 00:07:06,080 --> 00:07:08,200 Speaker 1: makes a very reasonable argument on that, and he goes 129 00:07:08,240 --> 00:07:11,200 Speaker 1: in to say a very unreasonable argument something. I think 130 00:07:11,240 --> 00:07:13,240 Speaker 1: it is so interesting is that again, even though he 131 00:07:13,280 --> 00:07:16,880 Speaker 1: clearly thinks this his new ground, that rhetorical device is 132 00:07:16,920 --> 00:07:18,960 Speaker 1: so common and even as a name where they call 133 00:07:19,000 --> 00:07:22,560 Speaker 1: it just asking questions, I'm just asking questions. I'm not 134 00:07:22,640 --> 00:07:25,160 Speaker 1: presenting data. I'm just asking questions. I just want to 135 00:07:25,200 --> 00:07:28,800 Speaker 1: lift this. And it's a really smarmy rhetorical device to 136 00:07:28,880 --> 00:07:32,640 Speaker 1: seem measured and approachable when you're anything but exactly. And 137 00:07:32,680 --> 00:07:34,920 Speaker 1: it worked because there are lots of corners of the 138 00:07:34,960 --> 00:07:38,000 Speaker 1: Internet where maybe some of our Mingtow friends from our 139 00:07:38,080 --> 00:07:42,200 Speaker 1: Mingtow episode hang out, where there are a ton of 140 00:07:42,280 --> 00:07:46,440 Speaker 1: men who found solace and are cheering Google guy on 141 00:07:46,600 --> 00:07:51,720 Speaker 1: for for bravely speaking out against PC culture. And it 142 00:07:51,840 --> 00:07:54,320 Speaker 1: just really reminds me of some of these white supremacists 143 00:07:54,320 --> 00:07:57,320 Speaker 1: who are trying to seem reasonable and approachable and just 144 00:07:57,480 --> 00:07:59,880 Speaker 1: asking questions. That's a perfect way to put it for 145 00:08:00,000 --> 00:08:03,160 Speaker 1: fortunately for all of us. Cynthia Lee at Vox Media 146 00:08:03,240 --> 00:08:08,000 Speaker 1: wrote the best piece in response to Google Guys memo, 147 00:08:08,360 --> 00:08:11,520 Speaker 1: and it's titled I'm a woman in computer science. Let me, 148 00:08:11,680 --> 00:08:15,960 Speaker 1: lady explain the Google memo to you. I love that title, y'all. 149 00:08:16,040 --> 00:08:18,840 Speaker 1: You've ever heard the phrase an epic drag this? I 150 00:08:18,840 --> 00:08:20,920 Speaker 1: don't use it for Vox articles a lot, but this 151 00:08:21,040 --> 00:08:23,920 Speaker 1: box article is an epic drag. I love it. I 152 00:08:24,080 --> 00:08:26,560 Speaker 1: love it. So she goes on, if I could just 153 00:08:26,600 --> 00:08:28,640 Speaker 1: read the whole article on this podcast, I would. I 154 00:08:28,640 --> 00:08:30,520 Speaker 1: think it would be like job. But clearly we don't 155 00:08:30,520 --> 00:08:33,160 Speaker 1: have that kind of time. What she does so brilliantly 156 00:08:33,280 --> 00:08:38,640 Speaker 1: here is she explains to the public why there was 157 00:08:38,720 --> 00:08:44,120 Speaker 1: such a vociferous backlash against Google Guy, because Google Guys 158 00:08:44,280 --> 00:08:50,360 Speaker 1: memo created so much anger and indignation that it almost 159 00:08:50,400 --> 00:08:52,480 Speaker 1: turned out to look like he was the poor victim 160 00:08:52,520 --> 00:08:54,840 Speaker 1: here because he did get canned by Google, and so 161 00:08:54,920 --> 00:08:57,560 Speaker 1: when he lost his job, everyone said, this is what 162 00:08:57,640 --> 00:08:59,720 Speaker 1: happens in PC culture, run him up. This is what 163 00:08:59,760 --> 00:09:04,400 Speaker 1: happens when you dare to dissent with the progressive social 164 00:09:04,440 --> 00:09:08,480 Speaker 1: engineers over at Google. You know, a poor white guy 165 00:09:08,559 --> 00:09:11,240 Speaker 1: just lost his job for speaking out on behalf of 166 00:09:11,280 --> 00:09:13,200 Speaker 1: all of us. Can I just say, if there's one 167 00:09:13,280 --> 00:09:16,120 Speaker 1: phrase I would love to never hear again, it's PC culture, 168 00:09:16,240 --> 00:09:18,400 Speaker 1: run amuck. I know, I would love to just throw 169 00:09:18,400 --> 00:09:20,040 Speaker 1: that in the dustbin of history. We should do an 170 00:09:20,040 --> 00:09:23,280 Speaker 1: episode on PC. Mean, what does that even mean? But 171 00:09:23,440 --> 00:09:28,760 Speaker 1: that's a whole other tangent. So she beautifully explains why 172 00:09:28,800 --> 00:09:33,840 Speaker 1: a memo that seems so benign and polite and like 173 00:09:34,040 --> 00:09:37,520 Speaker 1: just asking questions the memo, why there was such a backlash, 174 00:09:37,559 --> 00:09:40,040 Speaker 1: And she goes point by points, starting with a really 175 00:09:40,080 --> 00:09:43,920 Speaker 1: important one, fatigue. Women are tired, y'all. To be a 176 00:09:43,920 --> 00:09:46,160 Speaker 1: woman in tech is to know the thrill of participating 177 00:09:46,160 --> 00:09:48,840 Speaker 1: in one of the most transformative revolutions human kind has 178 00:09:48,880 --> 00:09:52,240 Speaker 1: ever known. To experience the crystalline satisfaction of finding an 179 00:09:52,240 --> 00:09:55,520 Speaker 1: elegant solution to an algorithmic challenge, to want to throw 180 00:09:55,559 --> 00:09:57,839 Speaker 1: the monitor out the window, and frustration with a bug, 181 00:09:57,880 --> 00:09:59,560 Speaker 1: and later due to a happy dance in a chair, 182 00:09:59,600 --> 00:10:01,680 Speaker 1: we'll find fixing it. To be a woman in tech 183 00:10:01,800 --> 00:10:04,440 Speaker 1: is also to always and forever be faced with skepticism 184 00:10:04,600 --> 00:10:07,680 Speaker 1: that I do and feel all those things authentically enough 185 00:10:07,800 --> 00:10:10,680 Speaker 1: to truly belong. There is always a jury and it 186 00:10:10,800 --> 00:10:14,240 Speaker 1: is always still out. Imagine if this was how you 187 00:10:14,360 --> 00:10:17,199 Speaker 1: felt in work, that you had all these ups and downs, 188 00:10:17,360 --> 00:10:20,280 Speaker 1: and you always felt like your interloper, You always felt 189 00:10:20,280 --> 00:10:22,200 Speaker 1: like your colleagues were looking at you like you didn't 190 00:10:22,240 --> 00:10:24,560 Speaker 1: quite belong. You faced this day in and day out 191 00:10:24,720 --> 00:10:27,640 Speaker 1: a lot of times silently, and then someone comes along 192 00:10:27,679 --> 00:10:30,280 Speaker 1: and says, you know, it really has it bad me 193 00:10:30,760 --> 00:10:34,800 Speaker 1: a white guy, I would be infuriated. Yeah, And also 194 00:10:34,920 --> 00:10:37,920 Speaker 1: she's points to this. You know what people of color 195 00:10:37,960 --> 00:10:40,200 Speaker 1: have been dealing with for a long time. Thankfully, there's 196 00:10:40,200 --> 00:10:43,080 Speaker 1: a term for it now, racial battle fatigue, which is 197 00:10:43,400 --> 00:10:46,240 Speaker 1: dealing with microaggressions at work all the time. Whether they 198 00:10:46,240 --> 00:10:50,800 Speaker 1: are a seemingly innocuous, offhand comment about you being really 199 00:10:50,920 --> 00:10:56,480 Speaker 1: smart for uh woman of color or really articulate for 200 00:10:56,600 --> 00:10:58,920 Speaker 1: being a black man, you're just like what, you know, 201 00:10:58,960 --> 00:11:00,240 Speaker 1: they don't even have to say the lot part of 202 00:11:00,240 --> 00:11:02,760 Speaker 1: the sentence for you to put it together, or being 203 00:11:02,760 --> 00:11:06,520 Speaker 1: told this is the worst You're not like most girls, Bridget, 204 00:11:06,800 --> 00:11:11,199 Speaker 1: You're so much cooler and smarter and better at science 205 00:11:11,240 --> 00:11:14,080 Speaker 1: and technology than most girls. You're like, that is not 206 00:11:14,120 --> 00:11:18,280 Speaker 1: a compliment. Please don't like drop that kind of misogyny 207 00:11:18,320 --> 00:11:21,360 Speaker 1: packaged in a gift box to me. Please. I can 208 00:11:21,360 --> 00:11:24,720 Speaker 1: see how that is meant to be flattering, but it's 209 00:11:25,000 --> 00:11:28,600 Speaker 1: totally She also goes on to acknowledge and really call 210 00:11:28,679 --> 00:11:31,439 Speaker 1: out Google guy for the fact that his memo treats 211 00:11:31,640 --> 00:11:35,079 Speaker 1: women at Google as the pair average. He almost talks 212 00:11:35,120 --> 00:11:40,760 Speaker 1: about women's lack of leadership and rise and retention at 213 00:11:40,800 --> 00:11:45,000 Speaker 1: Google as that he's talking about the average woman. And yes, 214 00:11:45,080 --> 00:11:49,120 Speaker 1: we know that only of computer science majors are women, 215 00:11:49,679 --> 00:11:52,320 Speaker 1: but we also know that the vast majority of women 216 00:11:52,360 --> 00:11:56,600 Speaker 1: at Google are not being pulled from just the average 217 00:11:56,640 --> 00:12:00,920 Speaker 1: pool of all women who study all things. So his 218 00:12:01,080 --> 00:12:03,600 Speaker 1: use of data, just like you mentioned earlier, it is 219 00:12:03,679 --> 00:12:07,240 Speaker 1: very misleading because the data he references are about the 220 00:12:07,320 --> 00:12:10,840 Speaker 1: average woman's proclivity for tech. She goes on to say, 221 00:12:11,000 --> 00:12:13,320 Speaker 1: what do averages have to do with hiring practices at 222 00:12:13,320 --> 00:12:16,840 Speaker 1: a company that famously hires fewer than one percent of applicants. 223 00:12:17,160 --> 00:12:20,199 Speaker 1: In the name of the rational empiricism and quantitative rigor 224 00:12:20,600 --> 00:12:23,400 Speaker 1: that the manifesto holds some dear shouldn't we insist that 225 00:12:23,480 --> 00:12:26,559 Speaker 1: it only cites studies that specifically speak to the tales 226 00:12:26,600 --> 00:12:29,480 Speaker 1: of of the distribution to the actual pool of women 227 00:12:29,480 --> 00:12:31,920 Speaker 1: that Google draws from. So basically, what she's saying here 228 00:12:32,000 --> 00:12:34,720 Speaker 1: is that Google does not hire a lot of people. 229 00:12:34,960 --> 00:12:37,480 Speaker 1: It's specifically does not hire a lot of women. If 230 00:12:37,480 --> 00:12:39,360 Speaker 1: you are a woman who ends up working at Google, 231 00:12:39,600 --> 00:12:43,040 Speaker 1: you are exceptional. You're You're not average. I almost quoted 232 00:12:43,040 --> 00:12:45,920 Speaker 1: Beyonce and swore, but we're not talking about you know 233 00:12:45,920 --> 00:12:48,760 Speaker 1: the lot. I'm going for it. You're not average, you 234 00:12:48,800 --> 00:12:52,360 Speaker 1: are exceptional. And this idea that he's dealing with all 235 00:12:52,440 --> 00:12:55,160 Speaker 1: these average women in tech who are bringing down the company, 236 00:12:55,200 --> 00:12:57,680 Speaker 1: Who are these pity hires because of their gender is 237 00:12:57,880 --> 00:13:01,280 Speaker 1: bf Cynthia goes on to really hammer this point home 238 00:13:01,640 --> 00:13:04,240 Speaker 1: by pointing out that women currently make up about thirty 239 00:13:04,280 --> 00:13:07,280 Speaker 1: percent of computer science majors at Stanford, which is one 240 00:13:07,280 --> 00:13:10,760 Speaker 1: of the key sources of Google's elite workforce. Right Stanford's 241 00:13:10,800 --> 00:13:13,520 Speaker 1: right around the corner, it's in Silicon Valley. Basically, they 242 00:13:13,559 --> 00:13:17,960 Speaker 1: get a lot of recruits from Stanford, but that thirty 243 00:13:18,000 --> 00:13:20,920 Speaker 1: percent turns into just nineteen percent when you look at 244 00:13:20,920 --> 00:13:25,800 Speaker 1: the percentage of Google's workforce that's female. So quote. Even 245 00:13:25,840 --> 00:13:27,960 Speaker 1: if we imagine for a moment that the manifesto is 246 00:13:28,000 --> 00:13:31,400 Speaker 1: correct and that there is some biological ceiling on the 247 00:13:31,440 --> 00:13:33,560 Speaker 1: percentage of women who will be suited to work at 248 00:13:33,600 --> 00:13:37,760 Speaker 1: Google less than fifty percent of their workforce, isn't it 249 00:13:37,800 --> 00:13:41,559 Speaker 1: the case that Google and tech generally is almost certainly 250 00:13:41,960 --> 00:13:45,400 Speaker 1: not yet hitting that ceiling. In other words, it is 251 00:13:45,520 --> 00:13:49,080 Speaker 1: clear that we are still operating in an environment where 252 00:13:49,080 --> 00:13:51,520 Speaker 1: it is very much more likely that women who are 253 00:13:51,600 --> 00:13:55,720 Speaker 1: biologically able to work in tech are chased away from 254 00:13:55,720 --> 00:14:01,920 Speaker 1: tech by sociological and other factors, van that biologically unsuited 255 00:14:01,920 --> 00:14:06,239 Speaker 1: women are somehow brought in by over zealous diversity programs. 256 00:14:06,640 --> 00:14:08,440 Speaker 1: And something else I want to lift up here is 257 00:14:08,480 --> 00:14:11,120 Speaker 1: that as a woman of color, a common idea is 258 00:14:11,160 --> 00:14:13,120 Speaker 1: that to get in the same place as a white man, 259 00:14:13,240 --> 00:14:15,240 Speaker 1: you have to be twice as good. And I think 260 00:14:15,360 --> 00:14:18,199 Speaker 1: the women that he works with are probably better than 261 00:14:18,240 --> 00:14:20,080 Speaker 1: he is. They probably to get where they are, they 262 00:14:20,080 --> 00:14:22,360 Speaker 1: had to be twice as good all the time. And 263 00:14:22,400 --> 00:14:25,200 Speaker 1: so if he's looking around and just assuming, of course, 264 00:14:25,280 --> 00:14:27,360 Speaker 1: I'm better than all of these women and they are 265 00:14:27,400 --> 00:14:29,880 Speaker 1: just you know, they're their pity hires who you know, 266 00:14:29,880 --> 00:14:32,920 Speaker 1: worko ins or whatever. That is so wrong, because any 267 00:14:32,920 --> 00:14:35,400 Speaker 1: woman who works in a male dominated field knows to 268 00:14:35,400 --> 00:14:37,400 Speaker 1: be taken seriously, you've got to be twice as good 269 00:14:37,440 --> 00:14:39,360 Speaker 1: to go half as far right to be in that 270 00:14:39,480 --> 00:14:43,840 Speaker 1: one percent of people actually hires. My other huge beef 271 00:14:44,000 --> 00:14:47,280 Speaker 1: with his manifesto that Cynthia points out is that this 272 00:14:47,360 --> 00:14:52,720 Speaker 1: is not philosophy class. Okay, this is not an innocuous 273 00:14:52,800 --> 00:15:00,280 Speaker 1: just asking questions conversation over the theory behind nature versus 274 00:15:00,400 --> 00:15:03,400 Speaker 1: nurture when it comes to women and deck. There is 275 00:15:03,440 --> 00:15:11,040 Speaker 1: a very clear intent behind his very organized memo. Cynthia 276 00:15:11,080 --> 00:15:14,160 Speaker 1: goes on to write, quote, if his proposals were adopted, 277 00:15:14,640 --> 00:15:18,480 Speaker 1: it wouldn't be some abstract concept of average that doesn't 278 00:15:18,480 --> 00:15:22,040 Speaker 1: get a scholarship. It would be an actual individual woman. 279 00:15:22,480 --> 00:15:25,320 Speaker 1: It would be an actual female Googler who doesn't get 280 00:15:25,360 --> 00:15:28,160 Speaker 1: to attend the Grace Hopper conference, which provides many women 281 00:15:28,200 --> 00:15:30,520 Speaker 1: with their first experience of being in a majority woman 282 00:15:30,800 --> 00:15:34,040 Speaker 1: tech conference space. So she basically is making this point 283 00:15:34,080 --> 00:15:38,160 Speaker 1: that this is seemingly innocuous in how he wrote it, 284 00:15:38,200 --> 00:15:40,880 Speaker 1: and his tone was going for benign right he was 285 00:15:40,920 --> 00:15:44,400 Speaker 1: going for harmless. There is nothing harmless about the proposals 286 00:15:44,440 --> 00:15:50,080 Speaker 1: he was suggesting. He was literally suggesting taking away resources 287 00:15:50,120 --> 00:15:54,560 Speaker 1: from his female colleagues, opportunities from his female colleagues, because 288 00:15:54,560 --> 00:15:57,600 Speaker 1: he wasn't invited to those opportunities like the Grace Hopper 289 00:15:57,680 --> 00:16:00,320 Speaker 1: conference completely. And I think that you you really ailed it. 290 00:16:00,520 --> 00:16:03,360 Speaker 1: This is not some abstract person. These are real people 291 00:16:03,440 --> 00:16:07,320 Speaker 1: who would have real things, concrete opportunities taken away from them. 292 00:16:07,480 --> 00:16:10,160 Speaker 1: And I just think I can't even imagine writing a 293 00:16:10,200 --> 00:16:13,320 Speaker 1: memo at work where I say, oh, I don't think 294 00:16:13,400 --> 00:16:16,600 Speaker 1: Bobby in accounting should get x Y. We shouldn't be 295 00:16:16,600 --> 00:16:19,240 Speaker 1: taking x y Z away from Bobby, and then being like, WHOA, 296 00:16:19,240 --> 00:16:21,200 Speaker 1: why is Bobby so upset? It's like, yeah, because that's 297 00:16:21,240 --> 00:16:25,400 Speaker 1: screwed up exactly. So he's trying to not name names 298 00:16:25,720 --> 00:16:30,240 Speaker 1: and yet take resources away from his colleagues. I also 299 00:16:30,360 --> 00:16:32,880 Speaker 1: think it's worth noting I'm curious with people who were 300 00:16:32,920 --> 00:16:35,960 Speaker 1: big supporters of him know this how he deals with race. 301 00:16:36,240 --> 00:16:38,720 Speaker 1: So she writes, it's striking to me that the manifesto 302 00:16:38,760 --> 00:16:42,800 Speaker 1: author repeatedly lists race alongside gender when listing programs and 303 00:16:42,800 --> 00:16:46,000 Speaker 1: preferences he thinks should be done away with. But unlike gender, 304 00:16:46,200 --> 00:16:48,760 Speaker 1: he never purports to have any scientific backing for this. 305 00:16:49,120 --> 00:16:52,120 Speaker 1: This omission is telling and so basically saying, well, we 306 00:16:52,200 --> 00:16:56,360 Speaker 1: all know people of color are biologically worse at this, 307 00:16:56,680 --> 00:16:59,160 Speaker 1: and she says, well, defenders of the memo still be 308 00:16:59,240 --> 00:17:01,800 Speaker 1: comfortable at the author had casually summarized race and i 309 00:17:01,920 --> 00:17:05,080 Speaker 1: Q studies to argue that purported biological differences and not 310 00:17:05,160 --> 00:17:09,120 Speaker 1: discrimination or an equal access to education, explain Google's shortage 311 00:17:09,119 --> 00:17:13,320 Speaker 1: of African American programmers. Basically, he's just sort of casually 312 00:17:13,760 --> 00:17:17,080 Speaker 1: like everyone just knows that blacks aren't good programmers. They're 313 00:17:17,080 --> 00:17:19,600 Speaker 1: just biologically not good at this. That's basically the point 314 00:17:19,640 --> 00:17:22,040 Speaker 1: he's making for women. If he made that point, I mean, 315 00:17:22,119 --> 00:17:24,760 Speaker 1: it's baffling and it's so messed up, and I don't 316 00:17:24,800 --> 00:17:27,399 Speaker 1: see how people can't look at this and how this 317 00:17:27,520 --> 00:17:29,880 Speaker 1: holds up under that kind of scrutiny. But I will 318 00:17:29,880 --> 00:17:32,960 Speaker 1: say that again, in these in these pockets of the Internet, 319 00:17:33,200 --> 00:17:35,000 Speaker 1: that is not that is a theory that would fly. 320 00:17:35,320 --> 00:17:38,399 Speaker 1: People really do get very invested in this idea of 321 00:17:38,440 --> 00:17:42,639 Speaker 1: certain people being biologically good at things and other people 322 00:17:42,680 --> 00:17:45,560 Speaker 1: being biologically bad at that. Are you saying that that argument, 323 00:17:45,760 --> 00:17:49,160 Speaker 1: the racist one would fly. I think for a lot 324 00:17:49,200 --> 00:17:51,760 Speaker 1: of people, if presented with that, they would say, sure, 325 00:17:51,920 --> 00:17:54,439 Speaker 1: of course blacks can't understand geometry. That they would they 326 00:17:54,480 --> 00:17:56,760 Speaker 1: would really think up. And I think it's telling that 327 00:17:56,840 --> 00:17:58,439 Speaker 1: you don't have to go to corners of the internet 328 00:17:58,480 --> 00:18:01,280 Speaker 1: to find people who would agree of that conclusion on 329 00:18:01,359 --> 00:18:06,080 Speaker 1: gender correct because it's definitely less even plausible to say 330 00:18:06,080 --> 00:18:08,600 Speaker 1: that kind of an argument out loud about race. But 331 00:18:08,720 --> 00:18:10,879 Speaker 1: when you saying about women, is like, yeah, this this 332 00:18:10,920 --> 00:18:14,359 Speaker 1: guy seems like if you made that argument about race, 333 00:18:14,560 --> 00:18:17,840 Speaker 1: people would be like, well, saying that blacks are innately worse, 334 00:18:17,920 --> 00:18:20,280 Speaker 1: that computer science is racist and messed up, right, But 335 00:18:20,359 --> 00:18:22,480 Speaker 1: you can kind of make that argument about women, and 336 00:18:22,560 --> 00:18:27,440 Speaker 1: it's kind of fine. Bizarre, it's not bizarre. It's infuriating. Yes, 337 00:18:27,520 --> 00:18:30,119 Speaker 1: it's I'm like livid right now just thinking about the 338 00:18:30,200 --> 00:18:32,879 Speaker 1: handstry and the facial expressions that are happening in the 339 00:18:32,920 --> 00:18:35,320 Speaker 1: studio right now. I wish everyone could see them. But 340 00:18:35,400 --> 00:18:38,199 Speaker 1: here's something to set us off here for a second. 341 00:18:38,400 --> 00:18:43,919 Speaker 1: Her conclusion in this piece, Cynthia Ly's piece is really perfect, 342 00:18:44,000 --> 00:18:47,159 Speaker 1: in my opinion, she writes in the end, focusing the 343 00:18:47,200 --> 00:18:51,119 Speaker 1: conversation on the minutia of the scientific claims in the 344 00:18:51,160 --> 00:18:56,560 Speaker 1: manifesto is a red herring. Regardless of whether biological differences exist, 345 00:18:56,680 --> 00:19:00,800 Speaker 1: there's no shortage of glaring evidence in individual stories and 346 00:19:00,960 --> 00:19:04,600 Speaker 1: in scientific studies that women in tech experienced bias and 347 00:19:04,640 --> 00:19:09,960 Speaker 1: a general lack of a welcoming environment, as do underrepresented minorities. 348 00:19:10,080 --> 00:19:12,879 Speaker 1: Until these problems are resolved, our focus should be on 349 00:19:13,000 --> 00:19:17,119 Speaker 1: remedying that injustice. After that work is complete, we can 350 00:19:17,160 --> 00:19:21,480 Speaker 1: reassess whether small, effect sized biological components have anything to 351 00:19:21,520 --> 00:19:24,880 Speaker 1: do with lingering imbalances. And that's that's why I love 352 00:19:25,000 --> 00:19:28,440 Speaker 1: her response so much. It's because I've no matter what 353 00:19:28,520 --> 00:19:31,760 Speaker 1: this guy says. Obviously, the data is very very clear, 354 00:19:32,000 --> 00:19:35,359 Speaker 1: both in personal stories and in hard numbers, there's a 355 00:19:35,400 --> 00:19:38,800 Speaker 1: problem with women and minorities in tech. And to say 356 00:19:38,840 --> 00:19:41,480 Speaker 1: that there isn't. It's just it's just turning a blind 357 00:19:41,520 --> 00:19:43,919 Speaker 1: eye to this data. It's just it's not being fact based, 358 00:19:44,119 --> 00:19:46,960 Speaker 1: as he and even and he's doing that while pretending 359 00:19:46,960 --> 00:19:50,520 Speaker 1: to be super a fact base. No, I just I know, 360 00:19:50,600 --> 00:19:52,440 Speaker 1: I know, I know that I don't know this guy, 361 00:19:52,480 --> 00:19:54,400 Speaker 1: but I know this guy. You know what you mean. 362 00:19:54,560 --> 00:19:56,879 Speaker 1: And I'm raising my fist at the air like I 363 00:19:56,960 --> 00:19:59,639 Speaker 1: wanna pull an Abe Simpson and yell at a cloud 364 00:19:59,680 --> 00:20:06,199 Speaker 1: about old. Okay, so we're gonna take a quick break, 365 00:20:06,280 --> 00:20:08,360 Speaker 1: but we I just want to hammer home be where 366 00:20:08,400 --> 00:20:12,600 Speaker 1: we do that this is the world's most profitable industry. 367 00:20:12,640 --> 00:20:16,000 Speaker 1: So for all their proclaimed data driven nous, an argument 368 00:20:16,080 --> 00:20:18,800 Speaker 1: like the one that Google guy made doesn't seem to 369 00:20:18,840 --> 00:20:23,400 Speaker 1: give a hoot that better decisions are made with more 370 00:20:23,480 --> 00:20:27,600 Speaker 1: diverse teams, that companies outcomes and bottom line results tend 371 00:20:27,640 --> 00:20:30,359 Speaker 1: to improve with more diverse teams. And this is an 372 00:20:30,440 --> 00:20:36,600 Speaker 1: argument that even Cheryl Sandberg, the whitest, wealthiest voice in feminism, 373 00:20:36,680 --> 00:20:41,159 Speaker 1: really made to her fellow Silicon Valley coworkers, which is 374 00:20:41,280 --> 00:20:46,160 Speaker 1: it's just good business to have gender inclusion and diversity 375 00:20:46,200 --> 00:20:52,080 Speaker 1: on your docket for corporate priorities. So in this industry, 376 00:20:52,160 --> 00:20:54,680 Speaker 1: especially the one that's shaping the future we are all 377 00:20:54,720 --> 00:20:59,120 Speaker 1: going to live in, it's critically important that we actually 378 00:20:59,119 --> 00:21:02,439 Speaker 1: rectify these and justices and focus on what can be 379 00:21:02,480 --> 00:21:05,119 Speaker 1: done on the cultural level. And I think we should 380 00:21:05,119 --> 00:21:07,960 Speaker 1: get into why this is such a big deal after 381 00:21:08,000 --> 00:21:18,800 Speaker 1: this quick break and we're back and we're talking about 382 00:21:18,920 --> 00:21:21,879 Speaker 1: why it's so critically critically important that tech get its 383 00:21:21,920 --> 00:21:24,560 Speaker 1: act together. Now, we know that tech is the most 384 00:21:24,560 --> 00:21:27,400 Speaker 1: profitable industry out there, but it's also the industry that's 385 00:21:27,440 --> 00:21:31,120 Speaker 1: poised to solve some of our biggest, most critical life challenges. 386 00:21:31,400 --> 00:21:33,680 Speaker 1: This is an industry that if we if they really 387 00:21:33,680 --> 00:21:36,320 Speaker 1: put their mind to it, they could possibly really get somewhere. 388 00:21:36,320 --> 00:21:39,480 Speaker 1: When you think about climate change, immigration, all of these 389 00:21:39,480 --> 00:21:43,400 Speaker 1: things that are critically important life or death things tech 390 00:21:43,400 --> 00:21:45,480 Speaker 1: could probably solve if they put their mind to it. 391 00:21:45,880 --> 00:21:48,840 Speaker 1: But if they don't figure out that what makes for 392 00:21:49,160 --> 00:21:53,280 Speaker 1: good tech solutions are inclusive teams and inclusive hiring practices, 393 00:21:53,520 --> 00:21:55,879 Speaker 1: they'll never get there. And I think what really frustrates 394 00:21:55,920 --> 00:21:58,119 Speaker 1: me is that they don't see the way this is 395 00:21:58,160 --> 00:22:01,160 Speaker 1: costing them money and costing them at efficiency. The data 396 00:22:01,240 --> 00:22:04,040 Speaker 1: is so clear that when you have inclusive teams and 397 00:22:04,080 --> 00:22:07,000 Speaker 1: diverse teams, you're better at your job, And it almost 398 00:22:07,000 --> 00:22:11,040 Speaker 1: feels like they are prioritizing being a browy frat house 399 00:22:11,240 --> 00:22:16,520 Speaker 1: over making actual, effective, cost driven choices. I completely agree, 400 00:22:16,720 --> 00:22:20,240 Speaker 1: and I am still an unbridled optimist on this, even 401 00:22:20,240 --> 00:22:24,320 Speaker 1: though all the data and these stories are depressing as hell. 402 00:22:25,080 --> 00:22:28,240 Speaker 1: The reason I'm optimistic is because when we say they 403 00:22:28,280 --> 00:22:33,919 Speaker 1: don't seem to care that minority in tech is being 404 00:22:33,960 --> 00:22:37,879 Speaker 1: fired like Google, guy, that I mean not all of them, right, 405 00:22:37,920 --> 00:22:43,360 Speaker 1: But there are majorly well funded efforts to rectify these injustices, 406 00:22:43,960 --> 00:22:47,160 Speaker 1: and I want to dive into some of the imperfect 407 00:22:47,280 --> 00:22:50,000 Speaker 1: solutions that are being worked on. But the good news 408 00:22:50,080 --> 00:22:52,119 Speaker 1: is that there are solutions being put forth. There's a 409 00:22:52,119 --> 00:22:56,359 Speaker 1: lot of money being invested in increasing diversity and inclusion 410 00:22:56,600 --> 00:23:00,879 Speaker 1: at companies like Google. Um, So let's let's talk a 411 00:23:00,920 --> 00:23:04,120 Speaker 1: little bit about how, like, what are the multi phase 412 00:23:05,240 --> 00:23:10,520 Speaker 1: interventions that tech companies can make to really close the 413 00:23:10,560 --> 00:23:13,399 Speaker 1: gap when it comes to women in tech. Well, I 414 00:23:13,400 --> 00:23:16,480 Speaker 1: think the first one is probably filling the STEM pipeline. Um. 415 00:23:16,520 --> 00:23:18,760 Speaker 1: From this Atlantic article that you keep quoting from because 416 00:23:18,760 --> 00:23:20,879 Speaker 1: it's amazing and you should definitely read it. More than 417 00:23:20,960 --> 00:23:23,240 Speaker 1: half of college and university students are women, and the 418 00:23:23,240 --> 00:23:26,439 Speaker 1: percentage of women entering many STEM fields has risen. Computer 419 00:23:26,520 --> 00:23:29,720 Speaker 1: science is a glaring exception. The percentage of female computer 420 00:23:29,800 --> 00:23:33,120 Speaker 1: at information science majors peaked in nineteen four at about 421 00:23:34,119 --> 00:23:37,160 Speaker 1: It is declined more or less steadily ever since. Today 422 00:23:37,160 --> 00:23:40,639 Speaker 1: it stands at eight, which is shocking because computer science 423 00:23:40,680 --> 00:23:46,400 Speaker 1: was way less sexy. A you know what, what's what's 424 00:23:46,400 --> 00:23:50,359 Speaker 1: the opposite of sexy boxy boxy? Also, I'm thinking about 425 00:23:50,400 --> 00:23:53,520 Speaker 1: the design of computers were similarly right, So like now 426 00:23:53,640 --> 00:23:55,840 Speaker 1: tech is so sexy, like you can solve the biggest 427 00:23:55,840 --> 00:24:00,280 Speaker 1: world's problems. It's such a financially attractive industry if you 428 00:24:00,359 --> 00:24:02,880 Speaker 1: want to not only have social impact but also get 429 00:24:03,160 --> 00:24:06,360 Speaker 1: rich along the way. And somehow that's attracted more men 430 00:24:07,119 --> 00:24:09,359 Speaker 1: to the table than women. I think that's very telling. 431 00:24:09,760 --> 00:24:11,720 Speaker 1: But I also think it's interesting when you add race 432 00:24:11,800 --> 00:24:13,879 Speaker 1: to that dynamic. As a black woman who worked for 433 00:24:13,920 --> 00:24:16,439 Speaker 1: a while on a tech sector, I think adding that 434 00:24:16,560 --> 00:24:19,879 Speaker 1: extra layer of intersectionality is so critically important. So I 435 00:24:19,880 --> 00:24:22,080 Speaker 1: don't want to say that you know, when black women 436 00:24:22,080 --> 00:24:24,680 Speaker 1: and women of color are not there, We are there, 437 00:24:24,920 --> 00:24:26,840 Speaker 1: but I think so much more can be done to 438 00:24:26,920 --> 00:24:29,240 Speaker 1: sort of bring us into the pipeline. You really have 439 00:24:29,320 --> 00:24:30,720 Speaker 1: to see it to be it. So I think what 440 00:24:30,840 --> 00:24:33,639 Speaker 1: it really comes down to is creating these inclusive and 441 00:24:33,680 --> 00:24:37,720 Speaker 1: diverse workplace cultures in ways that are baked into the framework, 442 00:24:37,760 --> 00:24:39,879 Speaker 1: not just sort of tacked in after the fact um. 443 00:24:39,880 --> 00:24:41,720 Speaker 1: When I was working into Lacon Valley, I worked for 444 00:24:41,760 --> 00:24:44,359 Speaker 1: an organization called Medium Shout Out to Medium, which I 445 00:24:44,359 --> 00:24:47,160 Speaker 1: still know and love and used to this day. UM. 446 00:24:47,160 --> 00:24:50,840 Speaker 1: But something I was really struck by with my employment 447 00:24:50,840 --> 00:24:54,879 Speaker 1: there was how good HR practices were sort of baked 448 00:24:54,880 --> 00:24:57,240 Speaker 1: into the framework very early on. This didn't feel like 449 00:24:57,320 --> 00:24:59,800 Speaker 1: something that you just got a you know, five minute 450 00:24:59,840 --> 00:25:02,200 Speaker 1: let sure about after you've been hired. It was clear 451 00:25:02,200 --> 00:25:04,919 Speaker 1: that they had made really really critical steps to have 452 00:25:05,080 --> 00:25:08,080 Speaker 1: that be part of what made the organization the organization. 453 00:25:08,480 --> 00:25:10,600 Speaker 1: And one of the things I really liked about Medium 454 00:25:10,600 --> 00:25:13,400 Speaker 1: was that even when they were hiring for hard tech 455 00:25:13,520 --> 00:25:17,200 Speaker 1: jobs like engineer or you know, UM coders and things 456 00:25:17,240 --> 00:25:20,960 Speaker 1: like that, they would have really inclusive and diverse hiring 457 00:25:21,000 --> 00:25:23,840 Speaker 1: teams who were interviewing people. So even though you know, 458 00:25:23,960 --> 00:25:26,040 Speaker 1: I don't have a hard hard tech background, UM, I 459 00:25:26,080 --> 00:25:28,720 Speaker 1: don't have a computer science degree, I would sometimes be 460 00:25:28,920 --> 00:25:31,160 Speaker 1: on a team that was interviewing someone for a hard 461 00:25:31,200 --> 00:25:33,880 Speaker 1: tech job, and so, you know, I don't know anything 462 00:25:33,920 --> 00:25:37,480 Speaker 1: about software engineering, but I know what makes someone a 463 00:25:37,480 --> 00:25:40,359 Speaker 1: good team player. I know what makes someone UM a 464 00:25:40,359 --> 00:25:43,120 Speaker 1: good person to have on a team. And I felt 465 00:25:43,160 --> 00:25:46,600 Speaker 1: like my input as a non hard skills person, as 466 00:25:46,600 --> 00:25:49,320 Speaker 1: a woman of color, as someone who really advocates for 467 00:25:49,400 --> 00:25:53,320 Speaker 1: soft skills, that was taken just as seriously as the 468 00:25:53,359 --> 00:25:56,399 Speaker 1: head software senior engineer who was sort of saying like, oh, 469 00:25:56,440 --> 00:25:58,040 Speaker 1: this guy can code this way or that way. And 470 00:25:58,040 --> 00:25:59,800 Speaker 1: I think that was really a good way of making 471 00:25:59,800 --> 00:26:03,639 Speaker 1: sure that we had people who just knew how to 472 00:26:03,720 --> 00:26:06,000 Speaker 1: not be jerks. That that was an important part of 473 00:26:06,040 --> 00:26:08,679 Speaker 1: how we hired and I really I think that that 474 00:26:08,760 --> 00:26:10,640 Speaker 1: really made a difference in terms of the climate at 475 00:26:10,680 --> 00:26:13,359 Speaker 1: Medium that whereas other tech companies are should have known 476 00:26:13,480 --> 00:26:18,159 Speaker 1: for these hiring these like special snowflake hard skills, folks 477 00:26:18,240 --> 00:26:21,600 Speaker 1: who you know, can be awful to their coworkers, can 478 00:26:21,600 --> 00:26:23,960 Speaker 1: be awful to women, and people of color can make 479 00:26:24,240 --> 00:26:27,800 Speaker 1: workspaces really tough to show up in. We we did 480 00:26:27,840 --> 00:26:31,640 Speaker 1: them out. I'll never forget one of our engineers. He 481 00:26:31,760 --> 00:26:34,119 Speaker 1: didn't hire someone who was, you know, technically and on 482 00:26:34,160 --> 00:26:37,200 Speaker 1: paper very very good. And I think I was like, oh, 483 00:26:37,200 --> 00:26:38,919 Speaker 1: why this guy? You know, it seems like he had 484 00:26:38,960 --> 00:26:41,040 Speaker 1: all the all the tech skills that you were looking for. 485 00:26:41,440 --> 00:26:43,600 Speaker 1: And he said, well, one of the questions I asked 486 00:26:43,640 --> 00:26:46,600 Speaker 1: him was, you know, why are you leaving your last 487 00:26:46,640 --> 00:26:50,040 Speaker 1: job and he said, because them it was two team focused. 488 00:26:50,040 --> 00:26:51,960 Speaker 1: I wasn't getting enough of the credit. And he said 489 00:26:51,960 --> 00:26:54,000 Speaker 1: that was a huge red flag for me that he 490 00:26:54,040 --> 00:26:56,760 Speaker 1: saw himself as it should be about me and how 491 00:26:56,840 --> 00:26:58,800 Speaker 1: great I am, because I am so great, and that 492 00:26:58,880 --> 00:27:01,600 Speaker 1: he didn't understand all the different aspects of a team 493 00:27:01,640 --> 00:27:03,879 Speaker 1: that go into be making your team effective. And he 494 00:27:03,960 --> 00:27:08,360 Speaker 1: was like, no dice, no dice. That's amazing, definitely. I mean, 495 00:27:08,560 --> 00:27:11,439 Speaker 1: it's also so clear to me that one of the 496 00:27:11,440 --> 00:27:13,800 Speaker 1: other big takeaways that I think other companies can gain 497 00:27:13,840 --> 00:27:16,479 Speaker 1: from this is that it sounds to me like Medium 498 00:27:16,520 --> 00:27:20,000 Speaker 1: had a very clear set of values, so they brought 499 00:27:20,080 --> 00:27:22,520 Speaker 1: into the hiring process. It was clear what we value 500 00:27:22,600 --> 00:27:27,199 Speaker 1: as a company. And you'll note it wasn't pure merit 501 00:27:28,080 --> 00:27:30,040 Speaker 1: because that guy would have gotten through it was on 502 00:27:30,160 --> 00:27:33,840 Speaker 1: merit alone. So you're you're basically communicating what it looks 503 00:27:33,880 --> 00:27:36,640 Speaker 1: like to have a hiring process that not only reflects 504 00:27:36,640 --> 00:27:39,680 Speaker 1: the diversity of your company, which is beneficial for the 505 00:27:39,680 --> 00:27:42,959 Speaker 1: person on the other side of the interview, but also 506 00:27:43,280 --> 00:27:46,560 Speaker 1: integrates your core values as a company. Is equally important 507 00:27:46,800 --> 00:27:49,240 Speaker 1: with the hard skills that are hiring for exactly, and 508 00:27:49,280 --> 00:27:51,280 Speaker 1: I think when you look at the data, more and 509 00:27:51,320 --> 00:27:54,560 Speaker 1: more hiring managers want these kinds of soft skills. You 510 00:27:54,600 --> 00:27:56,600 Speaker 1: could be the best, you know, have the best hard 511 00:27:56,600 --> 00:27:58,359 Speaker 1: skills ever. But if you can't work on a team, 512 00:27:58,400 --> 00:28:00,720 Speaker 1: if you can't have a conversation through co worker without 513 00:28:00,760 --> 00:28:03,959 Speaker 1: insulting them or being a hostile, and you know, adding 514 00:28:03,960 --> 00:28:06,119 Speaker 1: to a hostile work environment, you should you have no 515 00:28:06,200 --> 00:28:08,600 Speaker 1: business working some places like those skills are important too, 516 00:28:09,200 --> 00:28:12,280 Speaker 1: so and it's beyond a meritocracy, beyond I like that. 517 00:28:12,720 --> 00:28:14,600 Speaker 1: Another company that I would say is doing this right 518 00:28:14,680 --> 00:28:18,639 Speaker 1: is Pinterest, which is adopted an anti bias checklist that 519 00:28:18,720 --> 00:28:22,280 Speaker 1: they use in hiring, and that checklist basically helps those 520 00:28:22,320 --> 00:28:26,399 Speaker 1: who are making hiring decisions prime against unconscious bias. It 521 00:28:26,480 --> 00:28:29,800 Speaker 1: illuminates some of the ways in which unconscious bias can 522 00:28:29,880 --> 00:28:34,800 Speaker 1: impede those decisions and helps counteract it systematically from the 523 00:28:34,880 --> 00:28:38,200 Speaker 1: hiring process. And what I like so much about that 524 00:28:38,320 --> 00:28:40,280 Speaker 1: is that it under it kind of shows that they 525 00:28:40,360 --> 00:28:42,480 Speaker 1: understand that these things can be sort of innate and 526 00:28:42,520 --> 00:28:45,080 Speaker 1: it takes a little bit of unlearning to work past them. 527 00:28:45,080 --> 00:28:48,520 Speaker 1: Because I know myself, if I was a hiring manager 528 00:28:48,520 --> 00:28:51,480 Speaker 1: for a project and a woman who walked in who 529 00:28:51,560 --> 00:28:54,400 Speaker 1: was a black woman with natural hair who went to Howard, 530 00:28:54,720 --> 00:28:57,400 Speaker 1: I automatically be like, oh, she's like me. I would 531 00:28:57,440 --> 00:28:59,760 Speaker 1: have to fight through the bias because we all like 532 00:29:00,000 --> 00:29:02,640 Speaker 1: people who are like us, and so I like that 533 00:29:02,680 --> 00:29:06,800 Speaker 1: this that that that technique, it accounts for the fact 534 00:29:06,800 --> 00:29:09,280 Speaker 1: that this is a mate, and then it takes working 535 00:29:09,360 --> 00:29:11,960 Speaker 1: through that to counteract it. It It says, no, you have 536 00:29:12,120 --> 00:29:16,880 Speaker 1: to take a step back and really analyze candidates along 537 00:29:16,920 --> 00:29:19,000 Speaker 1: the lines of who they are, not just who you are, 538 00:29:19,120 --> 00:29:22,000 Speaker 1: and what you want to see exactly exactly, and it 539 00:29:22,360 --> 00:29:25,240 Speaker 1: it designs against it, which I think is as full 540 00:29:25,280 --> 00:29:27,960 Speaker 1: proof and as full provous we can be as fallible 541 00:29:28,000 --> 00:29:30,320 Speaker 1: human beings. One other company I want to shout out 542 00:29:30,320 --> 00:29:32,080 Speaker 1: to you because they've done this in a really creative 543 00:29:32,600 --> 00:29:37,040 Speaker 1: and somewhat controversial ways. Actually Intel, Intel, which you know 544 00:29:37,520 --> 00:29:41,920 Speaker 1: in tech terms, is ancient, right, Intel, like an ancient 545 00:29:42,120 --> 00:29:45,959 Speaker 1: been around for a long long time. Um. They've actually 546 00:29:45,960 --> 00:29:49,320 Speaker 1: been releasing their diversity numbers since two thousands, so like 547 00:29:49,360 --> 00:29:52,000 Speaker 1: almost a decade earlier than Google and a lot of 548 00:29:52,000 --> 00:29:53,960 Speaker 1: the others had to be pressured into it. What was 549 00:29:54,120 --> 00:29:57,880 Speaker 1: interesting is that they have implemented hiring goals along diversity 550 00:29:57,960 --> 00:30:01,200 Speaker 1: lines that are very public. To be entire company knows 551 00:30:01,240 --> 00:30:05,200 Speaker 1: that we value diversity in hiring. That means we are 552 00:30:05,240 --> 00:30:07,320 Speaker 1: going to put our money where our mouths are on 553 00:30:07,360 --> 00:30:13,640 Speaker 1: this issue and incentivize financially having other members of the 554 00:30:13,640 --> 00:30:18,640 Speaker 1: Intel team recruit diverse hires. Now you might be thinking 555 00:30:19,000 --> 00:30:21,480 Speaker 1: that the word quota is coming to mind. Here in 556 00:30:21,480 --> 00:30:25,240 Speaker 1: the United States, we can't talk about hiring quotas, which 557 00:30:25,280 --> 00:30:28,560 Speaker 1: here in the United States are illegal, although they're widely 558 00:30:28,600 --> 00:30:32,280 Speaker 1: practiced in some European countries like Norway that you know 559 00:30:32,320 --> 00:30:35,680 Speaker 1: has a real actual quota, like say, you know of 560 00:30:35,720 --> 00:30:38,680 Speaker 1: a public company's board must be female, and that's worked 561 00:30:38,760 --> 00:30:41,040 Speaker 1: quite well in Norway. But here in the US, they 562 00:30:41,080 --> 00:30:45,480 Speaker 1: have hiring goals that Intel that quote to make its 563 00:30:45,480 --> 00:30:49,280 Speaker 1: goals a little more well quote alike, Intel introduced money 564 00:30:49,320 --> 00:30:53,080 Speaker 1: into the equation in Intel's annual performance bonus plan. Success 565 00:30:53,080 --> 00:30:56,520 Speaker 1: in meeting diversity goals factors into whether the company gives 566 00:30:56,560 --> 00:31:01,720 Speaker 1: employees and across the board bonus, so the amounts differ. 567 00:31:01,840 --> 00:31:05,960 Speaker 1: But basically, if Intel as a whole meets its diversity 568 00:31:06,000 --> 00:31:10,160 Speaker 1: goals in hiring, everyone gets paid. I love that so 569 00:31:10,160 --> 00:31:12,959 Speaker 1: so much, and I actually think that incentivizing it is 570 00:31:13,040 --> 00:31:15,800 Speaker 1: great because it's possible to think that you're making a 571 00:31:15,840 --> 00:31:18,840 Speaker 1: good faith effort to find candidate pools by just posting 572 00:31:18,840 --> 00:31:21,200 Speaker 1: on a few list serves. But our networks tend to 573 00:31:21,240 --> 00:31:23,240 Speaker 1: look like us, like my network looks like me. And 574 00:31:23,240 --> 00:31:25,480 Speaker 1: so if you have a job opening and you just 575 00:31:25,520 --> 00:31:27,640 Speaker 1: post it to your network, I'm sorry, You're gonna get 576 00:31:27,760 --> 00:31:29,520 Speaker 1: people who are like you. And if they posted to 577 00:31:29,520 --> 00:31:31,080 Speaker 1: their network, is gonna be people that are like them. 578 00:31:31,080 --> 00:31:33,880 Speaker 1: If they're like you, it's just recreating this problem. But 579 00:31:33,920 --> 00:31:37,920 Speaker 1: if you incentivize it financially, that incentivizes people getting creative 580 00:31:38,000 --> 00:31:40,480 Speaker 1: and going outside their networks in a big way because 581 00:31:40,480 --> 00:31:43,200 Speaker 1: they want that financial incentive, which I think is great. Now, 582 00:31:43,240 --> 00:31:46,719 Speaker 1: we could talk literally for I don't know, five different 583 00:31:46,760 --> 00:31:51,840 Speaker 1: episodes worth of ways that your company can mitigate unconscious 584 00:31:51,840 --> 00:31:55,240 Speaker 1: bias can create cultures of inclusion, but that would be 585 00:31:55,280 --> 00:31:58,120 Speaker 1: an hr podcast. It would be a very different, much 586 00:31:58,200 --> 00:32:03,960 Speaker 1: longer conversation. Fortunately, Ellen Pow of All People teamed up 587 00:32:04,000 --> 00:32:07,200 Speaker 1: with a bunch of other senior women in tech to 588 00:32:07,360 --> 00:32:12,840 Speaker 1: provide resources, very detailed resources for how companies in tech, 589 00:32:12,880 --> 00:32:18,800 Speaker 1: and I would argue across industries can implement diverse, inclusive 590 00:32:19,200 --> 00:32:23,000 Speaker 1: team cultures. You can find all of that info for 591 00:32:23,080 --> 00:32:27,200 Speaker 1: free at project include dot org, where at the very 592 00:32:27,240 --> 00:32:29,800 Speaker 1: top of their website they start the whole conversation off 593 00:32:29,840 --> 00:32:33,480 Speaker 1: with this value statement. They say, quote, true diversity means 594 00:32:33,520 --> 00:32:37,880 Speaker 1: better teams, better financial returns, better companies, and a better, 595 00:32:38,000 --> 00:32:42,280 Speaker 1: more innovative world. Projects include is our community for accelerating 596 00:32:42,320 --> 00:32:46,240 Speaker 1: meaningful and during diversity and inclusion in the tech industry. 597 00:32:46,320 --> 00:32:51,920 Speaker 1: And they have so many resources on everything from hiring, onboarding, compensation, 598 00:32:52,520 --> 00:32:58,600 Speaker 1: providing feedback, training, training managers, leading as venture capitalists, measuring progress. 599 00:32:58,680 --> 00:33:03,160 Speaker 1: I mean, the resources there are robust and we would 600 00:33:03,240 --> 00:33:05,040 Speaker 1: love to talk through every detail of them, but we 601 00:33:05,040 --> 00:33:07,560 Speaker 1: don't have the time. The last thing I want to 602 00:33:07,640 --> 00:33:11,640 Speaker 1: update folks on for this section on progress that's being 603 00:33:11,680 --> 00:33:15,400 Speaker 1: made is actually some progress that not only can be 604 00:33:15,440 --> 00:33:18,480 Speaker 1: made on the corporate front, but on the legal front. 605 00:33:18,800 --> 00:33:21,920 Speaker 1: So until recently, the government has been largely absent, and 606 00:33:21,960 --> 00:33:25,000 Speaker 1: how these companies deal with their employees. Um. I feel 607 00:33:25,000 --> 00:33:26,480 Speaker 1: like it's a theme of how we deal with things 608 00:33:26,480 --> 00:33:28,680 Speaker 1: on this show that we always say, well, there can 609 00:33:28,760 --> 00:33:31,200 Speaker 1: be a government or a policy solution to that, and 610 00:33:31,280 --> 00:33:34,920 Speaker 1: luckily we might be getting somewhere on that front. Earlier 611 00:33:34,960 --> 00:33:38,480 Speaker 1: in August, California State Senator Hannah Beth Jackson introduced a 612 00:33:38,520 --> 00:33:41,040 Speaker 1: bill that but a men the California Civil Code to 613 00:33:41,080 --> 00:33:44,600 Speaker 1: tackle an ongoing pattern of sexist conduct into the Coon Valley, 614 00:33:44,680 --> 00:33:48,640 Speaker 1: specifically sexual harassment in the venture capitalist and entrepreneur context. 615 00:33:48,920 --> 00:33:52,040 Speaker 1: So I just love that. You know, even though these 616 00:33:52,040 --> 00:33:54,920 Speaker 1: companies have been taking steps on their own policy, good 617 00:33:54,960 --> 00:33:58,360 Speaker 1: policy choices can help sort of buttress what they're doing 618 00:33:58,400 --> 00:34:00,760 Speaker 1: and make this a more could cool part of how 619 00:34:00,800 --> 00:34:04,600 Speaker 1: they're addressing this problem exactly. So what this law, this 620 00:34:04,720 --> 00:34:09,319 Speaker 1: proposed law, will really do is strengthen the already existing 621 00:34:09,560 --> 00:34:13,439 Speaker 1: civil code, California's Civil Code fifty one point nine, which 622 00:34:13,440 --> 00:34:16,880 Speaker 1: imposes liability for sexual harassment when there is a quote, 623 00:34:16,960 --> 00:34:22,000 Speaker 1: business service, or professional relationship between the plaintiff and the defendant. Now, 624 00:34:22,120 --> 00:34:24,400 Speaker 1: that language has been a little bit vague because it 625 00:34:24,480 --> 00:34:30,960 Speaker 1: doesn't explicitly include venture capitalists into that equation. So in 626 00:34:31,040 --> 00:34:36,719 Speaker 1: certain situations, the law being unclear, there could be problematic. Um, 627 00:34:36,840 --> 00:34:40,680 Speaker 1: what they would actually do is add relationships to that 628 00:34:40,760 --> 00:34:44,360 Speaker 1: list of who's liable in a sexual harassment situation according 629 00:34:44,360 --> 00:34:48,640 Speaker 1: to the Civil Code. Now, in addition to such relationships 630 00:34:48,680 --> 00:34:53,279 Speaker 1: like teachers, landlords, attorneys, and physicians, the new proposed legislation 631 00:34:53,440 --> 00:34:56,480 Speaker 1: adds venture capitalists to that list. And I think that's 632 00:34:56,480 --> 00:35:00,000 Speaker 1: so important because we've acknowledge the ways that power dynamic 633 00:35:00,160 --> 00:35:02,600 Speaker 1: can really impact these relationships when they get toxic and 634 00:35:03,080 --> 00:35:06,359 Speaker 1: you know, sex and sexual harassment is involved, it's fine. 635 00:35:06,400 --> 00:35:07,920 Speaker 1: I feel like this is a case where the law 636 00:35:08,000 --> 00:35:10,319 Speaker 1: is finally catching up with what is actually happening and 637 00:35:10,320 --> 00:35:13,239 Speaker 1: recognizing the unequal power dynamics that are often at play 638 00:35:13,280 --> 00:35:16,799 Speaker 1: here exactly. And it's part of the other ways that 639 00:35:16,800 --> 00:35:21,760 Speaker 1: that we need stronger legal protections for people like Ellen 640 00:35:21,800 --> 00:35:24,759 Speaker 1: Pow who whose case got thrown out because she wasn't 641 00:35:24,800 --> 00:35:29,640 Speaker 1: able to legally prove sexual discrimination and harassment despite everything 642 00:35:29,680 --> 00:35:32,080 Speaker 1: we've learned about her story in this episode and really 643 00:35:32,120 --> 00:35:35,799 Speaker 1: the last episode. There's also some ways that on the 644 00:35:35,840 --> 00:35:39,560 Speaker 1: federal level, agencies like the Equal Employment Opportunity Commission or 645 00:35:39,600 --> 00:35:42,399 Speaker 1: the National Labor Relations Board, which is my favorite new 646 00:35:42,480 --> 00:35:48,239 Speaker 1: thing to talk about, yeah, as a new vehicle for organizing, 647 00:35:48,680 --> 00:35:52,040 Speaker 1: and even the Securities and Exchange Commission have already began 648 00:35:52,120 --> 00:35:58,040 Speaker 1: placing greater scrutiny on these non disparagement agreements that oftentimes 649 00:35:58,200 --> 00:36:01,480 Speaker 1: when employees are being hired in tech sector are required 650 00:36:01,520 --> 00:36:05,320 Speaker 1: to sign those non disparagement agreements forbid them from whistle 651 00:36:05,360 --> 00:36:08,400 Speaker 1: blowing like Susan Fowler did, who, by the way, tweeted 652 00:36:08,440 --> 00:36:12,520 Speaker 1: recently that she's still in debt due to the fact 653 00:36:12,560 --> 00:36:15,240 Speaker 1: that she came out and publicly to make these statements. 654 00:36:15,239 --> 00:36:20,239 Speaker 1: So she's experienced a net loss for being a whistleblower. 655 00:36:20,280 --> 00:36:24,000 Speaker 1: So let's not pretend like the bravery and courage that 656 00:36:24,040 --> 00:36:27,400 Speaker 1: comes of being Ellen Pow or Susan Fowler and daring 657 00:36:27,440 --> 00:36:30,000 Speaker 1: to speak out and go public with your story doesn't 658 00:36:30,000 --> 00:36:34,680 Speaker 1: come with a pretty hefty price tag legally, professionally and personally. 659 00:36:34,960 --> 00:36:37,160 Speaker 1: And I mean, just to just to lift that up. 660 00:36:37,400 --> 00:36:40,240 Speaker 1: And it's so sad and such a shame that women 661 00:36:40,320 --> 00:36:42,840 Speaker 1: have to make that kind of a tough choice that 662 00:36:42,920 --> 00:36:46,080 Speaker 1: they have to decide between financial stability and keeping their 663 00:36:46,160 --> 00:36:49,000 Speaker 1: job and keeping their event paid and speaking out about 664 00:36:49,040 --> 00:36:52,239 Speaker 1: something that's really toxic that's happening to them at work exactly. So, 665 00:36:52,320 --> 00:36:58,400 Speaker 1: these arbitration clauses in employment agreements and non disparagement agreements 666 00:36:58,400 --> 00:37:02,560 Speaker 1: that are pretty common in hiring in the tech sector 667 00:37:03,160 --> 00:37:06,239 Speaker 1: make it really hard for employees who are experiencing discrimination 668 00:37:06,560 --> 00:37:10,080 Speaker 1: and even harassment to do anything other than settle quietly 669 00:37:10,120 --> 00:37:13,919 Speaker 1: out of court or like have a whisper campaign where 670 00:37:13,920 --> 00:37:16,200 Speaker 1: you're just telling your girlfriend over drinks like, hey, this 671 00:37:16,200 --> 00:37:18,759 Speaker 1: guy's a creep. Watch out. And again, I mean it's 672 00:37:18,800 --> 00:37:20,839 Speaker 1: important to note that if you don't, if you're not 673 00:37:20,920 --> 00:37:22,759 Speaker 1: able to speak up about it, that's what happens, and 674 00:37:22,840 --> 00:37:25,640 Speaker 1: it becomes gossip and whispers and it helps. It really 675 00:37:25,640 --> 00:37:27,279 Speaker 1: doesn't help anybody to have that, to have this be 676 00:37:27,320 --> 00:37:30,080 Speaker 1: happening in the dark. Exactly when we come back from 677 00:37:30,080 --> 00:37:31,840 Speaker 1: a quick break, we're going to talk through more ways 678 00:37:31,880 --> 00:37:34,840 Speaker 1: that you, as an individual woman, whether you are experiencing 679 00:37:34,880 --> 00:37:39,080 Speaker 1: harassment in tech or not, can take action on this 680 00:37:39,160 --> 00:37:41,520 Speaker 1: issue with us, because like all of us, we just 681 00:37:41,600 --> 00:37:45,880 Speaker 1: want to see women men treated fairly and equally and 682 00:37:45,960 --> 00:37:49,080 Speaker 1: given the equal opportunity to achieve our full potential in 683 00:37:49,320 --> 00:37:52,239 Speaker 1: shaping the future. Will be right back after a quick 684 00:37:52,280 --> 00:38:03,600 Speaker 1: word from our sponsors. So we're back and we are 685 00:38:03,640 --> 00:38:06,640 Speaker 1: talking through some of the ways that individuals can take 686 00:38:06,719 --> 00:38:10,919 Speaker 1: action on making tech a safer space for all of us. Right, 687 00:38:11,000 --> 00:38:13,480 Speaker 1: that's really what we're what we're about here. So the 688 00:38:13,520 --> 00:38:17,799 Speaker 1: first couple of things to consider if you are in 689 00:38:17,840 --> 00:38:21,080 Speaker 1: a position, whether it's tech or not quite frankly, in 690 00:38:21,120 --> 00:38:27,720 Speaker 1: which you are basically the victim of harassment, unwanted sexual 691 00:38:27,760 --> 00:38:32,000 Speaker 1: advances feeling fearful for your safety at work, or bearing 692 00:38:32,040 --> 00:38:35,319 Speaker 1: witness to overt or not so overt discrimination based on 693 00:38:35,440 --> 00:38:37,560 Speaker 1: race or gender. When it comes to hiring and promotion 694 00:38:37,600 --> 00:38:40,880 Speaker 1: at work, You've got some options, You've got some choices, 695 00:38:41,160 --> 00:38:44,320 Speaker 1: and we mentioned earlier before the break right how difficult 696 00:38:44,320 --> 00:38:47,680 Speaker 1: those choices can be. But do you want to speak 697 00:38:47,719 --> 00:38:49,719 Speaker 1: to that at alvertation? Yeah, I mean, I just want 698 00:38:49,760 --> 00:38:52,200 Speaker 1: to be real about it. It's I feel like it's 699 00:38:52,239 --> 00:38:54,239 Speaker 1: easy to say you should be I feel like this 700 00:38:54,280 --> 00:38:56,000 Speaker 1: episode has been a lot of us being ra Ra 701 00:38:56,160 --> 00:38:58,719 Speaker 1: Ellen Pale, Ra Ros Susan Feller. But if you don't 702 00:38:58,719 --> 00:39:00,400 Speaker 1: have it in you to be An Ellen Pal or 703 00:39:00,480 --> 00:39:04,880 Speaker 1: Susan Fowler, that is understandable and okay, right, Like we 704 00:39:05,000 --> 00:39:08,239 Speaker 1: it's it's an unfair system that we should be expected 705 00:39:08,280 --> 00:39:10,960 Speaker 1: to take on this burden, and I don't think it's 706 00:39:11,000 --> 00:39:14,400 Speaker 1: fair to you know, you shouldn't be single handedly responsible 707 00:39:14,440 --> 00:39:17,200 Speaker 1: for dismantling systems that we did not set up exactly, 708 00:39:17,280 --> 00:39:18,880 Speaker 1: And so I think it's important to lift that up 709 00:39:18,880 --> 00:39:21,040 Speaker 1: and acknowledge it that as much as we're talking about 710 00:39:21,040 --> 00:39:23,320 Speaker 1: how awesome what these women did and how they started 711 00:39:23,360 --> 00:39:26,960 Speaker 1: these these amazing dialogues, that's not for everybody, and you 712 00:39:26,960 --> 00:39:30,000 Speaker 1: shouldn't feel like it's on you singularly to do that. 713 00:39:30,040 --> 00:39:32,280 Speaker 1: If it's not, you're not in a position to do it, exactly. 714 00:39:32,320 --> 00:39:35,399 Speaker 1: I'm thinking back to one of my favorite quotes from 715 00:39:35,400 --> 00:39:39,640 Speaker 1: Audrey Lord, who says, caring for myself is not self indulgence, 716 00:39:39,760 --> 00:39:42,600 Speaker 1: it is self preservation, and that is an act of 717 00:39:42,680 --> 00:39:46,560 Speaker 1: political warfare. And the context that she brings to that 718 00:39:46,640 --> 00:39:50,760 Speaker 1: conversation as a woman of color, as a queer woman 719 00:39:50,880 --> 00:39:53,360 Speaker 1: of color, speaks to the fact that we are living 720 00:39:53,800 --> 00:40:00,359 Speaker 1: amongst and within institutions of power and supreme to see 721 00:40:00,400 --> 00:40:02,520 Speaker 1: I would go as far as to say, they weren't 722 00:40:02,560 --> 00:40:05,480 Speaker 1: set up by us, weren't set up by women, weren't 723 00:40:05,480 --> 00:40:07,160 Speaker 1: set up by people of color, weren't set up by 724 00:40:07,239 --> 00:40:13,879 Speaker 1: queer people. And so if you are tasked with dismantling that, 725 00:40:13,880 --> 00:40:16,120 Speaker 1: that's an unfair fight. It's an unfair fight, and it's 726 00:40:16,160 --> 00:40:18,160 Speaker 1: a fight you can't win. There are things we can 727 00:40:18,200 --> 00:40:21,600 Speaker 1: all do, but feeling the need to sacrifice yourself in 728 00:40:21,680 --> 00:40:24,560 Speaker 1: order to tackle this system should not be one of them. 729 00:40:24,800 --> 00:40:26,840 Speaker 1: And so I think it really comes back to small 730 00:40:26,880 --> 00:40:29,600 Speaker 1: things that everyone can do. Right. We can all acknowledge 731 00:40:29,600 --> 00:40:31,960 Speaker 1: things like racial battle fatigue. We can all acknowledge the 732 00:40:32,000 --> 00:40:34,400 Speaker 1: ways it plays out for gender right, like some of 733 00:40:34,400 --> 00:40:37,240 Speaker 1: the women that wrote in working at Google saying dealing 734 00:40:37,239 --> 00:40:39,319 Speaker 1: with this stuff day in day out, day in day out. 735 00:40:39,560 --> 00:40:42,320 Speaker 1: We can all understand that and acknowledge that it is 736 00:40:42,400 --> 00:40:44,800 Speaker 1: real and has consequences. We can all do more to 737 00:40:44,880 --> 00:40:47,440 Speaker 1: be less microaggressive. We can learn what that looks like. 738 00:40:47,480 --> 00:40:49,840 Speaker 1: We can learn why it's not okay to say certain 739 00:40:49,880 --> 00:40:52,200 Speaker 1: things to your to your colleagues, even if you mean it, 740 00:40:52,360 --> 00:40:54,279 Speaker 1: you know you don't mean it in a bad way, 741 00:40:54,320 --> 00:40:55,799 Speaker 1: you don't mean a fan, you didn't know you were 742 00:40:55,800 --> 00:40:59,319 Speaker 1: saying something wrong. Understanding why that intent is not that 743 00:40:59,360 --> 00:41:01,960 Speaker 1: important and how it impacts your colleagues. We can all 744 00:41:02,000 --> 00:41:04,680 Speaker 1: do that, and I think for more on that especially. 745 00:41:04,680 --> 00:41:07,680 Speaker 1: We dove into that on our episode about coming out 746 00:41:07,680 --> 00:41:12,160 Speaker 1: at work too, and how intent might be pure, but 747 00:41:12,320 --> 00:41:15,480 Speaker 1: the outcome might be you're making your colleagues feel isolated 748 00:41:15,520 --> 00:41:18,120 Speaker 1: and other and that's not cool and that's not okay, 749 00:41:18,120 --> 00:41:20,719 Speaker 1: and it contributes to racial battle fatigue and to the 750 00:41:20,760 --> 00:41:24,240 Speaker 1: fatigue being a woman in tech in a minority majority environment. 751 00:41:24,680 --> 00:41:26,440 Speaker 1: The other thing I want to think about, because I'm 752 00:41:26,440 --> 00:41:29,640 Speaker 1: now a huge fan of the Labor Relations Board is 753 00:41:29,840 --> 00:41:32,880 Speaker 1: Going back to our women in Labor episode, we highlighted 754 00:41:32,920 --> 00:41:35,120 Speaker 1: some of the ways in which labor organizing looks really 755 00:41:35,120 --> 00:41:39,000 Speaker 1: different nowadays. So, if you are engaged in a whisper 756 00:41:39,040 --> 00:41:42,520 Speaker 1: campaign with all the women in your office about a 757 00:41:42,760 --> 00:41:49,120 Speaker 1: known harasser or a known predatory coworker, with the help 758 00:41:49,120 --> 00:41:52,719 Speaker 1: of the Labor Relations Board, you can actually team up 759 00:41:52,800 --> 00:41:56,000 Speaker 1: with your coworkers to form a work site committee. Not 760 00:41:56,320 --> 00:41:59,920 Speaker 1: a labor union, right, but sort of a modern, temporary, 761 00:42:00,880 --> 00:42:05,839 Speaker 1: a substitute form of organizing and then send a certified 762 00:42:06,080 --> 00:42:10,080 Speaker 1: letter petition case, make your case, document your case as 763 00:42:10,080 --> 00:42:15,040 Speaker 1: a collective that protects you from retaliatory consequences. So what 764 00:42:15,120 --> 00:42:17,960 Speaker 1: I found really compelling in this New York Times article 765 00:42:18,520 --> 00:42:21,399 Speaker 1: about the use of these work site committees from back 766 00:42:21,400 --> 00:42:23,600 Speaker 1: in two thousand and fifteen, the title of which is 767 00:42:23,640 --> 00:42:27,839 Speaker 1: workers organized, but don't unionize to get protection under labor 768 00:42:27,920 --> 00:42:30,640 Speaker 1: law and really, you know, again we're not attorney is 769 00:42:30,640 --> 00:42:32,760 Speaker 1: so talk to the Labor Relations Board and find out 770 00:42:32,840 --> 00:42:36,320 Speaker 1: if your workplace is eligible. And this is all compliant 771 00:42:36,400 --> 00:42:39,600 Speaker 1: given your workplace situation. But if you team up with 772 00:42:39,640 --> 00:42:42,320 Speaker 1: your colleagues to make a formal complaint and then they 773 00:42:42,600 --> 00:42:48,239 Speaker 1: you experience retaliatory punishment. That retaliation is definitely illegal, but 774 00:42:48,320 --> 00:42:50,319 Speaker 1: you have to have this on the books. You have 775 00:42:50,400 --> 00:42:53,280 Speaker 1: to have the record of you putting forth a formal 776 00:42:53,440 --> 00:42:56,640 Speaker 1: complaint as a collective right. I think it's so telling 777 00:42:56,680 --> 00:43:00,440 Speaker 1: that it's all about avoiding retaliation because so many of 778 00:43:00,480 --> 00:43:02,560 Speaker 1: the stories that we talked about today, that's such a 779 00:43:02,560 --> 00:43:05,640 Speaker 1: big component of those stories is being retaliated against if 780 00:43:05,680 --> 00:43:09,759 Speaker 1: they if they speak up. Exactly. I think one of 781 00:43:09,800 --> 00:43:12,719 Speaker 1: the key messages I want to close out on is 782 00:43:13,520 --> 00:43:16,719 Speaker 1: we don't want to talk about how lousy women have 783 00:43:16,840 --> 00:43:20,520 Speaker 1: it in text so much that we gloss over the 784 00:43:20,560 --> 00:43:24,319 Speaker 1: fact that tech is actually a very attractive industry and 785 00:43:24,360 --> 00:43:27,480 Speaker 1: that there are plenty of women who are thriving in 786 00:43:27,600 --> 00:43:31,560 Speaker 1: tech because in some ways, the worst off we talked 787 00:43:31,560 --> 00:43:34,520 Speaker 1: about how much women have it. The more we highlight 788 00:43:34,560 --> 00:43:38,000 Speaker 1: how bad women have in tech, the more we perpetuate 789 00:43:38,040 --> 00:43:40,160 Speaker 1: the myth that women are not welcome here. Yes, I 790 00:43:40,160 --> 00:43:43,000 Speaker 1: I super want to close on, you know, I don't 791 00:43:43,000 --> 00:43:44,600 Speaker 1: want to go too hard on how bad women have 792 00:43:44,600 --> 00:43:46,600 Speaker 1: it in tech because I don't want to discourage other 793 00:43:46,640 --> 00:43:49,560 Speaker 1: women from joining this field. I really found myself in 794 00:43:49,560 --> 00:43:51,640 Speaker 1: the tech space right. I loved working there. I made 795 00:43:51,640 --> 00:43:53,560 Speaker 1: more money than I ever made any job. I ever 796 00:43:53,600 --> 00:43:57,680 Speaker 1: had skills like impulsive decision making and creative decision making 797 00:43:57,760 --> 00:44:00,319 Speaker 1: and taking risks. All of these things I'd strong with 798 00:44:00,440 --> 00:44:03,360 Speaker 1: during my career. We're finally being rewarded, and so I 799 00:44:03,760 --> 00:44:06,719 Speaker 1: loved my time there. Even in the Atlantic piece, but 800 00:44:06,760 --> 00:44:09,760 Speaker 1: when we quoted time and time again about how lousy 801 00:44:09,800 --> 00:44:13,120 Speaker 1: women have it and whose hyperbolic title really lives up 802 00:44:13,160 --> 00:44:15,800 Speaker 1: to the dire situation that was described in that article, 803 00:44:16,360 --> 00:44:19,760 Speaker 1: even that article goes on to say, quote, the dozens 804 00:44:19,800 --> 00:44:23,279 Speaker 1: of women I interviewed for this article love working in tech. 805 00:44:23,800 --> 00:44:27,239 Speaker 1: They love the problem solving, the camaraderie, the opportunity for 806 00:44:27,360 --> 00:44:31,480 Speaker 1: swift advancement and high salaries, the fun of working with 807 00:44:31,520 --> 00:44:36,200 Speaker 1: technology itself. They appreciate their many male colleagues who are 808 00:44:36,200 --> 00:44:40,560 Speaker 1: considerate and supportive. Yet all of them had stories about 809 00:44:40,600 --> 00:44:43,719 Speaker 1: incidents that, no matter how quicker glancing, chipped away at 810 00:44:43,719 --> 00:44:47,200 Speaker 1: their sense of belonging and expertise. I would plus a 811 00:44:47,280 --> 00:44:50,040 Speaker 1: hundred that that sounds exactly like my experience. I love 812 00:44:50,120 --> 00:44:52,200 Speaker 1: the fast paces. I loved how fun it was, and 813 00:44:52,239 --> 00:44:55,040 Speaker 1: I really did appreciate being in a space where even 814 00:44:55,040 --> 00:44:57,360 Speaker 1: if they didn't always get it right, My male colleagues 815 00:44:57,440 --> 00:45:00,680 Speaker 1: were supportive and vocally supportive exact and that's one of 816 00:45:00,719 --> 00:45:03,160 Speaker 1: the other action items we should just I want to 817 00:45:03,239 --> 00:45:07,440 Speaker 1: highlight there, which is calling out micro aggressions and making 818 00:45:07,440 --> 00:45:10,080 Speaker 1: sure you're not contributing to micro aggressions can be taken 819 00:45:10,120 --> 00:45:13,279 Speaker 1: a step further, especially for our male listeners who can 820 00:45:13,280 --> 00:45:16,560 Speaker 1: step in and use your male privilege to really call 821 00:45:16,680 --> 00:45:19,759 Speaker 1: out misogyny on display at work and when micro aggressions 822 00:45:19,800 --> 00:45:22,560 Speaker 1: do happen, to step in and try to rectify them. Yeah, 823 00:45:22,600 --> 00:45:24,840 Speaker 1: and really just be an advocate, be an advocate for 824 00:45:24,880 --> 00:45:28,520 Speaker 1: the for women and people of color who are showing 825 00:45:28,600 --> 00:45:30,640 Speaker 1: up in this space. So in case you I don't know, 826 00:45:31,200 --> 00:45:34,040 Speaker 1: women have historically always been at the forefront of technology. 827 00:45:34,400 --> 00:45:38,040 Speaker 1: Um Grace Hopper invented coding languages. Aida Lovelace invented the 828 00:45:38,080 --> 00:45:40,759 Speaker 1: idea of algorithms way back in the eighteen hundreds. I 829 00:45:40,920 --> 00:45:43,440 Speaker 1: to be Wells, one of my personal heroes was a 830 00:45:43,520 --> 00:45:46,400 Speaker 1: data scientist who really brought a data driven approach to 831 00:45:46,560 --> 00:45:51,759 Speaker 1: research and journalism around lynchings. And even now, finally Hollywood 832 00:45:51,880 --> 00:45:56,200 Speaker 1: seems to be taking up the mantle of retelling the 833 00:45:56,239 --> 00:45:59,799 Speaker 1: story of women's involvement in tech, my personal favorite being 834 00:46:00,120 --> 00:46:03,120 Speaker 1: In Figures, which was I think my favorite movie that 835 00:46:03,160 --> 00:46:06,399 Speaker 1: I've seen in the last year, probably multiple years, all 836 00:46:06,440 --> 00:46:10,240 Speaker 1: about the data scientists and the computer scientists, women, women 837 00:46:10,280 --> 00:46:13,399 Speaker 1: of color, black women in particular of NASA that helped 838 00:46:13,440 --> 00:46:16,440 Speaker 1: put a man on the moon, uh for the United States. 839 00:46:16,520 --> 00:46:19,839 Speaker 1: And even the Imitation Game, that movie that tells the 840 00:46:19,960 --> 00:46:23,280 Speaker 1: story of the World War two code breakers at Bletchley 841 00:46:23,320 --> 00:46:27,160 Speaker 1: Park fails to really clarify in in the most overt 842 00:46:27,239 --> 00:46:30,000 Speaker 1: terms that they could that over two thirds of those 843 00:46:30,040 --> 00:46:32,920 Speaker 1: code breakers were women. So we need to as a 844 00:46:33,000 --> 00:46:37,160 Speaker 1: society remember that. Yes, even though tech is very male 845 00:46:37,239 --> 00:46:40,280 Speaker 1: dominated right now and is in many ways overtly hostile 846 00:46:40,360 --> 00:46:43,319 Speaker 1: to women in tech, this is an industry for which 847 00:46:43,400 --> 00:46:48,640 Speaker 1: women have always been instrumental from its very early groundings, 848 00:46:48,760 --> 00:46:52,400 Speaker 1: especially here in US history. Yeah, and don't let crappy 849 00:46:52,480 --> 00:46:55,360 Speaker 1: dudes or pop culture tell you otherwise, because we've always 850 00:46:55,360 --> 00:46:59,040 Speaker 1: been here exactly. Well, thank you so much for tuning 851 00:46:59,080 --> 00:47:03,480 Speaker 1: into this hefty double episode of stuff Mom never told you. 852 00:47:03,560 --> 00:47:06,400 Speaker 1: There is so much more to dive into, and it 853 00:47:06,440 --> 00:47:08,719 Speaker 1: comes to women in tech. We want to hear from you. 854 00:47:08,760 --> 00:47:11,120 Speaker 1: Are you a woman in tech right now, What has 855 00:47:11,160 --> 00:47:14,399 Speaker 1: your experience been like, what positive changes do you see 856 00:47:14,400 --> 00:47:16,560 Speaker 1: on the horizon, and what work do we all still 857 00:47:16,920 --> 00:47:19,640 Speaker 1: have to do to make sure tech is being as 858 00:47:19,640 --> 00:47:23,560 Speaker 1: inclusive to women and minorities as it can. We send 859 00:47:23,600 --> 00:47:26,160 Speaker 1: us a tweet at mom Stuff Podcast, find us on 860 00:47:26,200 --> 00:47:28,920 Speaker 1: Instagram at stuff mom Never Told You, and, as always, 861 00:47:29,200 --> 00:47:32,040 Speaker 1: send us your lovely emails at mom stuff at how 862 00:47:32,120 --> 00:47:44,279 Speaker 1: stuff works dot com