1 00:00:05,280 --> 00:00:08,200 Speaker 1: Hey, this is Anny hand Samantha. I'm welcome to Stephane. 2 00:00:08,280 --> 00:00:19,680 Speaker 1: Never told your protection of I Heart Radio. Today we 3 00:00:19,720 --> 00:00:22,880 Speaker 1: are once again joined by our good friend Bridget Todd. 4 00:00:23,120 --> 00:00:25,360 Speaker 1: Thanks for being with us as Bridget always a pleasure. 5 00:00:25,400 --> 00:00:28,240 Speaker 1: Thank you for having me back. And happy days, by 6 00:00:28,320 --> 00:00:30,640 Speaker 1: the way, Happy holidays to YouTube. Are you all about 7 00:00:30,640 --> 00:00:34,680 Speaker 1: to get into some holiday fun? I you know what 8 00:00:34,840 --> 00:00:38,760 Speaker 1: I think? I so I cannot when I get presents 9 00:00:39,159 --> 00:00:40,920 Speaker 1: and and you can attest to this, Like when I 10 00:00:40,960 --> 00:00:42,640 Speaker 1: received the presents that I'm about to give people, I 11 00:00:42,680 --> 00:00:45,280 Speaker 1: almost can't wait. So I'm like, I've been kind of 12 00:00:45,440 --> 00:00:48,920 Speaker 1: slowly handing out gifts even though it's not really Christmas time. 13 00:00:49,080 --> 00:00:51,520 Speaker 1: I did this for my post carrier and I was like, oh, 14 00:00:51,680 --> 00:00:53,840 Speaker 1: Christmas two weeks away, but I went ahead and gave 15 00:00:53,880 --> 00:00:56,560 Speaker 1: it to him. I was like, that's I'm a little eager. 16 00:00:56,680 --> 00:01:01,520 Speaker 1: So yes, that is my holidays nannigans. Yeah, we're already 17 00:01:01,520 --> 00:01:03,760 Speaker 1: in the holiday season. You know. I think I get 18 00:01:03,880 --> 00:01:09,320 Speaker 1: this acceptable anytime, that's fair, especially now right Maybe this 19 00:01:09,360 --> 00:01:11,600 Speaker 1: will help before. But yeah, I realized yesterday as I 20 00:01:11,640 --> 00:01:14,200 Speaker 1: was doing Days, I was like, it's not two weeks 21 00:01:14,240 --> 00:01:20,039 Speaker 1: away from Christmas. I probably should slow down. Oh see, 22 00:01:20,080 --> 00:01:23,760 Speaker 1: I'm like, I'm normally much more holiday spirit. I don't 23 00:01:23,760 --> 00:01:25,200 Speaker 1: know if it's even that, but I guess because I 24 00:01:25,240 --> 00:01:27,800 Speaker 1: go out more, I'm doing more stuff. And now I'm like, oh, yeah, 25 00:01:27,800 --> 00:01:31,400 Speaker 1: I guess I should be doing this holiday stuff. And 26 00:01:31,440 --> 00:01:33,120 Speaker 1: I'm in my apartment. So it's like, I don't know 27 00:01:33,160 --> 00:01:36,520 Speaker 1: what the hold up is, but I put up my 28 00:01:36,560 --> 00:01:40,119 Speaker 1: little tree, right Bridget Do you have you done your 29 00:01:40,280 --> 00:01:43,839 Speaker 1: holiday starting traditions? None? I am. I have to say, 30 00:01:44,040 --> 00:01:46,319 Speaker 1: I am look a little bit of a grinch historically, 31 00:01:46,400 --> 00:01:49,320 Speaker 1: like I'm not really big into the holidays. Um, this 32 00:01:49,640 --> 00:01:51,400 Speaker 1: time around, I don't know. This is the first time 33 00:01:51,400 --> 00:01:55,480 Speaker 1: I've ever It's weird. I have always hated celebrating the holidays, 34 00:01:55,480 --> 00:01:57,640 Speaker 1: and when as it creeps in and you know, after 35 00:01:57,680 --> 00:02:00,400 Speaker 1: Halloween when they start the Christmas music, I'm rolling my eyes. 36 00:02:00,760 --> 00:02:02,800 Speaker 1: This is the only time that I've been like, I 37 00:02:02,840 --> 00:02:06,440 Speaker 1: want I want things to be festive, so a little bit. 38 00:02:06,720 --> 00:02:10,920 Speaker 1: I've already started watching Lifetime movies where you know, a 39 00:02:11,000 --> 00:02:14,120 Speaker 1: busy city woman meets at hometown holiday hugs. I've already 40 00:02:14,120 --> 00:02:15,880 Speaker 1: I've already seen a couple of those. So I'm getting 41 00:02:15,919 --> 00:02:19,919 Speaker 1: into the spirit. Yeah, so we are doing our movies 42 00:02:20,200 --> 00:02:23,280 Speaker 1: based on the holidays essentially or somewhat themed, and we 43 00:02:23,320 --> 00:02:26,080 Speaker 1: are now watching when we finished both of us sleep 44 00:02:26,080 --> 00:02:29,720 Speaker 1: list in Seattle, and You've got mil and I'm like, okay, 45 00:02:29,760 --> 00:02:31,679 Speaker 1: my next because that's not in my traditions. So we 46 00:02:31,720 --> 00:02:33,560 Speaker 1: watched it so we could talk about it. It's coming 47 00:02:33,560 --> 00:02:36,919 Speaker 1: in upcoming episodes. But now I'm like, oh, I really 48 00:02:37,639 --> 00:02:42,480 Speaker 1: realizing my holiday movies are romantic comedies and does not 49 00:02:42,639 --> 00:02:47,040 Speaker 1: my personality personally, but those are my traditions, and I'm like, 50 00:02:47,120 --> 00:02:53,000 Speaker 1: maybe I should investigate while so much ye took the 51 00:02:53,000 --> 00:02:55,200 Speaker 1: bottom of this. Oh my god, if you if you 52 00:02:55,200 --> 00:02:57,160 Speaker 1: want to watch a good romantic comedy that it's also 53 00:02:57,200 --> 00:03:01,080 Speaker 1: holiday related, may I recommend the holiday I just thought 54 00:03:01,120 --> 00:03:05,840 Speaker 1: about this than do not sleep on Jack Black as 55 00:03:05,840 --> 00:03:09,560 Speaker 1: a romantic comedy lead to take me a moment to 56 00:03:09,760 --> 00:03:13,280 Speaker 1: really get there, because the chemistry seems so friendship, like 57 00:03:13,320 --> 00:03:16,560 Speaker 1: they we're like, okay, it builds, it build, But it 58 00:03:16,680 --> 00:03:19,520 Speaker 1: still took me a minute to get to that, I think. 59 00:03:19,520 --> 00:03:21,280 Speaker 1: But they were the more fun couple, like they were 60 00:03:21,280 --> 00:03:28,920 Speaker 1: the couple that I was rooting for them definitely definitely okay, 61 00:03:29,280 --> 00:03:33,360 Speaker 1: which validated me and I feel so much better today. Yeah, 62 00:03:34,000 --> 00:03:36,839 Speaker 1: we have like literally the opposite holiday maybe taste, which 63 00:03:36,840 --> 00:03:38,440 Speaker 1: is nothing wrong with that, but that was my first 64 00:03:38,440 --> 00:03:40,120 Speaker 1: time watching both of those movies. Well, no, I've seen 65 00:03:40,160 --> 00:03:43,520 Speaker 1: You've Got Me. I went through that, but it's been 66 00:03:43,560 --> 00:03:50,040 Speaker 1: interesting for me. Yes, But today we were talking in 67 00:03:50,080 --> 00:03:52,600 Speaker 1: the ongoing continuing segment about what women are up to 68 00:03:52,600 --> 00:03:54,280 Speaker 1: you on the internet. And by the way, I love 69 00:03:54,320 --> 00:03:57,160 Speaker 1: Bridget that you have like this fancy to me fancy 70 00:03:57,160 --> 00:03:59,960 Speaker 1: headphones Mike set up. Now you look like you're like 71 00:04:00,000 --> 00:04:02,400 Speaker 1: in a control center. I know, I feel like I'm 72 00:04:02,400 --> 00:04:05,720 Speaker 1: taking calls like you know, I feel very official. I've 73 00:04:05,720 --> 00:04:07,760 Speaker 1: got the headset, the microphone. It's a little bit like 74 00:04:07,880 --> 00:04:13,400 Speaker 1: Gwen Stefani. She used to wear the where they're like, Mike, yeah, 75 00:04:13,520 --> 00:04:16,559 Speaker 1: you would just slipper, But you know she was working 76 00:04:16,600 --> 00:04:23,360 Speaker 1: it because she needed free Yes, yes, yes, I love it. Um. So, 77 00:04:23,600 --> 00:04:26,560 Speaker 1: who did you bring us to talk about today? Well, 78 00:04:26,600 --> 00:04:29,800 Speaker 1: today I really wanted to talk about the situation with 79 00:04:30,040 --> 00:04:36,120 Speaker 1: formally Google AI engineer tim Neet Jebru. Her story is 80 00:04:37,000 --> 00:04:39,320 Speaker 1: really familiar to me as a black woman who has 81 00:04:39,320 --> 00:04:42,039 Speaker 1: worked in tech. Um. It's also kind of infuriating but 82 00:04:42,240 --> 00:04:45,279 Speaker 1: also kind of hopeful, you know, I think that we 83 00:04:45,400 --> 00:04:47,839 Speaker 1: don't really talk enough about the way that women and 84 00:04:47,880 --> 00:04:50,720 Speaker 1: women of color and black women can be treated with 85 00:04:50,760 --> 00:04:54,760 Speaker 1: like sort of this like baseline hostility in certain workplaces, 86 00:04:54,839 --> 00:04:58,120 Speaker 1: often tech spaces. And you know, that's not good for 87 00:04:58,160 --> 00:05:00,640 Speaker 1: anybody in any workplace, whether you are are working at 88 00:05:00,640 --> 00:05:04,000 Speaker 1: a nonprofit or a podcaster or whatever wherever in between. 89 00:05:04,040 --> 00:05:06,680 Speaker 1: It's not good for anybody, but especially with tech, When 90 00:05:07,200 --> 00:05:10,599 Speaker 1: underrepresented voices are treated with hostility, it becomes a big 91 00:05:10,600 --> 00:05:13,840 Speaker 1: problem for everybody because tech drives so much of our 92 00:05:13,880 --> 00:05:16,680 Speaker 1: world and so much of our day to day. Right, Um, 93 00:05:16,720 --> 00:05:18,880 Speaker 1: And I was so excited when you sent this as 94 00:05:19,160 --> 00:05:21,800 Speaker 1: an email, and it maybe because or as an outline, 95 00:05:21,880 --> 00:05:24,000 Speaker 1: and maybe because I was following you and that's where 96 00:05:24,000 --> 00:05:25,960 Speaker 1: I saw it. But like I think, two days before, 97 00:05:26,000 --> 00:05:28,400 Speaker 1: I went down the rabbit hole of seeing what was 98 00:05:28,440 --> 00:05:31,320 Speaker 1: happening and why this was such a big conversation and 99 00:05:31,360 --> 00:05:35,000 Speaker 1: what was happening to her specifically, and how she was 100 00:05:35,120 --> 00:05:40,120 Speaker 1: being gaslighted in every turn, and it's such a phenomenal thing. 101 00:05:40,320 --> 00:05:42,960 Speaker 1: And it didn't nothing would have been corrected has she 102 00:05:43,040 --> 00:05:46,240 Speaker 1: not spoken out, And and the way Google and their 103 00:05:46,279 --> 00:05:50,440 Speaker 1: executives have backtracked, it's almost like a conspiracy theory movie, 104 00:05:50,520 --> 00:05:53,520 Speaker 1: like it is like it took me like as I've 105 00:05:53,520 --> 00:05:54,960 Speaker 1: been going through this, I was like, oh, yes, and 106 00:05:55,120 --> 00:05:56,600 Speaker 1: I need to hear all about this. I know Bridge 107 00:05:56,600 --> 00:05:58,240 Speaker 1: is gonna give me all the information because it is 108 00:05:58,279 --> 00:06:01,479 Speaker 1: again a bigger problem in what could also be a 109 00:06:01,520 --> 00:06:06,200 Speaker 1: conversation about performative uh justice in itself, a performative social justice, 110 00:06:06,320 --> 00:06:09,240 Speaker 1: and this is the iconic of like, oh, we're so 111 00:06:09,360 --> 00:06:12,080 Speaker 1: cool and we're definitely all about putting out black squares 112 00:06:12,080 --> 00:06:15,240 Speaker 1: and rooting for people, but not in action. So yes, 113 00:06:15,320 --> 00:06:19,120 Speaker 1: I'm excited. Absolutely, You're exactly right. I think with the situation, 114 00:06:19,560 --> 00:06:23,039 Speaker 1: you know, it really makes you ask like like are 115 00:06:23,160 --> 00:06:26,280 Speaker 1: you actually are these big companies that control so much 116 00:06:26,279 --> 00:06:29,599 Speaker 1: of our world? Are they actually meaningfully invested in the 117 00:06:29,640 --> 00:06:32,120 Speaker 1: communities they say they are, Like you posted your black square, 118 00:06:32,120 --> 00:06:34,240 Speaker 1: but how do you treat the black women who are 119 00:06:34,320 --> 00:06:37,880 Speaker 1: actually in your employee UM's it's a great question, so 120 00:06:37,960 --> 00:06:41,279 Speaker 1: essentially for folks who don't know so timnet Jabru, she 121 00:06:41,400 --> 00:06:44,920 Speaker 1: is a widely respected leader in AI ethics research. UM 122 00:06:45,040 --> 00:06:48,480 Speaker 1: she was really known for co authoring this groundbreaking paper 123 00:06:48,680 --> 00:06:51,040 Speaker 1: a couple of years ago that showed that facial recognition 124 00:06:51,160 --> 00:06:54,760 Speaker 1: software was less accurate when it came to identifying women 125 00:06:54,920 --> 00:06:57,680 Speaker 1: and people of color. So might be thinking, well, big deal, 126 00:06:57,760 --> 00:07:01,320 Speaker 1: who cares, But that means that those technologies that use 127 00:07:01,400 --> 00:07:05,040 Speaker 1: facial recognition software can end up discriminating against women and 128 00:07:05,080 --> 00:07:08,040 Speaker 1: people of color if they don't take into account our faces. 129 00:07:08,120 --> 00:07:11,760 Speaker 1: If we're not even being taken into account, then this, 130 00:07:11,760 --> 00:07:14,200 Speaker 1: this software that is that is controlling so much of 131 00:07:14,240 --> 00:07:18,360 Speaker 1: our world will sort of either a at best, you know, 132 00:07:18,440 --> 00:07:21,800 Speaker 1: not work for us, be at worst create problems for us. 133 00:07:21,840 --> 00:07:24,240 Speaker 1: You know, tech is better when it's created by teams 134 00:07:24,320 --> 00:07:26,560 Speaker 1: that look like the people that will be using it. 135 00:07:26,720 --> 00:07:30,120 Speaker 1: So this study was called Gender Shades, and it revealed 136 00:07:30,200 --> 00:07:33,880 Speaker 1: the gender and racial biases embedded in commercial facial recognition software. 137 00:07:34,040 --> 00:07:37,440 Speaker 1: It came out in February, and listen, I am not 138 00:07:37,480 --> 00:07:41,960 Speaker 1: going to pretend to be a facial recognition expert, so 139 00:07:42,200 --> 00:07:45,280 Speaker 1: just you know, really big caveat that. I don't want 140 00:07:45,280 --> 00:07:47,000 Speaker 1: to say that. I you know, I know what I'm 141 00:07:47,000 --> 00:07:50,920 Speaker 1: talking about here, um, But essentially, facial recognition is being 142 00:07:50,960 --> 00:07:53,320 Speaker 1: used more and more and as tech companies sell this 143 00:07:53,440 --> 00:07:56,200 Speaker 1: software to police departments to help them, you know, track 144 00:07:56,280 --> 00:07:59,240 Speaker 1: down suspects and things like that it's pretty clear that 145 00:07:59,280 --> 00:08:02,480 Speaker 1: if the if this software does not work on people 146 00:08:02,480 --> 00:08:04,600 Speaker 1: of color and women, it's a problem. Like it's a 147 00:08:04,640 --> 00:08:09,320 Speaker 1: it's a big issue for everybody. Yeah, and I love 148 00:08:10,240 --> 00:08:11,920 Speaker 1: there's so many things in our lives I feel that 149 00:08:11,960 --> 00:08:15,520 Speaker 1: we take for granted that the technology inherent in it 150 00:08:16,160 --> 00:08:18,960 Speaker 1: um does have these biases at the people coding it. 151 00:08:19,520 --> 00:08:22,760 Speaker 1: And that's why one of these the study was such 152 00:08:22,800 --> 00:08:25,360 Speaker 1: a huge deal. And like it you might not ever 153 00:08:25,400 --> 00:08:28,160 Speaker 1: think about it too much if if it works for you, 154 00:08:28,320 --> 00:08:32,400 Speaker 1: or are just just you know when you think about 155 00:08:32,440 --> 00:08:35,520 Speaker 1: like the sink recognizing you or the automatic faucet, and 156 00:08:35,559 --> 00:08:37,360 Speaker 1: you're just kind of like, oh, you don't put too 157 00:08:37,440 --> 00:08:41,319 Speaker 1: much thought into these things that like everyday impact that 158 00:08:41,360 --> 00:08:44,520 Speaker 1: they have exactly. Yeah, I mean if you have if 159 00:08:44,559 --> 00:08:47,440 Speaker 1: you are someone like me that has you know, melanin 160 00:08:47,600 --> 00:08:50,320 Speaker 1: darker skin, you probably already know this. But when you 161 00:08:50,360 --> 00:08:53,640 Speaker 1: go to the airport, those sinks that like automatic sinks 162 00:08:53,640 --> 00:08:55,560 Speaker 1: that you know just like put out a little bit 163 00:08:55,559 --> 00:08:57,520 Speaker 1: of soap and water, they don't actually work very well 164 00:08:57,559 --> 00:08:59,000 Speaker 1: for people who have dark skin. So when I go 165 00:08:59,000 --> 00:09:01,040 Speaker 1: to the airport, I'm trying to wat my hands. It's 166 00:09:01,040 --> 00:09:02,920 Speaker 1: a whole ordeal and it's like one of those things 167 00:09:02,920 --> 00:09:07,080 Speaker 1: that you don't even if you are, if you're not 168 00:09:07,160 --> 00:09:10,000 Speaker 1: experiencing it regularly, you might not even think about it. Right. 169 00:09:10,040 --> 00:09:13,520 Speaker 1: Another good example is um with COVID and everything. One 170 00:09:13,559 --> 00:09:15,840 Speaker 1: of the things that people suggested people that like folks 171 00:09:15,920 --> 00:09:18,440 Speaker 1: have in their home is a pulse oxyometer and that's 172 00:09:18,480 --> 00:09:20,240 Speaker 1: that little do dad that you put your finger in 173 00:09:20,280 --> 00:09:23,000 Speaker 1: and it tells you like how oxygenated your blood is 174 00:09:23,000 --> 00:09:25,559 Speaker 1: to see how you're like airflow is doing. And there 175 00:09:25,640 --> 00:09:27,400 Speaker 1: was a report that said that those didn't work very 176 00:09:27,400 --> 00:09:29,480 Speaker 1: well on darker skin to people. So it's like there 177 00:09:29,480 --> 00:09:32,720 Speaker 1: are all these little ways I think sometimes. You know, 178 00:09:32,840 --> 00:09:35,800 Speaker 1: for me, the thing with the bathroom sinks in the airports, 179 00:09:35,840 --> 00:09:39,120 Speaker 1: it's an annoyance. But if you were to to take 180 00:09:39,200 --> 00:09:41,480 Speaker 1: that to its ultimate level where it's like, well, what 181 00:09:41,559 --> 00:09:43,440 Speaker 1: other things don't work for me because I happen to 182 00:09:43,440 --> 00:09:45,480 Speaker 1: be a black woman, right, Like what other things am 183 00:09:45,480 --> 00:09:48,000 Speaker 1: I not? We're just like not designed with me and 184 00:09:48,040 --> 00:09:51,280 Speaker 1: mind that are going to at best make me miss 185 00:09:51,320 --> 00:09:54,040 Speaker 1: my flight because I couldn't wash my hands, or at worst, 186 00:09:54,200 --> 00:09:56,959 Speaker 1: you know, stop me from knowing how much oxygen is 187 00:09:56,960 --> 00:09:59,040 Speaker 1: in my blood, or like criminalize me right, And so 188 00:09:59,440 --> 00:10:01,480 Speaker 1: it's such a spectrum that I think that we don't 189 00:10:01,480 --> 00:10:04,600 Speaker 1: even really think about. It's so it's so coded into 190 00:10:04,640 --> 00:10:08,079 Speaker 1: everything around us, right, I mean absolutely, and it's become 191 00:10:08,360 --> 00:10:10,319 Speaker 1: it is a racial issue. It's not just a thing 192 00:10:10,360 --> 00:10:12,880 Speaker 1: of oh that didn't work for me, boom, move on. 193 00:10:13,120 --> 00:10:16,120 Speaker 1: Is literally like, well, this is how racial profile begins. 194 00:10:16,320 --> 00:10:18,400 Speaker 1: You think this is a person of color in the story, 195 00:10:18,440 --> 00:10:21,559 Speaker 1: and don't actually acknowledge who this individual person is, and 196 00:10:21,920 --> 00:10:27,720 Speaker 1: therefore you have this whole constant tragedies based on misidentification. 197 00:10:28,040 --> 00:10:30,240 Speaker 1: And then you're using a system that's supposed to be 198 00:10:30,280 --> 00:10:33,840 Speaker 1: infallible or accord like being sold as being infallible, and 199 00:10:33,960 --> 00:10:36,920 Speaker 1: using by law enforcement who already has a bias to 200 00:10:36,960 --> 00:10:40,960 Speaker 1: a specific community is really dangerous. So, yeah, that report 201 00:10:41,320 --> 00:10:44,240 Speaker 1: is more than important. It is life changing, life saving 202 00:10:44,720 --> 00:10:47,080 Speaker 1: having her being a part of that voice, but then 203 00:10:47,120 --> 00:10:49,679 Speaker 1: also understanding why she's a part of the ethics and 204 00:10:49,679 --> 00:10:53,520 Speaker 1: why this affects ethics exactly. I'm so glad that you 205 00:10:53,520 --> 00:10:56,040 Speaker 1: brought up ethics. I think that's something that's easy to 206 00:10:56,080 --> 00:11:01,240 Speaker 1: be missed where, you know. So, after this report came out, 207 00:11:01,320 --> 00:11:04,040 Speaker 1: she then went to Google, where she worked on AI 208 00:11:04,160 --> 00:11:05,840 Speaker 1: and I think, you know, Google is a leader in 209 00:11:05,880 --> 00:11:08,160 Speaker 1: the AI space, But when you have somebody on your 210 00:11:08,160 --> 00:11:11,480 Speaker 1: team who is trying to almost whistle blow the ways 211 00:11:11,559 --> 00:11:14,319 Speaker 1: in which your tech can fail people, tech that you're 212 00:11:14,360 --> 00:11:17,000 Speaker 1: selling to police departments, check that you're you know, using 213 00:11:17,000 --> 00:11:19,600 Speaker 1: to really build the world around us, someone who is 214 00:11:19,640 --> 00:11:22,880 Speaker 1: internally saying, hey, we should be asking some tough questions 215 00:11:22,880 --> 00:11:25,440 Speaker 1: about this technology, about how we're using about the ethics 216 00:11:25,520 --> 00:11:28,640 Speaker 1: of its use, about who who we are selling it to, 217 00:11:28,800 --> 00:11:31,120 Speaker 1: and and what they're using it for. I think that 218 00:11:31,240 --> 00:11:35,560 Speaker 1: Google probably liked having someone like her on their team 219 00:11:36,080 --> 00:11:38,480 Speaker 1: when it suited them, when they were able to say, oh, 220 00:11:38,520 --> 00:11:42,480 Speaker 1: we're so diverse, like look, how look how we are championing. 221 00:11:42,600 --> 00:11:45,280 Speaker 1: Are our black women on our team? Like look how 222 00:11:45,280 --> 00:11:49,400 Speaker 1: great we are? But when that same black woman was like, well, actually, Google, 223 00:11:49,520 --> 00:11:52,000 Speaker 1: here are some ways that your tech is used to 224 00:11:52,040 --> 00:11:54,760 Speaker 1: harm my community, or here are some ways that your 225 00:11:54,840 --> 00:11:57,560 Speaker 1: tech is you know, you maybe need to do some 226 00:11:57,600 --> 00:12:01,640 Speaker 1: more thinking about it's curious to me. Curious in quotes 227 00:12:02,040 --> 00:12:05,680 Speaker 1: that she was so swiftly forced out and so and 228 00:12:05,760 --> 00:12:08,760 Speaker 1: like done it was done. If it happened in the 229 00:12:08,760 --> 00:12:10,600 Speaker 1: way that she described, I which I have no reason 230 00:12:10,640 --> 00:12:15,079 Speaker 1: to believe otherwise um cruelly. You know. She describes this 231 00:12:15,200 --> 00:12:19,000 Speaker 1: back and force via email with her bosses and her 232 00:12:19,080 --> 00:12:21,960 Speaker 1: being like, well, you know, like what are we gonna 233 00:12:22,000 --> 00:12:24,160 Speaker 1: do here? You know, like if we're not able to 234 00:12:24,200 --> 00:12:25,880 Speaker 1: get get on the same page, you know, I might 235 00:12:25,880 --> 00:12:28,200 Speaker 1: have to resign. And this happening while she was on 236 00:12:28,320 --> 00:12:31,280 Speaker 1: essentially on vacation and coming back from vacation and realizing 237 00:12:31,280 --> 00:12:34,000 Speaker 1: her email was turned off, right like these are I 238 00:12:34,640 --> 00:12:38,280 Speaker 1: have been kind of unceremoniously pushed out of companies, And 239 00:12:38,720 --> 00:12:42,440 Speaker 1: there are choices that leadership that companies make, right like 240 00:12:42,520 --> 00:12:45,319 Speaker 1: you don't have to. You don't have to even if 241 00:12:45,320 --> 00:12:48,600 Speaker 1: you're partying ways with unemployee. You know, somebody made a 242 00:12:48,679 --> 00:12:51,120 Speaker 1: choice to to do it while she was on vacation 243 00:12:51,280 --> 00:12:53,880 Speaker 1: and have the first step be disabling her work email, 244 00:12:53,960 --> 00:12:56,360 Speaker 1: you know, I mean that's an ongoing thought for like, 245 00:12:56,400 --> 00:12:59,160 Speaker 1: anytime my email does not work, my first thought is 246 00:12:59,200 --> 00:13:01,000 Speaker 1: did I just get fired? Like it just like that 247 00:13:01,200 --> 00:13:04,199 Speaker 1: doesn't automatic? And so this is bringing a little too 248 00:13:05,360 --> 00:13:07,920 Speaker 1: to me. We have some more for you listeners, but 249 00:13:07,960 --> 00:13:09,240 Speaker 1: first we have a quick break for a word from 250 00:13:09,240 --> 00:13:26,160 Speaker 1: her sponsor in in fact, thank you sponsored. Can you 251 00:13:26,280 --> 00:13:29,720 Speaker 1: talk about why exactly she was fired from Google to 252 00:13:29,720 --> 00:13:32,840 Speaker 1: begin or and there words she resigned, which is untrue. 253 00:13:33,520 --> 00:13:36,680 Speaker 1: But why this happened exactly? Exactly, So a little bit 254 00:13:36,720 --> 00:13:39,719 Speaker 1: of background. On Wednesday, December the two, she was then 255 00:13:40,000 --> 00:13:43,320 Speaker 1: the co lead of Google's Ethical AI team, she announced 256 00:13:43,360 --> 00:13:45,520 Speaker 1: on Twitter that Google had forced her out and it 257 00:13:45,600 --> 00:13:49,080 Speaker 1: was a conflict over a different paper than the one 258 00:13:49,080 --> 00:13:51,920 Speaker 1: that we were just discussing from eighteen um that she 259 00:13:51,960 --> 00:13:54,960 Speaker 1: had co authored at at Google. So Jeff Dean, the 260 00:13:54,960 --> 00:13:57,760 Speaker 1: head of Google AI, told colleagues and an internal memo 261 00:13:58,040 --> 00:14:00,760 Speaker 1: which has since been put online at the paper quote 262 00:14:01,000 --> 00:14:05,319 Speaker 1: didn't meet our bar for publication, and that Jebrew had 263 00:14:05,320 --> 00:14:08,280 Speaker 1: said that she would resign unless Google met a number 264 00:14:08,280 --> 00:14:11,440 Speaker 1: of conditions which Google was unwilling to meet. She said 265 00:14:11,480 --> 00:14:14,000 Speaker 1: that she asked to negotiate like an exit date or 266 00:14:14,000 --> 00:14:16,400 Speaker 1: a last date um for her employment when she got 267 00:14:16,400 --> 00:14:18,719 Speaker 1: back on vacation, but during that time she was cut 268 00:14:18,760 --> 00:14:21,520 Speaker 1: off from her email. Uh, and so you might be asking, like, 269 00:14:21,600 --> 00:14:24,160 Speaker 1: what exactly could have been in this paper that was 270 00:14:24,200 --> 00:14:28,040 Speaker 1: so you know, horrible that Google had to fire her. Again, 271 00:14:28,160 --> 00:14:30,280 Speaker 1: I'm not going to pretend that I'm smart enough to 272 00:14:30,320 --> 00:14:34,240 Speaker 1: summarize the paper itself However, thankfully the actual smart people 273 00:14:34,240 --> 00:14:35,800 Speaker 1: who know what they're talking about a M I T. 274 00:14:35,960 --> 00:14:40,080 Speaker 1: Technology Review obtained a copy and summarized it. So they 275 00:14:40,120 --> 00:14:42,280 Speaker 1: got this copy from one of the papers co authors, 276 00:14:42,320 --> 00:14:46,080 Speaker 1: Emily M. Bender, who was a professor of computational linguistics 277 00:14:46,120 --> 00:14:48,760 Speaker 1: at the University of Washington, and there's a great summary 278 00:14:48,960 --> 00:14:50,600 Speaker 1: if you go to Technology review dot com you can 279 00:14:50,640 --> 00:14:53,240 Speaker 1: find it. But a little bit from what they said 280 00:14:53,280 --> 00:14:56,120 Speaker 1: was in the paper. So one the paper was getting 281 00:14:56,120 --> 00:14:59,600 Speaker 1: into AIS negative impacts on things like climate change. So 282 00:14:59,640 --> 00:15:02,440 Speaker 1: they they point out that, you know climate changes, We 283 00:15:02,520 --> 00:15:07,320 Speaker 1: know a big burden of climate change is faced by marginalized, 284 00:15:07,400 --> 00:15:11,239 Speaker 1: unrepresented people, and so if you are, you know, amassing 285 00:15:11,400 --> 00:15:14,320 Speaker 1: tons and tons and tons of money to study AI, 286 00:15:14,400 --> 00:15:16,520 Speaker 1: you can actually be having a negative impact on people 287 00:15:16,520 --> 00:15:20,040 Speaker 1: who are already negatively impacted by climate change. Another was 288 00:15:20,120 --> 00:15:22,720 Speaker 1: they were really concerned about AI's use of racist and 289 00:15:22,720 --> 00:15:25,720 Speaker 1: sexist language biases. Um, this is a little bit like 290 00:15:25,800 --> 00:15:30,080 Speaker 1: in the weeds, but essentially AI, if you if you use, 291 00:15:30,400 --> 00:15:34,560 Speaker 1: if you base AI on are already existing length like 292 00:15:34,640 --> 00:15:38,200 Speaker 1: racist or sexist or biased language, that language can be 293 00:15:38,280 --> 00:15:41,800 Speaker 1: sort of encoded into your AI. And so you know, 294 00:15:41,920 --> 00:15:44,240 Speaker 1: we I don't think anybody, really, I don't think anybody 295 00:15:44,280 --> 00:15:49,720 Speaker 1: working at Google sets out to create technology that pushes 296 00:15:50,480 --> 00:15:53,560 Speaker 1: racist and sexist ideology or biases. But we all have, 297 00:15:53,800 --> 00:15:55,640 Speaker 1: you know, implicit bias that you don't like. We don't 298 00:15:55,640 --> 00:15:58,640 Speaker 1: always know where our biases are or where things that 299 00:15:58,680 --> 00:16:01,480 Speaker 1: we don't we can't really see are. Something I might read, 300 00:16:01,600 --> 00:16:03,960 Speaker 1: might I'll be oh, that's fine, But somebody else from 301 00:16:03,960 --> 00:16:05,960 Speaker 1: a different background might read that and say, oh, actually 302 00:16:06,000 --> 00:16:08,880 Speaker 1: that's sexist or racist or problematic for X, y Z reason. 303 00:16:09,160 --> 00:16:11,680 Speaker 1: And so when you have teams that aren't able to 304 00:16:11,760 --> 00:16:13,760 Speaker 1: like look out for those kinds of things because they're 305 00:16:13,800 --> 00:16:17,320 Speaker 1: not as inclusive as they should be, it's a problem. 306 00:16:17,360 --> 00:16:19,400 Speaker 1: So one of the co authors of the study, when 307 00:16:19,480 --> 00:16:22,280 Speaker 1: asked what the goal of the paper was, she says, 308 00:16:22,840 --> 00:16:24,880 Speaker 1: we are working at a scale where the people building 309 00:16:24,880 --> 00:16:27,520 Speaker 1: the things can't actually get their arms around the data. 310 00:16:27,760 --> 00:16:30,880 Speaker 1: And because the upsides are so obvious, it's particularly important 311 00:16:30,880 --> 00:16:33,920 Speaker 1: to step back and ask ourselves what are the possible downsides? 312 00:16:34,160 --> 00:16:36,200 Speaker 1: How do we get the benefits of this while mitigating 313 00:16:36,200 --> 00:16:39,200 Speaker 1: the risk. And so it sounds like to me they 314 00:16:39,240 --> 00:16:42,960 Speaker 1: were really trying to point out the ways that Google's 315 00:16:43,040 --> 00:16:47,560 Speaker 1: work in AI could actually be adding to the negative 316 00:16:47,880 --> 00:16:51,360 Speaker 1: Like it sounds like they were concerned that AI, that 317 00:16:51,480 --> 00:16:55,640 Speaker 1: Google's work in AI was actually just adding to negative 318 00:16:55,680 --> 00:16:58,920 Speaker 1: consequences of in the AI space as opposed to actually 319 00:16:58,960 --> 00:17:00,840 Speaker 1: having bet at this, and so they were interested in 320 00:17:00,880 --> 00:17:03,200 Speaker 1: saying like, oh, well, gee, are we able to continue 321 00:17:03,280 --> 00:17:06,240 Speaker 1: this work while mitigating this risk and this these negative 322 00:17:06,280 --> 00:17:09,720 Speaker 1: factors that AI that we know can come with AI. Right, 323 00:17:10,160 --> 00:17:12,320 Speaker 1: There's so many things that just goes through my mind, 324 00:17:12,640 --> 00:17:15,240 Speaker 1: like all the practical sense though it seems like these 325 00:17:15,280 --> 00:17:17,560 Speaker 1: types of studies you do want them at the beginning. 326 00:17:17,560 --> 00:17:20,000 Speaker 1: Why wouldn't you want this more cost efficient to go 327 00:17:20,040 --> 00:17:22,359 Speaker 1: ahead and nip this in the bud corrected instead of 328 00:17:22,400 --> 00:17:25,160 Speaker 1: having a recall and then having lawsuits and then having 329 00:17:25,160 --> 00:17:26,960 Speaker 1: all of these things that I was gonna pileot because 330 00:17:27,080 --> 00:17:29,959 Speaker 1: it's gonna happen, bitch, is gonna happen. Like it's just 331 00:17:30,000 --> 00:17:33,159 Speaker 1: like an automatic like obvious. I don't know, Like I 332 00:17:33,200 --> 00:17:35,320 Speaker 1: just get so upset, like this is logical. This is 333 00:17:35,359 --> 00:17:37,320 Speaker 1: just a logical sense of being And that's why you 334 00:17:37,440 --> 00:17:40,480 Speaker 1: hired her, That's why she's doing these studies. Why are 335 00:17:40,520 --> 00:17:43,560 Speaker 1: you not listening if you have already said she is 336 00:17:43,560 --> 00:17:46,800 Speaker 1: a professional. This is her specialty. This is why we 337 00:17:46,880 --> 00:17:49,359 Speaker 1: hired her. Here's her reports. We don't love it because 338 00:17:49,359 --> 00:17:51,800 Speaker 1: you're calling us out for our mistakes, but the level 339 00:17:51,800 --> 00:17:54,600 Speaker 1: of narcissism that you would rather come out on top 340 00:17:54,640 --> 00:17:56,960 Speaker 1: and say I was right from the beginning than to 341 00:17:57,119 --> 00:18:00,600 Speaker 1: understand this is going to cost you way more money 342 00:18:00,640 --> 00:18:03,040 Speaker 1: in the end. So I'm just like, my mind is blown. 343 00:18:03,119 --> 00:18:05,879 Speaker 1: And how she became a villain to Google, like like 344 00:18:06,000 --> 00:18:08,879 Speaker 1: Google sees her as a villain in this situation, and 345 00:18:08,920 --> 00:18:11,560 Speaker 1: she had to be the one to say, hey, no, 346 00:18:11,720 --> 00:18:15,800 Speaker 1: that's not how that happened. Like it just absolutely baffled 347 00:18:15,840 --> 00:18:18,399 Speaker 1: me that that's where she had to be in And 348 00:18:18,760 --> 00:18:21,080 Speaker 1: the other alternative for them was she just leaves and 349 00:18:21,119 --> 00:18:23,399 Speaker 1: has someone cut else to come on. And maybe it 350 00:18:23,400 --> 00:18:25,320 Speaker 1: should be an obvious because it's very political in that 351 00:18:25,400 --> 00:18:28,560 Speaker 1: sense of we just want a yes person. Yeah, I 352 00:18:28,560 --> 00:18:31,639 Speaker 1: think that's I think. I mean, I'm as I'm like 353 00:18:31,720 --> 00:18:34,480 Speaker 1: nodding my head ferociously. I've been in a situation where 354 00:18:35,080 --> 00:18:40,080 Speaker 1: I think that it looks good to have an outspoken 355 00:18:40,440 --> 00:18:44,040 Speaker 1: critic in this space joined Google, or to have that 356 00:18:44,119 --> 00:18:46,400 Speaker 1: kind of coverage to say like, oh, well, of course 357 00:18:46,440 --> 00:18:48,680 Speaker 1: we're worried about We're we're concerned about this at Google. 358 00:18:48,760 --> 00:18:51,560 Speaker 1: We hired this person who is so outspoken. But then 359 00:18:51,640 --> 00:18:55,159 Speaker 1: when that person starts asking questions about the organization or 360 00:18:55,200 --> 00:19:00,200 Speaker 1: asking questions about Google's role in, you know, perpetuating things 361 00:19:00,200 --> 00:19:03,240 Speaker 1: that could be harmful. I think that's where people are like, oh, whoa, 362 00:19:03,440 --> 00:19:05,320 Speaker 1: we wanted you when it made us look good. We 363 00:19:05,400 --> 00:19:07,160 Speaker 1: don't want you if you're gonna make us look bad, 364 00:19:07,160 --> 00:19:10,760 Speaker 1: and like ask hard questions, you know, and got over 365 00:19:10,800 --> 00:19:13,280 Speaker 1: at Google, so like their side of the story. Jeff 366 00:19:13,280 --> 00:19:15,919 Speaker 1: Deane he shared Google side of the story, and he 367 00:19:16,000 --> 00:19:19,280 Speaker 1: said that uh, Jebuary and her colleagues only gave Google 368 00:19:19,320 --> 00:19:21,399 Speaker 1: AI a day to do an internal review of the 369 00:19:21,400 --> 00:19:23,280 Speaker 1: paper before they were going to submit it to a 370 00:19:23,320 --> 00:19:26,600 Speaker 1: conference for publication. And so he wrote quote, our aim 371 00:19:26,760 --> 00:19:29,240 Speaker 1: is to rival peer review journals in terms of the 372 00:19:29,359 --> 00:19:33,600 Speaker 1: rigor and thoughtfulness and how we review research before publication. However, 373 00:19:34,119 --> 00:19:38,160 Speaker 1: William Fitzgerald, a former Google PR manager, said no, no, no, no, no, 374 00:19:38,520 --> 00:19:41,240 Speaker 1: that's not true. Don't buy this. On Twitter, he said, 375 00:19:41,440 --> 00:19:43,480 Speaker 1: this is such a lie. It was part of my 376 00:19:43,600 --> 00:19:47,000 Speaker 1: job on the Google PR team to review these papers. Typically, 377 00:19:47,000 --> 00:19:49,000 Speaker 1: we got so many that we didn't even review them 378 00:19:49,000 --> 00:19:51,399 Speaker 1: in time, or a researcher would just published and we 379 00:19:51,400 --> 00:19:55,920 Speaker 1: wouldn't know until afterward. We all caps never punished people 380 00:19:56,000 --> 00:19:58,959 Speaker 1: for not doing proper process. So it does seem like 381 00:19:59,080 --> 00:20:01,280 Speaker 1: something is going on, right, Like, both of these things 382 00:20:01,320 --> 00:20:03,439 Speaker 1: can't be true. It can't be true that the only 383 00:20:03,480 --> 00:20:06,480 Speaker 1: issue was that we were concerned about the review process. 384 00:20:06,520 --> 00:20:08,719 Speaker 1: We didn't have enough time to review it, therefore we 385 00:20:08,720 --> 00:20:12,040 Speaker 1: weren't able to have them publish it. And that Google 386 00:20:12,320 --> 00:20:16,080 Speaker 1: never requires this kind of you know, this kind of 387 00:20:16,760 --> 00:20:20,040 Speaker 1: you know, intense review, yes, scrutiny, And it just really 388 00:20:20,160 --> 00:20:23,679 Speaker 1: makes me ask, you know, was she was her paper 389 00:20:23,760 --> 00:20:26,360 Speaker 1: getting a different kind of scrutiny than the other kinds 390 00:20:26,359 --> 00:20:29,920 Speaker 1: of paper that Google was putting out right? And and 391 00:20:29,960 --> 00:20:33,200 Speaker 1: it's particularly frustrating too, because we have heard of this 392 00:20:33,640 --> 00:20:39,680 Speaker 1: environment at Google of sexual harassment and toxicity on top 393 00:20:39,720 --> 00:20:42,679 Speaker 1: of all this, right, oh yeah, let me just be 394 00:20:42,720 --> 00:20:45,520 Speaker 1: really clear. Google has had a very big problem with 395 00:20:45,560 --> 00:20:48,960 Speaker 1: sexual harassment and sexual misconduct. And something that's even more 396 00:20:49,080 --> 00:20:52,840 Speaker 1: kind of disgusting is that sometimes they actually, in their 397 00:20:52,880 --> 00:20:56,840 Speaker 1: own words, gift the harassers huge sums of money. So 398 00:20:56,960 --> 00:21:00,560 Speaker 1: last year, more than Google employees around the World walked 399 00:21:00,560 --> 00:21:02,960 Speaker 1: out of the company's office to protest the fact that 400 00:21:03,000 --> 00:21:05,440 Speaker 1: Google had paid out over a hundred million dollars to 401 00:21:05,600 --> 00:21:09,159 Speaker 1: multiple executives accute of sexual harassment in the workplace. Google 402 00:21:09,160 --> 00:21:11,119 Speaker 1: paid out a total of a hundred and five million 403 00:21:11,160 --> 00:21:14,280 Speaker 1: dollars to Andy Reuben and Admit Signal after they were 404 00:21:14,320 --> 00:21:16,920 Speaker 1: accused of sexual harassment at the company. So this is 405 00:21:16,960 --> 00:21:19,480 Speaker 1: from tech Crunch. The suit, which was found by a shareholder, 406 00:21:19,720 --> 00:21:22,760 Speaker 1: James Martin, confirms that the board of directors approved a 407 00:21:22,880 --> 00:21:26,240 Speaker 1: ninety million dollar exit package for Reuben as a quote 408 00:21:26,359 --> 00:21:29,520 Speaker 1: goodbye present for him. No mention, of course, was made 409 00:21:29,560 --> 00:21:32,520 Speaker 1: about the true reason for his resignation, his egregious sexual 410 00:21:32,560 --> 00:21:36,359 Speaker 1: harassment while at Google. So it's interesting that you know, 411 00:21:36,840 --> 00:21:39,920 Speaker 1: if you if you write a paper and go through 412 00:21:39,960 --> 00:21:43,400 Speaker 1: our review process that Google doesn't like you are terminating 413 00:21:43,480 --> 00:21:47,720 Speaker 1: or you're you're forced out in this like wildly disrespectful way. 414 00:21:48,040 --> 00:21:51,159 Speaker 1: If you sexually harass your co workers, don't give you 415 00:21:51,280 --> 00:21:54,320 Speaker 1: millions of dollars as a gift. Well, that's the double 416 00:21:54,359 --> 00:21:59,120 Speaker 1: women once again that performed bullshit of them saying yes, 417 00:21:59,160 --> 00:22:01,159 Speaker 1: we definitely got rid of the person. We would not 418 00:22:01,240 --> 00:22:05,080 Speaker 1: stand for sexual harassers in our community. But we're gonna 419 00:22:05,119 --> 00:22:07,600 Speaker 1: give him lots of money, don't you know. We wasn't 420 00:22:07,600 --> 00:22:10,640 Speaker 1: just stn't like us. Like, that's that whole performative justice 421 00:22:10,720 --> 00:22:13,679 Speaker 1: that it's constant in these types of organizations. And for 422 00:22:13,800 --> 00:22:17,440 Speaker 1: those they hoped would remain silent, boy were they wrong. 423 00:22:17,560 --> 00:22:19,719 Speaker 1: Thank goodness, they were wrong. But they really thought they 424 00:22:19,720 --> 00:22:22,600 Speaker 1: could just silence people and be just move on with it. 425 00:22:22,720 --> 00:22:25,320 Speaker 1: And that's like the bigger picture of what she's done. 426 00:22:25,359 --> 00:22:27,760 Speaker 1: She's kind of like, uh no, on top of the 427 00:22:27,760 --> 00:22:29,480 Speaker 1: fact that the performative stuff is like we're going to 428 00:22:29,560 --> 00:22:31,400 Speaker 1: have a conference and I read this that they were 429 00:22:31,400 --> 00:22:35,520 Speaker 1: trying to have a zoom conference and have people answering 430 00:22:35,640 --> 00:22:37,760 Speaker 1: questions so you can all be on the same page 431 00:22:37,760 --> 00:22:40,840 Speaker 1: and just be, you know, really transparent without her presence, 432 00:22:40,880 --> 00:22:43,720 Speaker 1: so answering for the woman they fired, Like, that's just 433 00:22:43,760 --> 00:22:47,320 Speaker 1: a whole other level of how stupid can you get? 434 00:22:48,400 --> 00:22:54,080 Speaker 1: And trying to fix things? Yeah, I feel like everything 435 00:22:54,119 --> 00:22:57,200 Speaker 1: they've done has been a bit of a misstep, And honestly, 436 00:22:57,680 --> 00:23:00,840 Speaker 1: kudos to her or not, it's it would be so 437 00:23:01,000 --> 00:23:04,280 Speaker 1: it would be so much easier for her to be silent, 438 00:23:04,359 --> 00:23:06,800 Speaker 1: to not say anything, to not speak up and and honestly, 439 00:23:06,960 --> 00:23:11,080 Speaker 1: kudos to her for choosing a different path. And yeah, 440 00:23:11,119 --> 00:23:13,919 Speaker 1: so she actually addressed sort of exactly what you were 441 00:23:13,920 --> 00:23:16,640 Speaker 1: talking about on Twitter. She wrote, if you talk about 442 00:23:16,680 --> 00:23:19,880 Speaker 1: toxic workplace conditions, a lot of leaders will want you out. 443 00:23:20,160 --> 00:23:22,000 Speaker 1: If a lot of leaders want you out, they will 444 00:23:22,000 --> 00:23:24,240 Speaker 1: find a way to make it happen. If you're a harasser, 445 00:23:24,359 --> 00:23:26,199 Speaker 1: that's not the case. A lot of leaders will be 446 00:23:26,240 --> 00:23:28,760 Speaker 1: a okay with you being around. And I think that 447 00:23:28,920 --> 00:23:31,960 Speaker 1: really like just goes. I mean, that's the name of 448 00:23:31,960 --> 00:23:34,000 Speaker 1: the game. And I think like, I don't know if 449 00:23:34,000 --> 00:23:36,240 Speaker 1: you'll remember a few but back in the summer, when 450 00:23:36,560 --> 00:23:38,560 Speaker 1: so many different people were being pushed out of their 451 00:23:38,600 --> 00:23:40,880 Speaker 1: jobs because they were they had a history of racism 452 00:23:40,960 --> 00:23:43,200 Speaker 1: or sexism. Um, it seems like every day we woke 453 00:23:43,280 --> 00:23:45,520 Speaker 1: up and somebody else is getting fired. When those people 454 00:23:45,600 --> 00:23:48,160 Speaker 1: get resigned, I do think it's important to ask follow 455 00:23:48,200 --> 00:23:51,240 Speaker 1: up questions of well, they're resigning, but do they still 456 00:23:51,240 --> 00:23:53,439 Speaker 1: get stock options? Are they looks their resignation pack and 457 00:23:53,640 --> 00:23:55,919 Speaker 1: like are they getting are they what is their finance like? 458 00:23:56,200 --> 00:23:59,000 Speaker 1: Are you severing all financial ties with this person? And 459 00:23:59,040 --> 00:24:01,560 Speaker 1: what does that look like? So easy to see the 460 00:24:01,600 --> 00:24:06,840 Speaker 1: headline that's like, oh, racist boss is fired from company, 461 00:24:06,880 --> 00:24:09,600 Speaker 1: But then asking that question of well do they get 462 00:24:10,040 --> 00:24:11,800 Speaker 1: like what's our exit package look like? You know, do 463 00:24:11,840 --> 00:24:13,920 Speaker 1: they do? They do? They are they getting paid out 464 00:24:14,240 --> 00:24:16,919 Speaker 1: lots of money, money that could go to you know, 465 00:24:17,240 --> 00:24:19,639 Speaker 1: hiring more inclusive folks on your team, Like what does 466 00:24:19,680 --> 00:24:22,960 Speaker 1: it look like? I think really asking those questions is 467 00:24:23,000 --> 00:24:26,119 Speaker 1: really important, right And honestly, I feel like Google is 468 00:24:26,160 --> 00:24:30,040 Speaker 1: a prime example of saying they're gonna make change, and 469 00:24:30,080 --> 00:24:31,640 Speaker 1: this is where we were, you know what I mean, 470 00:24:31,680 --> 00:24:33,480 Speaker 1: Like if like from after Fergus and be like, we're 471 00:24:33,480 --> 00:24:36,080 Speaker 1: gonna make change Black lives matter, Absolutely we agree, and 472 00:24:36,119 --> 00:24:39,880 Speaker 1: here we are today too, nothing's really changed and we're 473 00:24:39,920 --> 00:24:42,479 Speaker 1: kind of sorry, but not really like that's it feels like. 474 00:24:42,520 --> 00:24:44,720 Speaker 1: I mean, we've been talking about this for a minute, um, 475 00:24:44,880 --> 00:24:47,440 Speaker 1: because you know, when we talk about voter suppression and 476 00:24:47,480 --> 00:24:50,240 Speaker 1: listening to black women, it's a concept focus and concept 477 00:24:50,240 --> 00:24:53,320 Speaker 1: of conversation for the last six months. But the fact 478 00:24:53,320 --> 00:24:55,560 Speaker 1: of the matter is just because we're seeing it now 479 00:24:55,600 --> 00:24:57,880 Speaker 1: doesn't mean things have changed. And we've this is kind 480 00:24:57,880 --> 00:25:00,000 Speaker 1: of like a replay almost like an election every four 481 00:25:00,040 --> 00:25:02,639 Speaker 1: should we come to this, it is still the same bs, 482 00:25:03,119 --> 00:25:05,440 Speaker 1: you know, and it's happening in technology as well. And 483 00:25:06,160 --> 00:25:08,480 Speaker 1: I can't remember what it was, but I just saw 484 00:25:08,520 --> 00:25:12,960 Speaker 1: the Pinterest art article lawsuit, the Pinterest lawsuit, and it's 485 00:25:12,960 --> 00:25:16,440 Speaker 1: like perfectly hand in hand in which the white CEO 486 00:25:16,760 --> 00:25:20,640 Speaker 1: woman came and filed on behalf of herself and two 487 00:25:20,720 --> 00:25:23,240 Speaker 1: specific black women who are part of the company and 488 00:25:23,280 --> 00:25:26,040 Speaker 1: talked about it, and she got awarded millions of dollars 489 00:25:26,240 --> 00:25:29,840 Speaker 1: and they got severance. They got it's exactly if they 490 00:25:29,840 --> 00:25:33,080 Speaker 1: got severance. And she is clear that these black women 491 00:25:33,119 --> 00:25:36,400 Speaker 1: who spoke up about being mistreated at Pinterest, they were 492 00:25:36,400 --> 00:25:38,159 Speaker 1: the ones who paved the way for her to be 493 00:25:38,200 --> 00:25:41,960 Speaker 1: able to file this suit successfully. And I do think 494 00:25:41,960 --> 00:25:45,240 Speaker 1: like keeping an eye on who gets a big payout 495 00:25:45,359 --> 00:25:47,960 Speaker 1: and who doesn't. And you know, I think we'll talk 496 00:25:47,960 --> 00:25:51,800 Speaker 1: about this um going forward in the episode. But you know, 497 00:25:52,760 --> 00:25:55,600 Speaker 1: it's so important, like I said earlier, it is so 498 00:25:55,680 --> 00:25:59,960 Speaker 1: important that people women speak up when they are mistreat 499 00:26:00,040 --> 00:26:02,919 Speaker 1: it that they don't that you know, we use our voice, 500 00:26:03,080 --> 00:26:06,400 Speaker 1: but we also cannot deny the very real fact that 501 00:26:06,720 --> 00:26:09,280 Speaker 1: doing that comes with a cost, you know. And it's 502 00:26:09,320 --> 00:26:12,240 Speaker 1: it's interesting to me who has to bear the brunch 503 00:26:12,240 --> 00:26:14,720 Speaker 1: of that cost, because we know that when you speak up, 504 00:26:14,720 --> 00:26:17,000 Speaker 1: when you blow the whistle, when you are honest about 505 00:26:17,040 --> 00:26:19,320 Speaker 1: something that's happening to you, when you're honest about in 506 00:26:19,440 --> 00:26:23,199 Speaker 1: work or anywhere, it's not it's not easy, and I 507 00:26:23,240 --> 00:26:28,000 Speaker 1: think it's harder for people who are already marginalized. You know, 508 00:26:29,280 --> 00:26:33,480 Speaker 1: the expectations are so low for those you know, privileged people, 509 00:26:33,520 --> 00:26:36,280 Speaker 1: and that that's the understanding, is that because the expectations 510 00:26:36,280 --> 00:26:39,880 Speaker 1: are so low, they're rewarded so quickly, so easily, and 511 00:26:39,920 --> 00:26:43,199 Speaker 1: that's in itself such a crime to me. Like I 512 00:26:43,200 --> 00:26:45,840 Speaker 1: would say like that because it's the most upsetting, Like 513 00:26:46,520 --> 00:26:50,679 Speaker 1: this is where these you know, social worker juvenal justice 514 00:26:50,720 --> 00:26:53,560 Speaker 1: person been like, I'm enraged, doesn't you know, because you 515 00:26:53,760 --> 00:26:56,159 Speaker 1: see what's happening, and there's nothing you can do because 516 00:26:56,520 --> 00:26:59,400 Speaker 1: it's a systemic thing. Once again, when we try to explain, 517 00:27:00,000 --> 00:27:03,360 Speaker 1: having to explain over and over again, why you sit here, 518 00:27:03,400 --> 00:27:06,520 Speaker 1: and you compliment the white men for doing the minimal 519 00:27:06,640 --> 00:27:12,080 Speaker 1: meaning I trust women, oh my god, you know what 520 00:27:12,119 --> 00:27:13,840 Speaker 1: I mean. And then the black women sitting out here 521 00:27:14,160 --> 00:27:17,160 Speaker 1: doing the work, going into the concept being a part 522 00:27:17,240 --> 00:27:21,560 Speaker 1: of the abuse and constant harassment. But they're like, but 523 00:27:21,600 --> 00:27:23,840 Speaker 1: they you know, the conversation is they kind of asked 524 00:27:23,880 --> 00:27:25,600 Speaker 1: for because you know, they're putting themselves in the limelight. 525 00:27:25,640 --> 00:27:28,160 Speaker 1: Like that's the conversation you have, Like, what the hell 526 00:27:28,280 --> 00:27:33,280 Speaker 1: is this conversation in itself? That we have to we 527 00:27:33,400 --> 00:27:35,960 Speaker 1: have these hurdles again. And I know this seems so simple, 528 00:27:36,280 --> 00:27:39,000 Speaker 1: the fact that that this is the level that we're 529 00:27:39,000 --> 00:27:42,120 Speaker 1: still at this point and people can't grasp it. I mean, 530 00:27:42,480 --> 00:27:44,639 Speaker 1: and it's the big question, So what does all of 531 00:27:44,720 --> 00:27:47,280 Speaker 1: this means, especially for women of color in tech? What 532 00:27:47,600 --> 00:27:50,520 Speaker 1: is and how do you unravel this? I'm getting really passionate. 533 00:27:50,560 --> 00:27:53,600 Speaker 1: Can you tell a lot of voice? I mean, I'm happy, 534 00:27:53,600 --> 00:27:56,040 Speaker 1: I am right there with your passion. I think that 535 00:27:56,560 --> 00:27:59,680 Speaker 1: we really need to have some tough conversations and the 536 00:27:59,760 --> 00:28:02,440 Speaker 1: kind of conversations that you're that you are sparking right now. 537 00:28:02,480 --> 00:28:04,919 Speaker 1: I think that we do need to ask, you know, 538 00:28:05,520 --> 00:28:08,640 Speaker 1: when someone who has more privileged steps up, are they 539 00:28:08,680 --> 00:28:10,840 Speaker 1: being rewarded in a different way than someone who is 540 00:28:10,920 --> 00:28:13,640 Speaker 1: less privileged? UM. I also think just the reality is 541 00:28:13,640 --> 00:28:16,399 Speaker 1: is that for a lot of women of color in 542 00:28:16,440 --> 00:28:19,760 Speaker 1: all workplaces, not just tech, this is a common story. UM. 543 00:28:19,800 --> 00:28:22,680 Speaker 1: I've definitely experienced. I've got experienced what she's going through, 544 00:28:22,680 --> 00:28:26,040 Speaker 1: but I've experienced similar things. There's this really really um 545 00:28:26,240 --> 00:28:28,879 Speaker 1: great comic or like graphic that I love that you 546 00:28:28,960 --> 00:28:31,600 Speaker 1: might have seen on Twitter called the quote problem woman 547 00:28:31,680 --> 00:28:34,280 Speaker 1: of Color in the Workplace that I think describes this 548 00:28:34,600 --> 00:28:37,000 Speaker 1: system that I've heard quite a bit about, where, you know, 549 00:28:37,040 --> 00:28:40,479 Speaker 1: a woman of color in the nonprofit space will get hired, 550 00:28:40,840 --> 00:28:42,920 Speaker 1: there's gonna be a brief honeymoon phase where everyone is 551 00:28:42,960 --> 00:28:45,400 Speaker 1: so excited that she's there, and then if she starts 552 00:28:45,480 --> 00:28:49,800 Speaker 1: calling out you know, negative things happening within the organization 553 00:28:49,880 --> 00:28:52,360 Speaker 1: or on the team, then she is sort of swiftly 554 00:28:52,640 --> 00:28:55,360 Speaker 1: pushed out. And I think that is such a common 555 00:28:55,880 --> 00:28:57,800 Speaker 1: that is a tale as old as time. I think 556 00:28:57,840 --> 00:29:00,640 Speaker 1: that we really need to have some tough conversation about, 557 00:29:01,160 --> 00:29:05,640 Speaker 1: you know, how we are how we are supporting women 558 00:29:05,680 --> 00:29:09,080 Speaker 1: of color in workplaces, because you know, there's just the 559 00:29:09,120 --> 00:29:13,480 Speaker 1: sprays I love. You know, Uh, Diversity is being is 560 00:29:13,520 --> 00:29:17,560 Speaker 1: being invited to a dance. Inclusion is being asked to 561 00:29:17,640 --> 00:29:20,440 Speaker 1: dance right, Like there's a different It's like having a 562 00:29:20,440 --> 00:29:23,280 Speaker 1: seat at the table is great, but it is not 563 00:29:23,400 --> 00:29:26,120 Speaker 1: enough if you can't use your voice, if you're not supported, 564 00:29:26,200 --> 00:29:28,240 Speaker 1: if when you do try to use your voice, you 565 00:29:28,280 --> 00:29:31,360 Speaker 1: are punished for it, you're fired for it, you're retaliated 566 00:29:31,360 --> 00:29:33,080 Speaker 1: against for it. And it doesn't even have to be 567 00:29:33,120 --> 00:29:35,640 Speaker 1: that explicit. It can just be a cult, a culture 568 00:29:36,000 --> 00:29:38,360 Speaker 1: that makes you feel boxed out and not supported. And 569 00:29:38,400 --> 00:29:40,800 Speaker 1: so all of these folks who are who who are 570 00:29:41,040 --> 00:29:45,080 Speaker 1: you know, ra ra gung ho about hiring women and 571 00:29:45,320 --> 00:29:48,120 Speaker 1: folks of color on their teams. Don't just hire them, 572 00:29:48,200 --> 00:29:50,920 Speaker 1: make sure they feel comfortable and supported to use their voice, 573 00:29:50,960 --> 00:29:52,880 Speaker 1: to speak up, to do their jobs. Make sure they 574 00:29:52,920 --> 00:29:55,600 Speaker 1: get the support and training and coaching they need, whatever 575 00:29:55,640 --> 00:29:57,400 Speaker 1: it is they need. Make sure they have the funding 576 00:29:57,440 --> 00:30:00,200 Speaker 1: they need. Just thinking that you that because you have 577 00:30:00,840 --> 00:30:03,320 Speaker 1: black folks, are women, are folks of color at the table, 578 00:30:03,640 --> 00:30:06,040 Speaker 1: that that's all that it ends there, because it certainly 579 00:30:06,040 --> 00:30:09,800 Speaker 1: does not, and that's tokenism, so we won't go into that. 580 00:30:09,840 --> 00:30:12,440 Speaker 1: But like, yeah, I was just thinking of all of 581 00:30:12,480 --> 00:30:15,360 Speaker 1: the times because when you talk about the cartoon, the 582 00:30:15,600 --> 00:30:18,479 Speaker 1: graphic that you were talking about. I don't know why, 583 00:30:18,560 --> 00:30:21,440 Speaker 1: but it came to mind about the Fresh Prince of 584 00:30:21,440 --> 00:30:23,840 Speaker 1: bel Air, the reunion and which I had the old 585 00:30:23,920 --> 00:30:26,520 Speaker 1: ant viv On and she had that conversation. It was 586 00:30:26,560 --> 00:30:30,360 Speaker 1: like you labeled me a difficult black woman, and that 587 00:30:30,600 --> 00:30:34,640 Speaker 1: literally cast me out and like xed me out and like, 588 00:30:34,720 --> 00:30:36,960 Speaker 1: yeah right, I know you feel it, But the thing 589 00:30:37,040 --> 00:30:38,640 Speaker 1: is like, but that's the truth of it all. And 590 00:30:38,680 --> 00:30:42,160 Speaker 1: I've seen this happen many times, especially in my industry 591 00:30:42,240 --> 00:30:44,880 Speaker 1: where I was working in the department Junal Justice, which 592 00:30:44,880 --> 00:30:49,320 Speaker 1: is like majority black community, because that is who they are. 593 00:30:49,360 --> 00:30:51,400 Speaker 1: They want to care for their people, and they know 594 00:30:51,480 --> 00:30:54,160 Speaker 1: those are the ones that are being ostracized to be 595 00:30:54,240 --> 00:30:57,640 Speaker 1: in that justice system, like being abused within that justice system. 596 00:30:57,640 --> 00:31:01,160 Speaker 1: So that's what I've seen, and just watching the language 597 00:31:01,160 --> 00:31:04,720 Speaker 1: and people talking about other black women and the little 598 00:31:04,800 --> 00:31:07,240 Speaker 1: keywords what they're really truly meaning to say, and how 599 00:31:07,240 --> 00:31:12,040 Speaker 1: it ostracizes and blacklists individuals, and that's exactly what's happening 600 00:31:12,080 --> 00:31:15,680 Speaker 1: in the industries as well. She became difficult quote unquote 601 00:31:16,120 --> 00:31:19,320 Speaker 1: because she had questions or because she wanted to accountability, 602 00:31:19,520 --> 00:31:22,920 Speaker 1: and it's such a thing that's like, it's mind blowing 603 00:31:23,000 --> 00:31:25,680 Speaker 1: to me that people can't recognize it immediately. Of course, 604 00:31:25,840 --> 00:31:29,880 Speaker 1: I say this because it is the internalized racism that 605 00:31:29,920 --> 00:31:32,360 Speaker 1: they cannot get let go of. And then the misogyn 606 00:31:32,360 --> 00:31:35,600 Speaker 1: noir of it all is that they're not having support 607 00:31:35,680 --> 00:31:40,600 Speaker 1: from their own community at times. Absolutely, I this would 608 00:31:40,600 --> 00:31:42,360 Speaker 1: be this would be like a two hour long podcast. 609 00:31:42,520 --> 00:31:45,560 Speaker 1: First of all, yes to everything you said about Janet 610 00:31:45,600 --> 00:31:48,320 Speaker 1: Huber a k a. The first mom on Fresh Prince 611 00:31:48,320 --> 00:31:52,120 Speaker 1: of bel Air, Justice for Janet Huber. Exactly right. You know, 612 00:31:52,720 --> 00:31:55,680 Speaker 1: I would never call another woman of There's certain words 613 00:31:55,720 --> 00:31:57,680 Speaker 1: I would never use for another woman of color in public. Right, 614 00:31:57,720 --> 00:31:59,720 Speaker 1: I would never call another woman of color aggressive, because 615 00:31:59,760 --> 00:32:04,240 Speaker 1: I just know how how that language is weaponized against us, 616 00:32:04,280 --> 00:32:06,600 Speaker 1: so that what we have to say, or like what 617 00:32:06,640 --> 00:32:08,680 Speaker 1: we have to say no longer matters, right, Like we 618 00:32:08,720 --> 00:32:11,920 Speaker 1: are no longer a three dimensional human being. We are 619 00:32:11,960 --> 00:32:14,520 Speaker 1: a caricature. And so there are certain things that I 620 00:32:14,520 --> 00:32:17,280 Speaker 1: would I would never you know, call another woman of 621 00:32:17,280 --> 00:32:19,200 Speaker 1: color in public, because it's just like, it's not it's 622 00:32:19,240 --> 00:32:24,800 Speaker 1: it's it's it is creating a it's creating a situation 623 00:32:24,800 --> 00:32:27,440 Speaker 1: where she can't win, Like she's no, like it doesn't 624 00:32:27,440 --> 00:32:29,200 Speaker 1: matter what she says, She'll never be able to win. It. 625 00:32:29,200 --> 00:32:33,360 Speaker 1: That sounds exactly what's happening at Google. You know. Uh 626 00:32:33,440 --> 00:32:37,160 Speaker 1: Jebry said that she you know, Google sort of apologized 627 00:32:37,200 --> 00:32:39,479 Speaker 1: for firing her and a company wide memo kind of, 628 00:32:39,760 --> 00:32:41,640 Speaker 1: but she's completely not buying it. And she says that 629 00:32:41,680 --> 00:32:44,280 Speaker 1: they're trying to paint her out to be an angry 630 00:32:44,280 --> 00:32:47,080 Speaker 1: black woman. She said, they paint me as this angry 631 00:32:47,080 --> 00:32:49,440 Speaker 1: black woman because they put you in this terrible workplace 632 00:32:49,480 --> 00:32:51,400 Speaker 1: and if you speak up about it, then you become 633 00:32:51,440 --> 00:32:54,719 Speaker 1: a problem. And then they start talking about de escalation strategies. 634 00:32:54,880 --> 00:32:57,240 Speaker 1: You write emails, they get ignored. You write documents, they 635 00:32:57,240 --> 00:32:59,440 Speaker 1: get ignored. You discuss how it's being done, and they 636 00:32:59,480 --> 00:33:01,040 Speaker 1: talk about you as if you're some kind of angry 637 00:33:01,080 --> 00:33:04,280 Speaker 1: black woman who needs to be contained. And I've been there, 638 00:33:04,360 --> 00:33:07,800 Speaker 1: like once someone writes you off as an angry black 639 00:33:07,840 --> 00:33:14,040 Speaker 1: woman or aggressive or you know any number of terms 640 00:33:14,080 --> 00:33:16,200 Speaker 1: that we have to diminish women, and what they say 641 00:33:16,680 --> 00:33:18,520 Speaker 1: people can't see you. It's like it's like it's like 642 00:33:18,560 --> 00:33:22,080 Speaker 1: it it completely muzzles anything that you could possibly ever say. 643 00:33:22,160 --> 00:33:24,600 Speaker 1: She's like, no one is ever able to take you 644 00:33:24,960 --> 00:33:27,600 Speaker 1: seriously again, And I think that's exactly what she's calling 645 00:33:27,600 --> 00:33:30,720 Speaker 1: out here. And even even in so calling it out, 646 00:33:31,080 --> 00:33:34,080 Speaker 1: it's a huge risk because it's like, oh, well, she's 647 00:33:34,320 --> 00:33:36,640 Speaker 1: they apologized, Like what else does what else does she want? 648 00:33:36,680 --> 00:33:38,560 Speaker 1: Like she she won't be satisfied with anything, you know. 649 00:33:38,560 --> 00:33:42,000 Speaker 1: It's like it's such a vicious cycle. Oh and talking 650 00:33:42,000 --> 00:33:44,400 Speaker 1: about like her being pushed out, she wasn't even pushed up. 651 00:33:44,400 --> 00:33:48,000 Speaker 1: They said she resigned, which was a complete falsehood from Jump. 652 00:33:48,040 --> 00:33:49,720 Speaker 1: Like that was my favorite part of this is like 653 00:33:50,040 --> 00:33:53,280 Speaker 1: they said she resigned whatever, and she's like, no, you 654 00:33:53,400 --> 00:33:56,840 Speaker 1: pushed me out, like you essentially fired me on again, 655 00:33:56,880 --> 00:33:59,720 Speaker 1: as you said, the most disrespectful way that could possibly 656 00:33:59,760 --> 00:34:02,560 Speaker 1: have happened, by just cutting me out of the system 657 00:34:02,760 --> 00:34:05,920 Speaker 1: without talking about negotiations, without talking about terms, without talking 658 00:34:05,920 --> 00:34:08,239 Speaker 1: about any of those things. And it's such a whole thing, 659 00:34:08,239 --> 00:34:09,640 Speaker 1: and then trying to turn it around and say, no, 660 00:34:09,880 --> 00:34:12,279 Speaker 1: but she quit, We didn't do it. She did it 661 00:34:12,520 --> 00:34:15,799 Speaker 1: like like like she turned the system off herself. It's 662 00:34:15,800 --> 00:34:18,760 Speaker 1: just kind of this whole level of gas lighting within 663 00:34:18,840 --> 00:34:22,040 Speaker 1: a multi billion dollar company that is like, are you 664 00:34:22,160 --> 00:34:24,680 Speaker 1: kidding me? Are you at twenty five year old boyfriend 665 00:34:24,719 --> 00:34:29,839 Speaker 1: that's trying to run away? Google the twenty five year 666 00:34:29,840 --> 00:34:35,760 Speaker 1: old boyfriend, Sorry babe, that it's really bad at reading texts. 667 00:34:37,440 --> 00:34:43,919 Speaker 1: We had that way friend. We do have a little 668 00:34:43,920 --> 00:34:45,520 Speaker 1: bit more for you listeners, but first we have a 669 00:34:45,520 --> 00:34:47,200 Speaker 1: one more cup break for a word from our sponsor, 670 00:35:01,320 --> 00:35:04,520 Speaker 1: and we're back. Thank you, sposing. So, how can we 671 00:35:04,560 --> 00:35:08,319 Speaker 1: take j bruse experience here as a black woman in 672 00:35:08,360 --> 00:35:12,359 Speaker 1: technology and extrapolate that out. That's a great question. So 673 00:35:12,440 --> 00:35:15,160 Speaker 1: one of the things I talked about a little bit earlier, 674 00:35:15,160 --> 00:35:17,200 Speaker 1: but I just want to make sure it's like super clear, 675 00:35:17,400 --> 00:35:20,720 Speaker 1: is that I think what's happening to her is really awful. 676 00:35:21,080 --> 00:35:23,960 Speaker 1: It's not just awful because Google is disrespecting somebody who 677 00:35:24,080 --> 00:35:26,359 Speaker 1: is like really respected in the AI space, but it's 678 00:35:26,360 --> 00:35:29,800 Speaker 1: bad for everybody. You know, that hostility toward not just 679 00:35:29,880 --> 00:35:32,080 Speaker 1: black women, but I think a lot of underrepresented people 680 00:35:32,239 --> 00:35:34,640 Speaker 1: that I think is at the core of tech has 681 00:35:34,800 --> 00:35:37,799 Speaker 1: negative implications for all of us. Because technology that's use 682 00:35:37,880 --> 00:35:39,960 Speaker 1: for everybody should be built by everybody. And so when 683 00:35:39,960 --> 00:35:43,600 Speaker 1: tech companies are hostile to underrepresented voices, when they push 684 00:35:43,680 --> 00:35:46,840 Speaker 1: underrepresented voices out and don't support them, that means fewer 685 00:35:46,840 --> 00:35:48,440 Speaker 1: of those voices are going to go on to shape 686 00:35:48,440 --> 00:35:51,320 Speaker 1: the technology and the platforms and the systems that really 687 00:35:51,560 --> 00:35:54,280 Speaker 1: and truly do run our world. It's like the systems 688 00:35:54,280 --> 00:35:57,239 Speaker 1: that meet that like help you get your Uber or 689 00:35:57,280 --> 00:35:59,360 Speaker 1: like get your instant cart, groceries to your house, to 690 00:35:59,440 --> 00:36:01,759 Speaker 1: the systems that you know surveil you when you go 691 00:36:01,800 --> 00:36:04,440 Speaker 1: outside of your house. You know, these systems are built 692 00:36:04,480 --> 00:36:07,200 Speaker 1: by people, right, And so we like to believe this 693 00:36:07,280 --> 00:36:11,600 Speaker 1: fairy tale that technology is neutral, but technology that is 694 00:36:11,640 --> 00:36:14,479 Speaker 1: built by people can't be neutral. And so the less 695 00:36:14,560 --> 00:36:18,480 Speaker 1: people who are who are supported and are retained in 696 00:36:18,480 --> 00:36:21,080 Speaker 1: the technology space, the less people we will have working 697 00:36:21,120 --> 00:36:24,560 Speaker 1: on these technologies. And like I said, you know, at 698 00:36:24,600 --> 00:36:27,920 Speaker 1: the at best, if these systems are not built for us, 699 00:36:27,960 --> 00:36:31,080 Speaker 1: for everybody, at best they might annoy you, like those 700 00:36:31,120 --> 00:36:33,520 Speaker 1: you know, faucets that don't work for me in the airport. 701 00:36:33,760 --> 00:36:37,480 Speaker 1: But at worst, they can really perpetuate harm on communities 702 00:36:37,480 --> 00:36:40,280 Speaker 1: that are already marginalized. And so I always tell people 703 00:36:40,280 --> 00:36:42,560 Speaker 1: this that it's not just we don't need to build 704 00:36:42,600 --> 00:36:45,239 Speaker 1: inclusive teams in tech because it's nice and nice thing 705 00:36:45,280 --> 00:36:47,880 Speaker 1: to do. We need to build it because it's critical. 706 00:36:48,000 --> 00:36:51,760 Speaker 1: Because this technology really does have the power to drive 707 00:36:51,880 --> 00:36:53,719 Speaker 1: our world. And so if it's not being built with 708 00:36:53,719 --> 00:36:56,719 Speaker 1: everybody in mind, you know what kind of harm is 709 00:36:56,719 --> 00:36:59,600 Speaker 1: is it perpetuating? And so really kind of getting clear 710 00:36:59,680 --> 00:37:02,960 Speaker 1: on at I think it's really key, and also just 711 00:37:03,000 --> 00:37:06,919 Speaker 1: sort of speaking about um what I was talking about 712 00:37:06,960 --> 00:37:10,560 Speaker 1: earlier about how you know there's a cost to being 713 00:37:10,640 --> 00:37:14,239 Speaker 1: the woman who speaks up. I really applaud anybody who 714 00:37:14,320 --> 00:37:16,719 Speaker 1: whistle blows out an organization or goes against the grand 715 00:37:16,719 --> 00:37:19,720 Speaker 1: at the organization when something when something bad is happening, 716 00:37:19,960 --> 00:37:23,399 Speaker 1: but that those things cannot go without a cost. And 717 00:37:23,760 --> 00:37:27,719 Speaker 1: I think for women like Tim net Debreu. You know 718 00:37:28,120 --> 00:37:32,040 Speaker 1: she is no longer employed, right and so um One 719 00:37:32,040 --> 00:37:35,719 Speaker 1: of my mentors in tech Sabrina Hersysa she runs She's 720 00:37:35,760 --> 00:37:38,759 Speaker 1: a human rights technologist and a complete and total badass, 721 00:37:39,320 --> 00:37:42,279 Speaker 1: but she runs an organization called b Bold Media, and 722 00:37:42,440 --> 00:37:45,080 Speaker 1: she has started this thing called the Bold Prize where 723 00:37:45,239 --> 00:37:49,960 Speaker 1: she is trying to give monetary gifts to women who 724 00:37:50,040 --> 00:37:53,200 Speaker 1: are pushed out of organizations for things like whistle blowing. UM. 725 00:37:53,239 --> 00:37:56,320 Speaker 1: There's a really powerful story about how she got started 726 00:37:56,360 --> 00:37:59,279 Speaker 1: doing this work with the with the Bold Prize. If 727 00:37:59,320 --> 00:38:01,080 Speaker 1: you are interest did there's a great episode of my 728 00:38:01,120 --> 00:38:03,319 Speaker 1: podcast There are no girls on the Internet all about it. 729 00:38:03,600 --> 00:38:07,600 Speaker 1: But essentially, way back when when UM M I T 730 00:38:08,280 --> 00:38:12,000 Speaker 1: was found to have this uh financial relationship with Jeffrey Epstein, 731 00:38:12,000 --> 00:38:15,480 Speaker 1: who we know was a serial sexual abuser at the time, 732 00:38:15,960 --> 00:38:18,200 Speaker 1: M I T was giving out this prize called the 733 00:38:18,239 --> 00:38:20,880 Speaker 1: Disobedience Award, right, and so this was a surprise that 734 00:38:20,960 --> 00:38:25,000 Speaker 1: was given to people in tech who break the rules, 735 00:38:25,000 --> 00:38:29,399 Speaker 1: who are courageous, who disobey and you know, one year 736 00:38:29,520 --> 00:38:32,000 Speaker 1: they gave it to the creators of Me Too in 737 00:38:32,160 --> 00:38:35,239 Speaker 1: STEM who you know, we're calling out sexual harassment in 738 00:38:35,320 --> 00:38:38,839 Speaker 1: technology and science. And so after this whole thing came 739 00:38:38,840 --> 00:38:41,560 Speaker 1: out with the financial relationship that m I T had 740 00:38:41,600 --> 00:38:44,800 Speaker 1: with Jeffrey Epstein, Sabrina thought, well, why should m I 741 00:38:44,880 --> 00:38:47,320 Speaker 1: T get to be the arbiters of who has courage 742 00:38:47,320 --> 00:38:50,759 Speaker 1: and who doesn't. They didn't act very courage courageous, they 743 00:38:50,760 --> 00:38:53,160 Speaker 1: don't They shouldn't be the ones who are deciding, you know, 744 00:38:53,200 --> 00:38:55,440 Speaker 1: who is acting morally and who is not. And so 745 00:38:55,880 --> 00:39:00,040 Speaker 1: she started uh the Bold Prize as a rival the 746 00:39:00,080 --> 00:39:02,799 Speaker 1: Disobedience Awards, so that you know, we didn't have to 747 00:39:02,800 --> 00:39:06,560 Speaker 1: only see organizations that perpetuate harm and and perpetuate the 748 00:39:06,600 --> 00:39:08,719 Speaker 1: status quo as the ones who were the also the 749 00:39:08,840 --> 00:39:11,000 Speaker 1: arbiters of who was courageous and who was it. And 750 00:39:11,040 --> 00:39:14,160 Speaker 1: so this year's recipient, one of them is Tim net Jabru. 751 00:39:14,280 --> 00:39:17,719 Speaker 1: And so if you want to donate money to this 752 00:39:17,760 --> 00:39:20,919 Speaker 1: crowdfunding campaign. You can go to bold Prize dot com. 753 00:39:20,960 --> 00:39:23,080 Speaker 1: I've already given money. I think it's a great cause, 754 00:39:23,360 --> 00:39:26,799 Speaker 1: and I think it also just highlights the fact that 755 00:39:26,800 --> 00:39:29,400 Speaker 1: when you are someone who is a trailblazer or you 756 00:39:29,680 --> 00:39:34,440 Speaker 1: practice public courage or public you know, public boldness, it 757 00:39:34,560 --> 00:39:37,520 Speaker 1: sometimes comes with a financial cost. And it shouldn't write 758 00:39:37,520 --> 00:39:39,520 Speaker 1: It shouldn't be this hard for people to do the 759 00:39:39,600 --> 00:39:41,959 Speaker 1: right thing right. I mean, essentially this could have cost 760 00:39:41,960 --> 00:39:43,960 Speaker 1: her career has she not spoken up. It was just 761 00:39:44,080 --> 00:39:46,719 Speaker 1: told she was wrong and her data was wrong. That 762 00:39:46,719 --> 00:39:50,160 Speaker 1: could have cost her so much credibility in itself. And 763 00:39:50,200 --> 00:39:52,320 Speaker 1: I love this in m T, who has a history 764 00:39:52,320 --> 00:39:55,520 Speaker 1: of being sexist and racist in their own like college 765 00:39:55,520 --> 00:39:58,440 Speaker 1: admissions and stuff that I'm like, really, you're the one 766 00:39:58,440 --> 00:40:03,120 Speaker 1: who's gonna be the well, you know, i' traitor of morality. Okay, 767 00:40:03,480 --> 00:40:06,279 Speaker 1: why should we accept that? That's like, like you guys, 768 00:40:06,360 --> 00:40:09,120 Speaker 1: don't you don't know why should we trust you to 769 00:40:09,200 --> 00:40:11,759 Speaker 1: say like, oh, these people are courageous, give them money. 770 00:40:11,760 --> 00:40:13,880 Speaker 1: It's like obvious you can't be trusted in that department. 771 00:40:14,320 --> 00:40:16,360 Speaker 1: I just think it's funny and I think like also 772 00:40:16,520 --> 00:40:19,680 Speaker 1: just reading through her comments and the people commenting on her, 773 00:40:19,719 --> 00:40:22,839 Speaker 1: and the people feeling the freedom to tell her thank 774 00:40:22,880 --> 00:40:25,960 Speaker 1: you for those who she actually personally helped at Google, 775 00:40:26,200 --> 00:40:29,000 Speaker 1: or those just hearing her stories and feeling seen like 776 00:40:29,160 --> 00:40:31,920 Speaker 1: she has opened up so much more, which is always 777 00:40:32,800 --> 00:40:35,160 Speaker 1: the good side, the good field side, of course, but 778 00:40:35,200 --> 00:40:37,799 Speaker 1: then the again, the financial costs, the credibility, what could 779 00:40:37,840 --> 00:40:39,840 Speaker 1: have happened, what can't happen, the fact that she's jobless, 780 00:40:40,000 --> 00:40:43,560 Speaker 1: all these things. It doesn't necessarily worry that out, but 781 00:40:43,640 --> 00:40:46,520 Speaker 1: it was incredible to see the influence that she has 782 00:40:47,000 --> 00:40:50,640 Speaker 1: with people all over the world who has been through 783 00:40:50,680 --> 00:40:53,359 Speaker 1: similar situations at a smaller scale or maybe a bitter scale, 784 00:40:53,400 --> 00:40:56,960 Speaker 1: but they were just quiet that they didn't have the courage, 785 00:40:57,040 --> 00:41:00,400 Speaker 1: or they didn't have the fortitude to come forward, or 786 00:41:00,400 --> 00:41:04,160 Speaker 1: maybe they just didn't have like like I think at 787 00:41:04,160 --> 00:41:06,520 Speaker 1: certain points it's not necessarily that you're just being courageous. 788 00:41:06,640 --> 00:41:10,319 Speaker 1: You just are like fed the US when it comes 789 00:41:10,360 --> 00:41:14,360 Speaker 1: down to like I'm tired, I'm done, Nah, exactly exactly, 790 00:41:14,400 --> 00:41:15,960 Speaker 1: and I think, you know, you hit the nail on 791 00:41:16,000 --> 00:41:18,360 Speaker 1: the head. I think that sometimes it just goes to 792 00:41:18,360 --> 00:41:22,399 Speaker 1: show that like when like systems might fail us, institutions 793 00:41:22,480 --> 00:41:24,399 Speaker 1: might fail us, but really we are all we have, 794 00:41:24,520 --> 00:41:27,520 Speaker 1: you know, community other women. Those have been the things 795 00:41:27,520 --> 00:41:29,960 Speaker 1: that really sustained me. And it's it's not surprising to 796 00:41:30,000 --> 00:41:33,759 Speaker 1: me that as these institutions continue to fail women, Um, 797 00:41:33,800 --> 00:41:36,560 Speaker 1: we have each other's bath, right, And I think, like exactly, 798 00:41:36,600 --> 00:41:39,879 Speaker 1: and yeah, maybe maybe people who speak up it's not 799 00:41:39,960 --> 00:41:43,000 Speaker 1: a calculated courageous thing. Maybe you're just like, you know what, 800 00:41:43,040 --> 00:41:48,520 Speaker 1: I got time today. You know I got something to say. Yes, 801 00:41:49,560 --> 00:41:52,759 Speaker 1: I love it. Um, Are there any other resources you 802 00:41:52,760 --> 00:41:55,120 Speaker 1: want to shout out or anything else you wanna end 803 00:41:55,160 --> 00:41:57,319 Speaker 1: on before we wrap up? I would just say, like, 804 00:41:57,400 --> 00:42:00,080 Speaker 1: please go to bowld price dot com if you're interested 805 00:42:00,120 --> 00:42:03,080 Speaker 1: in helping out with the crowdfunding campaign for Bold Prize, 806 00:42:03,080 --> 00:42:05,400 Speaker 1: and if you're interested in more information, check out there 807 00:42:05,400 --> 00:42:08,560 Speaker 1: are no girls on the Internet. The episode about UM 808 00:42:08,680 --> 00:42:13,320 Speaker 1: Jeffrey Epstein and Ottawa BOYA fun fact most people credit 809 00:42:14,000 --> 00:42:18,480 Speaker 1: Rodan Pharaoh with being the reason why Jeffrey Epstein's connection 810 00:42:18,560 --> 00:42:21,600 Speaker 1: to M I T was, you know, eventually publicized. Not 811 00:42:21,760 --> 00:42:24,800 Speaker 1: so he did a great service in publishing, but Ottawa 812 00:42:24,840 --> 00:42:27,040 Speaker 1: and Boya actually published at first. He was just a 813 00:42:27,120 --> 00:42:30,040 Speaker 1: first year grad student at m I T. So typically 814 00:42:30,040 --> 00:42:33,239 Speaker 1: there is an where there's an interesting man doing something 815 00:42:33,239 --> 00:42:35,919 Speaker 1: courageous if you look hard enough, sometimes as a woman 816 00:42:35,960 --> 00:42:39,880 Speaker 1: who did it first but silenced and didn't get the 817 00:42:39,880 --> 00:42:44,600 Speaker 1: credit exactly. Yes, yes, all about giving women credit here. 818 00:42:45,239 --> 00:42:47,960 Speaker 1: Um well, thank you as always for joining us. Bridget 819 00:42:48,040 --> 00:42:50,080 Speaker 1: or where can the good listeners find you? You can 820 00:42:50,120 --> 00:42:52,680 Speaker 1: find me on Twitter at Bridget Marie or on Instagram 821 00:42:52,680 --> 00:42:55,799 Speaker 1: at Bridget Marie and d C. Yes, and definitely go 822 00:42:55,920 --> 00:43:00,400 Speaker 1: check out Bridge podcast and check her out online. Always 823 00:43:00,440 --> 00:43:03,200 Speaker 1: getting the information from you. You keep me up to 824 00:43:03,320 --> 00:43:06,360 Speaker 1: date on things, so I really do appreciate the fire. 825 00:43:06,400 --> 00:43:08,560 Speaker 1: I'm like, yeah, I'm ready, let's go. Yes, let's burn 826 00:43:08,560 --> 00:43:15,280 Speaker 1: it down. Yes, I can't wait for the next episode. 827 00:43:16,200 --> 00:43:18,040 Speaker 1: If you would like to email as you can or 828 00:43:18,080 --> 00:43:20,239 Speaker 1: email as Stuff Media mom Stuff at ihart me dot com. 829 00:43:20,239 --> 00:43:21,960 Speaker 1: You can also find us on Instagram at Stuff I've 830 00:43:22,000 --> 00:43:24,080 Speaker 1: Never Told You are on Twitter at mom Stuff Podcast. Thanks, 831 00:43:24,120 --> 00:43:27,640 Speaker 1: It's always started. Super producer Andrew Howards, Thanks to you 832 00:43:27,680 --> 00:43:29,600 Speaker 1: for listening Stuff I Never Told You. The production of 833 00:43:29,640 --> 00:43:31,600 Speaker 1: I Heart Radio. For more podcast from My Heart Radio 834 00:43:31,719 --> 00:43:33,680 Speaker 1: is i Heeart Radio app, Apple podcast or wherever you 835 00:43:33,680 --> 00:43:34,680 Speaker 1: listen to your favorite shows