1 00:00:04,440 --> 00:00:12,319 Speaker 1: Welcome to tech Stuff, a production from iHeartRadio. Hey there, 2 00:00:12,320 --> 00:00:15,880 Speaker 1: and welcome to tech Stuff. I'm your host, Jonathan Strickland. 3 00:00:15,920 --> 00:00:19,000 Speaker 1: I'm an executive producer with iHeart Podcasts and how the 4 00:00:19,079 --> 00:00:22,720 Speaker 1: tech are you. So there are a couple of related 5 00:00:22,760 --> 00:00:25,880 Speaker 1: ideas that I wanted to talk about today that are 6 00:00:25,880 --> 00:00:29,800 Speaker 1: important not just in tech, but obviously beyond. And those 7 00:00:29,840 --> 00:00:34,199 Speaker 1: ideas are the concepts behind open source projects, as well 8 00:00:34,240 --> 00:00:38,080 Speaker 1: as the importance of diversity and representation. Beyond that, I 9 00:00:38,120 --> 00:00:40,519 Speaker 1: want to talk about a word I really don't like. 10 00:00:41,520 --> 00:00:45,960 Speaker 1: The word is optics, not the study of light, but 11 00:00:46,120 --> 00:00:51,120 Speaker 1: rather how people perceive something like when you are concerned 12 00:00:51,120 --> 00:00:55,640 Speaker 1: with the optics of a situation. And it's an understandable thing, 13 00:00:56,040 --> 00:00:59,160 Speaker 1: but I feel like it takes prominence in a way 14 00:00:59,200 --> 00:01:03,000 Speaker 1: that is ultimately harmful in a lot of situations. Now, 15 00:01:03,040 --> 00:01:04,720 Speaker 1: the reason I want to talk about all of this 16 00:01:04,840 --> 00:01:07,480 Speaker 1: actually has to do with a really bonker story in 17 00:01:07,560 --> 00:01:11,399 Speaker 1: tech right now. It's about a software developer conference that 18 00:01:11,480 --> 00:01:14,280 Speaker 1: now has been canceled entirely at least for this year, 19 00:01:15,000 --> 00:01:17,840 Speaker 1: due to what appears to be a misguided attempt to 20 00:01:17,880 --> 00:01:22,679 Speaker 1: make the conference lineup seem more diverse. So before we 21 00:01:22,720 --> 00:01:25,080 Speaker 1: get into the whole diversity issue. I want to talk 22 00:01:25,080 --> 00:01:29,200 Speaker 1: a little bit about the concept of open source. Christine 23 00:01:29,240 --> 00:01:31,960 Speaker 1: Peterson gets the credit for coming up with the term 24 00:01:32,080 --> 00:01:36,520 Speaker 1: open source on February third, nineteen ninety eight. The idea, however, 25 00:01:36,680 --> 00:01:40,440 Speaker 1: is actually much older than that. In fact, before that 26 00:01:40,720 --> 00:01:44,399 Speaker 1: it was called sort of free software, but that made 27 00:01:44,400 --> 00:01:47,280 Speaker 1: people think of just software that was made so that 28 00:01:47,319 --> 00:01:50,040 Speaker 1: anyone could get the software right, they wouldn't have to 29 00:01:50,040 --> 00:01:52,320 Speaker 1: pay for it. Free software, though, was meant to be 30 00:01:53,040 --> 00:01:57,640 Speaker 1: this concept about people freely being able to access everything 31 00:01:57,760 --> 00:02:00,680 Speaker 1: about the software, not just the program itself, but it's 32 00:02:00,800 --> 00:02:03,760 Speaker 1: source code anyway. The basic idea is that an open 33 00:02:03,800 --> 00:02:08,440 Speaker 1: source project is one that is publicly viewable and modifiable. 34 00:02:08,960 --> 00:02:12,800 Speaker 1: Open source projects don't have to exclusively be about software, 35 00:02:13,080 --> 00:02:16,680 Speaker 1: but that's kind of where the concept grew out of. Also, 36 00:02:17,400 --> 00:02:20,480 Speaker 1: with a true open source project, this isn't the case 37 00:02:20,520 --> 00:02:23,080 Speaker 1: for every single one. There are different variations of these, 38 00:02:23,120 --> 00:02:27,000 Speaker 1: but with one that's truly open source, anyone can distribute it. 39 00:02:27,480 --> 00:02:31,239 Speaker 1: So you take the code and then you can distribute 40 00:02:31,280 --> 00:02:34,120 Speaker 1: the code as a program. You can modify it and 41 00:02:34,160 --> 00:02:38,080 Speaker 1: distribute your variation of that program. There are lots of 42 00:02:38,160 --> 00:02:42,160 Speaker 1: different examples of this. Linux is a great example of 43 00:02:42,200 --> 00:02:46,480 Speaker 1: this anyway. It's a sort of communal approach to software development, 44 00:02:46,919 --> 00:02:50,280 Speaker 1: and the idea is that the community benefits because the 45 00:02:50,280 --> 00:02:54,880 Speaker 1: community gets to participate in the actual process of creating 46 00:02:54,919 --> 00:02:57,680 Speaker 1: the stuff. Now, if you contrast this with the way 47 00:02:57,720 --> 00:03:01,720 Speaker 1: most companies work, then you have a much more closed system. 48 00:03:01,800 --> 00:03:05,560 Speaker 1: That's where you lock development down behind the closed doors 49 00:03:05,560 --> 00:03:08,959 Speaker 1: of your company. Apple is a great example of this 50 00:03:09,160 --> 00:03:12,400 Speaker 1: because the company has famously tried to close off a 51 00:03:12,440 --> 00:03:15,840 Speaker 1: lot of its ecosystems over the years. Not so much today, 52 00:03:16,280 --> 00:03:19,200 Speaker 1: at least not as much as it used to, but yeah, 53 00:03:19,280 --> 00:03:24,120 Speaker 1: it really forged its reputation on this approach. So here, 54 00:03:24,200 --> 00:03:26,840 Speaker 1: you do all the development behind closed doors with your 55 00:03:27,160 --> 00:03:31,160 Speaker 1: own staff, and then you release your finished application for 56 00:03:31,200 --> 00:03:34,560 Speaker 1: purchase or whatever, and the IP of the software remains 57 00:03:34,560 --> 00:03:38,160 Speaker 1: the property of the company. Right the public is not 58 00:03:38,280 --> 00:03:41,280 Speaker 1: allowed to access the source code or modify it in 59 00:03:41,360 --> 00:03:43,760 Speaker 1: any way. The only way the public gets access to 60 00:03:43,960 --> 00:03:47,880 Speaker 1: anything is to purchase a copy of the program. So 61 00:03:48,520 --> 00:03:53,840 Speaker 1: the open source approach has some interesting potential benefits. So 62 00:03:53,960 --> 00:03:57,000 Speaker 1: one is that by opening up development to the general public, 63 00:03:57,520 --> 00:04:00,600 Speaker 1: well that means everyone can contribute to it and you 64 00:04:00,680 --> 00:04:04,200 Speaker 1: get lots of different branches off the same basic project, 65 00:04:04,400 --> 00:04:07,400 Speaker 1: you can have a lot more folks looking over the 66 00:04:07,440 --> 00:04:12,320 Speaker 1: code and looking for vulnerabilities and bugs. So people could 67 00:04:12,320 --> 00:04:15,040 Speaker 1: be looking at it from all different angles, and it 68 00:04:15,200 --> 00:04:19,159 Speaker 1: means that you can very rapidly develop patches for bugs 69 00:04:19,200 --> 00:04:23,600 Speaker 1: and vulnerabilities to address issues before they become a real problem. So, 70 00:04:23,920 --> 00:04:28,280 Speaker 1: in an ideal situation, an open source project will keep 71 00:04:28,320 --> 00:04:32,360 Speaker 1: the features that work and then gradually ditch features that 72 00:04:32,440 --> 00:04:36,239 Speaker 1: don't work, and it can be a very laborious process. 73 00:04:36,240 --> 00:04:38,880 Speaker 1: It's not necessarily super fast, but it also tends to 74 00:04:38,920 --> 00:04:43,400 Speaker 1: be less expensive than relegating a project to a single author, 75 00:04:43,680 --> 00:04:47,520 Speaker 1: whether that author is like literally one person or a 76 00:04:47,560 --> 00:04:51,279 Speaker 1: company in charge of creating the software. Another way to 77 00:04:51,320 --> 00:04:53,920 Speaker 1: look at this is to consider how in an open 78 00:04:53,960 --> 00:04:58,240 Speaker 1: source project, the smartest and most talented developers can potentially 79 00:04:58,360 --> 00:05:01,160 Speaker 1: take part, no matter where they are or who they 80 00:05:01,160 --> 00:05:03,320 Speaker 1: work for, or perhaps if you want to be a 81 00:05:03,360 --> 00:05:07,279 Speaker 1: little more egalitarian about all this, you could say developers 82 00:05:07,320 --> 00:05:11,359 Speaker 1: who complement one another with their strengths, like one developer's 83 00:05:11,360 --> 00:05:15,680 Speaker 1: strengths covers another one's weaknesses. With open source projects, you 84 00:05:15,760 --> 00:05:18,840 Speaker 1: have this huge community of developers who do that and 85 00:05:18,920 --> 00:05:23,080 Speaker 1: together they can build a really great product. But in 86 00:05:23,120 --> 00:05:26,360 Speaker 1: a company that's using its own proprietary methods and its 87 00:05:26,360 --> 00:05:29,880 Speaker 1: own closed off system, the company is limited by the 88 00:05:30,080 --> 00:05:35,479 Speaker 1: talent contained within the organization. They might produce great software, 89 00:05:36,000 --> 00:05:39,240 Speaker 1: but there's a cap on resources there. Right, they are 90 00:05:39,320 --> 00:05:42,480 Speaker 1: only going to ever be as great as the strength 91 00:05:42,520 --> 00:05:44,800 Speaker 1: of their team, and their team is limited by the 92 00:05:44,800 --> 00:05:47,920 Speaker 1: people who work within that organization, whereas with open source, 93 00:05:48,480 --> 00:05:51,039 Speaker 1: there is no limit on the team. It's literally anyone 94 00:05:51,040 --> 00:05:54,719 Speaker 1: who wants to participate. That doesn't necessarily mean that open 95 00:05:54,760 --> 00:05:58,880 Speaker 1: source is always going to be better than a closed project. 96 00:05:59,240 --> 00:06:02,320 Speaker 1: That's not the case. It doesn't always mean that the 97 00:06:02,360 --> 00:06:05,160 Speaker 1: software that emerges from an open source project is going 98 00:06:05,200 --> 00:06:09,080 Speaker 1: to be useful. Right, Sometimes it's not, But open source 99 00:06:09,120 --> 00:06:12,440 Speaker 1: projects do enjoy benefits that closed projects simply do not 100 00:06:12,640 --> 00:06:16,839 Speaker 1: have access to. Now, this again relates to the concept 101 00:06:16,880 --> 00:06:20,960 Speaker 1: of diversity. So one way to look at diversity and 102 00:06:21,040 --> 00:06:25,320 Speaker 1: representation within any industry, but we're specifically focusing on tech, 103 00:06:25,839 --> 00:06:29,560 Speaker 1: is to say, I wish to address the inherent disparity 104 00:06:29,880 --> 00:06:34,359 Speaker 1: that's in this organization by fixing the problem of underrepresentation. 105 00:06:35,240 --> 00:06:38,560 Speaker 1: Like in a lot of tech companies, it's no secret 106 00:06:38,800 --> 00:06:40,599 Speaker 1: especially here in the United States, and a lot of 107 00:06:40,640 --> 00:06:44,400 Speaker 1: tech companies here in the US, the vast majority of employees, 108 00:06:44,440 --> 00:06:48,599 Speaker 1: but really of like management and executive levels, tends to 109 00:06:48,640 --> 00:06:53,839 Speaker 1: be white dudes. And it really kind of is an issue, right, 110 00:06:53,920 --> 00:06:55,960 Speaker 1: It's not just kind of an issue, It is an issue. 111 00:06:56,480 --> 00:07:01,560 Speaker 1: And this point perspective on diversity. It underlines the unfairness 112 00:07:01,600 --> 00:07:04,400 Speaker 1: present in a lot of organizations where the demographics of 113 00:07:04,440 --> 00:07:07,279 Speaker 1: the company might be well out of alignment with the 114 00:07:07,320 --> 00:07:11,560 Speaker 1: overall population demographics. And there are countless examples of systemic 115 00:07:11,640 --> 00:07:14,640 Speaker 1: issues that have contributed to this situation. I'm not saying 116 00:07:14,680 --> 00:07:19,760 Speaker 1: that companies have consciously pursued this and have tried to 117 00:07:19,960 --> 00:07:25,040 Speaker 1: make these organizations be dominated by white men. That's not 118 00:07:25,080 --> 00:07:27,640 Speaker 1: what I'm saying. There are a lot of elements that 119 00:07:27,680 --> 00:07:31,360 Speaker 1: have contributed to this being sort of a status quo, 120 00:07:32,120 --> 00:07:35,720 Speaker 1: and some of those our big social problems like racism 121 00:07:35,800 --> 00:07:39,160 Speaker 1: or sexism or some other ingrained social barrier that then 122 00:07:39,320 --> 00:07:42,360 Speaker 1: plays out all the way through the process of the 123 00:07:42,400 --> 00:07:45,560 Speaker 1: creation of these companies. And again, it's not a conscious 124 00:07:45,640 --> 00:07:48,840 Speaker 1: choice necessarily, it's something that's the product of a lot 125 00:07:48,880 --> 00:07:52,080 Speaker 1: of other elements. But the result is we have organizations 126 00:07:52,080 --> 00:07:56,160 Speaker 1: that effectively discriminate against people in certain categories. And I 127 00:07:56,160 --> 00:07:58,400 Speaker 1: think a lot of folks would say that having rules 128 00:07:58,440 --> 00:08:01,160 Speaker 1: that are even you know, perhaps un spoken or unwritten, 129 00:08:01,400 --> 00:08:05,040 Speaker 1: that deny someone an opportunity based solely on who they 130 00:08:05,040 --> 00:08:08,800 Speaker 1: are just isn't right. But there's also another way that 131 00:08:08,840 --> 00:08:11,400 Speaker 1: we can look at diversity. It's a very different perspective, 132 00:08:11,400 --> 00:08:14,280 Speaker 1: but it's just as important, which is diversity creates the 133 00:08:14,360 --> 00:08:19,960 Speaker 1: opportunity for organizations to benefit from multiple perspectives. So if 134 00:08:20,040 --> 00:08:24,120 Speaker 1: you staff an entire company with like minded folks and 135 00:08:24,160 --> 00:08:28,760 Speaker 1: they all share similar backgrounds and similar ideologies, you'll probably 136 00:08:28,840 --> 00:08:32,400 Speaker 1: be pretty effective in producing whatever it is you make. 137 00:08:32,720 --> 00:08:35,319 Speaker 1: You'll be good at that, but you're not nearly as 138 00:08:35,440 --> 00:08:39,600 Speaker 1: likely to be super innovative and transformative because you're not 139 00:08:39,640 --> 00:08:42,840 Speaker 1: bringing new ideas to the table. When you do bring 140 00:08:42,880 --> 00:08:46,280 Speaker 1: together different perspectives that have been shaped by very different 141 00:08:46,320 --> 00:08:49,760 Speaker 1: life experiences, that can really inspire creativity. It can also 142 00:08:49,960 --> 00:08:54,560 Speaker 1: head off massive problems. Case in point, We've talked about 143 00:08:54,559 --> 00:08:57,560 Speaker 1: this a lot on the show, but like facial recognition software, 144 00:08:57,960 --> 00:09:02,920 Speaker 1: we have shown over many cases it is disproportionately inaccurate 145 00:09:03,559 --> 00:09:08,600 Speaker 1: for people of color and for women compared to how 146 00:09:08,760 --> 00:09:11,880 Speaker 1: accurate it is for say, white men. And because you 147 00:09:12,000 --> 00:09:17,240 Speaker 1: have agencies like law enforcement agencies depending upon these technologies, 148 00:09:17,920 --> 00:09:22,520 Speaker 1: their dependence on those technologies is causing disproportionate harm in 149 00:09:22,600 --> 00:09:26,400 Speaker 1: these communities of people of color. And that's a real problem. 150 00:09:26,559 --> 00:09:31,280 Speaker 1: And one way to approach this problem is to have 151 00:09:31,360 --> 00:09:34,640 Speaker 1: more diversity in the actual process of developing the technologies 152 00:09:34,720 --> 00:09:40,600 Speaker 1: so that those unintentional biases that get built into these 153 00:09:40,600 --> 00:09:45,600 Speaker 1: systems are addressed before it gets to deployment. But that 154 00:09:45,600 --> 00:09:48,920 Speaker 1: doesn't just magically happen. You need to have these different 155 00:09:48,960 --> 00:09:53,959 Speaker 1: perspectives in order to actually get momentum on these things. Now, 156 00:09:54,000 --> 00:09:57,960 Speaker 1: I really do believe that diversity isn't just important to 157 00:09:58,080 --> 00:10:00,959 Speaker 1: address historic imbalances. I do. I think that is important. 158 00:10:00,960 --> 00:10:05,239 Speaker 1: I think we do need to address historic imbalances in 159 00:10:05,280 --> 00:10:09,800 Speaker 1: the various areas of society and culture that we experience 160 00:10:09,880 --> 00:10:13,000 Speaker 1: on a daily basis. But it also gives organizations the 161 00:10:13,080 --> 00:10:17,320 Speaker 1: chance to make better products and reach new customers. It 162 00:10:17,360 --> 00:10:20,280 Speaker 1: gives the opportunity for new ideas to take hold in 163 00:10:20,320 --> 00:10:24,160 Speaker 1: an organization, and that can fuel all sorts of innovation. 164 00:10:24,800 --> 00:10:28,559 Speaker 1: Often wonder how many amazing ideas have we never had 165 00:10:28,559 --> 00:10:31,920 Speaker 1: the chance to benefit from simply because there were systemic 166 00:10:32,000 --> 00:10:35,360 Speaker 1: barriers that kept the people who had those ideas from 167 00:10:35,400 --> 00:10:39,560 Speaker 1: being able to contribute. Right, Just imagine that. Imagine how 168 00:10:39,760 --> 00:10:43,880 Speaker 1: further along we would be if everyone who had great 169 00:10:43,920 --> 00:10:47,840 Speaker 1: ideas had had a chance to make those ideas into something. 170 00:10:48,440 --> 00:10:53,000 Speaker 1: But because we have these systemic issues in place, they 171 00:10:53,080 --> 00:10:55,520 Speaker 1: were prevented from doing so. I think that is a 172 00:10:55,640 --> 00:11:00,160 Speaker 1: huge tragedy. It's to the detriment of everyone. It's just 173 00:11:00,240 --> 00:11:02,559 Speaker 1: the person who had the idea, but everyone who could 174 00:11:02,600 --> 00:11:05,760 Speaker 1: have benefited from that idea. All right, we're going to 175 00:11:05,840 --> 00:11:07,880 Speaker 1: take a quick break. When we come back, i'll talk 176 00:11:07,960 --> 00:11:12,640 Speaker 1: more about diversity representation and this crazy story about the 177 00:11:12,679 --> 00:11:25,199 Speaker 1: tech conference. But first, let's thank our sponsors. Okay, we 178 00:11:25,240 --> 00:11:28,679 Speaker 1: are now up to that terrible word I hate optics. 179 00:11:29,120 --> 00:11:33,239 Speaker 1: So again I'm talking about how people perceive things. Sometimes 180 00:11:33,360 --> 00:11:40,080 Speaker 1: organizations will pursue diversity and representation projects not to actually 181 00:11:40,200 --> 00:11:45,480 Speaker 1: address unfairness or to encourage new ideas, but rather so 182 00:11:45,559 --> 00:11:48,600 Speaker 1: that they appear to the outside world to be concerned 183 00:11:48,640 --> 00:11:51,720 Speaker 1: about that kind of thing. So, in other words, they'll 184 00:11:51,760 --> 00:11:55,000 Speaker 1: do the base level, not even the base level of 185 00:11:55,080 --> 00:11:59,200 Speaker 1: effort for face value, just to get the benefit of 186 00:11:59,240 --> 00:12:03,520 Speaker 1: the public seeing the organization as being fair and evolved 187 00:12:03,760 --> 00:12:06,920 Speaker 1: while not actually doing much to support these concepts internally. 188 00:12:07,400 --> 00:12:11,200 Speaker 1: So here's an example. A company might designate someone to 189 00:12:11,280 --> 00:12:14,800 Speaker 1: be head diversity officer, but then that person might have 190 00:12:15,000 --> 00:12:18,560 Speaker 1: few if any resources to actually do anything meaningful within 191 00:12:18,600 --> 00:12:22,360 Speaker 1: the organization. So on paper, the company says, of course, 192 00:12:22,400 --> 00:12:25,680 Speaker 1: we're concerned about diversity and representation, because we even have 193 00:12:25,800 --> 00:12:30,199 Speaker 1: a manager with that job title. Here's the person right here. Meanwhile, 194 00:12:30,360 --> 00:12:35,679 Speaker 1: the corporate status inside the organization remains largely unchanged. That 195 00:12:35,720 --> 00:12:38,360 Speaker 1: optics thing is a real doozy, and it obviously affects 196 00:12:38,440 --> 00:12:43,600 Speaker 1: organizations across all contexts and industries. In tech, we can 197 00:12:43,640 --> 00:12:46,560 Speaker 1: see it affecting lots of different people. Often we see 198 00:12:46,600 --> 00:12:50,079 Speaker 1: it centering on women. Now, it's no secret that women 199 00:12:50,080 --> 00:12:53,720 Speaker 1: are underrepresented in the tech industry. The World Bank estimates 200 00:12:54,040 --> 00:12:56,800 Speaker 1: that forty nine point seven percent of the world's population 201 00:12:57,200 --> 00:13:00,640 Speaker 1: is female. Here in the United States, actually make up 202 00:13:00,679 --> 00:13:04,240 Speaker 1: fifty point five percent of the population, and yet if 203 00:13:04,280 --> 00:13:06,680 Speaker 1: we look at the tech industry, we see that women 204 00:13:06,760 --> 00:13:09,320 Speaker 1: hold only around twenty eight percent of the jobs in 205 00:13:09,360 --> 00:13:13,760 Speaker 1: mathematical and computational fields here in the US. Now, that's 206 00:13:13,800 --> 00:13:16,760 Speaker 1: still way ahead of the EU where women make up 207 00:13:16,880 --> 00:13:19,920 Speaker 1: nineteen point one percent of the workforce, and information and 208 00:13:19,960 --> 00:13:24,320 Speaker 1: communication technologies, and part of that disparity might come down 209 00:13:24,360 --> 00:13:28,559 Speaker 1: to some bigger picture stuff rather than just inherent unfairness 210 00:13:28,679 --> 00:13:32,120 Speaker 1: in the industry. I don't want to say that's the 211 00:13:32,240 --> 00:13:36,480 Speaker 1: end all bel issue. These are big, big challenges and 212 00:13:36,520 --> 00:13:39,520 Speaker 1: they have lots of factors that affect them. So for example, 213 00:13:39,559 --> 00:13:43,200 Speaker 1: according to the Women Tech Network, only forty seven percent 214 00:13:43,240 --> 00:13:45,559 Speaker 1: of the world's population of women are actually in the 215 00:13:45,600 --> 00:13:49,400 Speaker 1: global workforce to begin with, so more than half of 216 00:13:49,400 --> 00:13:53,160 Speaker 1: all women are not in the global workforce. To get 217 00:13:53,200 --> 00:13:56,280 Speaker 1: into why that is would take a lot of digressions, 218 00:13:56,320 --> 00:13:58,839 Speaker 1: and obviously it involves lots of different factors in different 219 00:13:58,840 --> 00:14:01,800 Speaker 1: countries all around the world, so it gets very complicated. 220 00:14:01,960 --> 00:14:05,319 Speaker 1: But it is a contributing factor that when you look 221 00:14:05,360 --> 00:14:08,040 Speaker 1: at the full population of women, less than half of 222 00:14:08,120 --> 00:14:10,920 Speaker 1: them are in the global workforce. But another issue is 223 00:14:10,960 --> 00:14:14,319 Speaker 1: that traditionally, especially here in the United States, women have 224 00:14:14,400 --> 00:14:17,840 Speaker 1: been discouraged from pursuing an education in or a career 225 00:14:18,000 --> 00:14:24,080 Speaker 1: in fields like mathematics, computer science, engineering, etc. In concert 226 00:14:24,120 --> 00:14:27,320 Speaker 1: with this is a social tendency that we think of 227 00:14:27,400 --> 00:14:31,320 Speaker 1: these kinds of jobs as being male oriented, and so 228 00:14:31,440 --> 00:14:36,400 Speaker 1: there's this social tendency to discourage women from considering opportunities 229 00:14:36,840 --> 00:14:42,160 Speaker 1: in what are traditionally referenced as STEM subjects. Now, of course, 230 00:14:42,680 --> 00:14:46,120 Speaker 1: there's still plenty of women who study and excel at 231 00:14:46,160 --> 00:14:49,120 Speaker 1: subjects like math and engineering. There are still lots of 232 00:14:49,120 --> 00:14:53,280 Speaker 1: women who go on to forge amazing careers undeterred by 233 00:14:53,320 --> 00:14:56,000 Speaker 1: any social hurdles that are in the way. But the 234 00:14:56,040 --> 00:14:58,760 Speaker 1: point I'm making is that there are these social hurdles 235 00:14:58,800 --> 00:15:03,920 Speaker 1: where there aren'traditionally for men, and that is one of 236 00:15:03,960 --> 00:15:07,760 Speaker 1: the many factors that then contributes to this lower percentage 237 00:15:07,800 --> 00:15:11,040 Speaker 1: of women within the industry, which in turn ends up 238 00:15:11,080 --> 00:15:15,640 Speaker 1: contributing to lots of other issues like sexism in the workplace, 239 00:15:15,920 --> 00:15:17,880 Speaker 1: which you know, we've talked a lot about that on 240 00:15:17,920 --> 00:15:19,520 Speaker 1: this show too, because there are a lot of tech 241 00:15:19,520 --> 00:15:24,760 Speaker 1: companies that have had some high profile scandals revolving around, 242 00:15:24,920 --> 00:15:28,240 Speaker 1: you know, sexism in the workforce. Now, breaking down these 243 00:15:28,280 --> 00:15:32,480 Speaker 1: barriers is a really challenging and ongoing process. There is 244 00:15:32,520 --> 00:15:34,920 Speaker 1: no easy solution to it. And I'm not here to 245 00:15:34,960 --> 00:15:37,920 Speaker 1: tell you that companies just need to flip a switch 246 00:15:37,920 --> 00:15:41,040 Speaker 1: and everything will be fine. That's not true. There's a 247 00:15:41,080 --> 00:15:43,720 Speaker 1: lot of work to be done, and it goes beyond 248 00:15:43,800 --> 00:15:47,920 Speaker 1: any one organization. We're talking about massive changes that are 249 00:15:47,920 --> 00:15:50,920 Speaker 1: going to take years to really take hold and start 250 00:15:50,960 --> 00:15:54,240 Speaker 1: to affect things like social concepts that have been deeply 251 00:15:54,520 --> 00:15:59,040 Speaker 1: ingrained in various cultures. But part of that means that 252 00:15:59,080 --> 00:16:03,160 Speaker 1: you're also ensure diversity in industry events. So if you 253 00:16:03,200 --> 00:16:05,480 Speaker 1: go to a big tech conference and you see that 254 00:16:05,680 --> 00:16:09,240 Speaker 1: all the listed speakers are white dudes, you're probably going 255 00:16:09,280 --> 00:16:12,520 Speaker 1: to walk away thinking this conference has really reinforced the 256 00:16:12,560 --> 00:16:15,920 Speaker 1: idea that white men are somehow the natural dominant group 257 00:16:15,960 --> 00:16:21,280 Speaker 1: in tech, Like there's some sort of just inclination in 258 00:16:21,360 --> 00:16:24,120 Speaker 1: white men to be really good at tech, and that's 259 00:16:24,200 --> 00:16:26,120 Speaker 1: why it's the way it is. It's not that there's 260 00:16:26,160 --> 00:16:28,880 Speaker 1: an inherent unfairness in the system. White guys are just 261 00:16:28,960 --> 00:16:33,360 Speaker 1: good at technology. That is not really true. I mean, 262 00:16:33,560 --> 00:16:37,120 Speaker 1: let me just remind you that the first person to 263 00:16:37,440 --> 00:16:42,920 Speaker 1: suggest that numbers and mathematical operations could represent things like 264 00:16:43,200 --> 00:16:49,560 Speaker 1: music and images was Ada Lovelace, a nineteenth century mathematician 265 00:16:49,760 --> 00:16:53,440 Speaker 1: who also happened to be a woman. She was amazing. 266 00:16:54,440 --> 00:16:58,360 Speaker 1: So the people who are having these phenomenal ideas are 267 00:16:58,400 --> 00:17:00,440 Speaker 1: not just white men. Sure, there are white men who 268 00:17:00,440 --> 00:17:03,600 Speaker 1: do have phenomenal ideas, but there are people of color 269 00:17:03,640 --> 00:17:06,440 Speaker 1: who have phenomenal ideas. There are women who have phenomenal ideas. 270 00:17:06,480 --> 00:17:11,639 Speaker 1: They're all sorts whose voices aren't necessarily being heard. So again, 271 00:17:11,720 --> 00:17:14,200 Speaker 1: if you go to a conference and all the speakers 272 00:17:14,240 --> 00:17:18,520 Speaker 1: are white dudes, that's reinforcing the stereotype that tech is 273 00:17:18,560 --> 00:17:21,560 Speaker 1: a place for white men, or just if you want 274 00:17:21,560 --> 00:17:26,280 Speaker 1: to be slightly less reductive men in general. Anyway, with 275 00:17:26,359 --> 00:17:29,240 Speaker 1: the growing awareness of the disparities in tech, there has 276 00:17:29,280 --> 00:17:32,760 Speaker 1: been a push for conferences to include speakers who represent 277 00:17:33,000 --> 00:17:36,280 Speaker 1: groups other than white dudes. So there's a lot of 278 00:17:36,320 --> 00:17:40,960 Speaker 1: pressure on conferences and the companies that throw these conferences 279 00:17:41,359 --> 00:17:44,520 Speaker 1: to make sure that there's greater representation in their lineup 280 00:17:44,560 --> 00:17:48,360 Speaker 1: of keynote speakers. Now, I'm sure that for most conferences 281 00:17:48,720 --> 00:17:53,479 Speaker 1: there's actual a mix of sincere desire to represent different 282 00:17:53,480 --> 00:17:56,800 Speaker 1: perspectives as well as a concern for optics. I'm not 283 00:17:56,840 --> 00:17:59,439 Speaker 1: here to tell you every conference out there is just 284 00:17:59,560 --> 00:18:04,400 Speaker 1: scram to get some token representation on their list so 285 00:18:04,440 --> 00:18:07,040 Speaker 1: that the pressure is off. I mean, I'm sure that's 286 00:18:07,160 --> 00:18:09,359 Speaker 1: part of it, and I'm sure for some conferences it 287 00:18:09,440 --> 00:18:11,879 Speaker 1: might even be the majority part of it. But I 288 00:18:11,920 --> 00:18:14,359 Speaker 1: also bet there are plenty of people who are genuinely 289 00:18:14,400 --> 00:18:18,840 Speaker 1: working to get a diverse lineup, not because it's going 290 00:18:18,920 --> 00:18:21,480 Speaker 1: to look good for the conference, but because it's going 291 00:18:21,520 --> 00:18:24,600 Speaker 1: to deliver the best message that ends up being the 292 00:18:24,720 --> 00:18:29,239 Speaker 1: really valuable experience that people walk away from that in 293 00:18:29,280 --> 00:18:32,280 Speaker 1: turn ends up benefiting not just the speaker, not just 294 00:18:32,320 --> 00:18:35,199 Speaker 1: the audience, but the conference itself. So again, it's the 295 00:18:35,240 --> 00:18:37,639 Speaker 1: bet to go on because it's the one that pays 296 00:18:37,680 --> 00:18:41,399 Speaker 1: off the best for everybody involved. So I hope that 297 00:18:41,480 --> 00:18:47,399 Speaker 1: most conferences aren't consumed with the concept of making good optics. 298 00:18:47,840 --> 00:18:50,000 Speaker 1: But that brings us up to the weird story that 299 00:18:50,080 --> 00:18:53,760 Speaker 1: happened this week or that unfolded this week. So there's 300 00:18:53,800 --> 00:18:57,800 Speaker 1: a developer conference called Devternity, and it was scheduled to 301 00:18:57,840 --> 00:19:02,959 Speaker 1: take place online next week. Actually, the conference originates out 302 00:19:03,000 --> 00:19:07,760 Speaker 1: of Latvia and typically includes several keynote sessions followed by workshops. 303 00:19:08,160 --> 00:19:12,159 Speaker 1: From what I understand, the conference has attracted several hundreds 304 00:19:12,200 --> 00:19:16,119 Speaker 1: of guests or or rather attendees in the past, but 305 00:19:16,320 --> 00:19:19,480 Speaker 1: then an investigation by four h four Media kind of 306 00:19:19,480 --> 00:19:24,760 Speaker 1: set off a series of events that prompted the company 307 00:19:24,880 --> 00:19:30,720 Speaker 1: behind this event to cancel it. And it all has 308 00:19:30,760 --> 00:19:36,720 Speaker 1: to do with diversity and representation. I rambled on a 309 00:19:36,720 --> 00:19:38,920 Speaker 1: little long. We're going to take another quick break. When 310 00:19:38,920 --> 00:19:51,280 Speaker 1: we come back, I'll explain more. Okay, so let's go 311 00:19:51,359 --> 00:19:54,320 Speaker 1: back and talk about dev Ternity. The founder of that 312 00:19:54,440 --> 00:19:58,080 Speaker 1: conference is a man named Edwards Zobs and I am 313 00:19:58,119 --> 00:20:03,160 Speaker 1: going to butcher names. Terrible at pronunciation in general, even 314 00:20:03,200 --> 00:20:06,480 Speaker 1: of English words, and Eastern European is just going to 315 00:20:06,480 --> 00:20:12,080 Speaker 1: make it even more comically inept. But that's my fault anyway. 316 00:20:12,480 --> 00:20:16,600 Speaker 1: As conferences go, Defternity really is kind of a modest size. 317 00:20:16,640 --> 00:20:19,960 Speaker 1: I mean, a few hundred attendees. That's no slouch, but 318 00:20:20,640 --> 00:20:23,480 Speaker 1: it's very small compared to the big events that happen 319 00:20:23,560 --> 00:20:27,560 Speaker 1: in other parts of the world. But Sizov's appears to 320 00:20:27,600 --> 00:20:31,200 Speaker 1: have been very good at securing at least some prominent 321 00:20:31,400 --> 00:20:34,600 Speaker 1: male speakers for the conference, but it seems he was 322 00:20:34,680 --> 00:20:38,240 Speaker 1: less effective at convincing prominent women in the field to 323 00:20:38,680 --> 00:20:43,119 Speaker 1: follow suit. Now, one explanation he gives, or if you prefer, 324 00:20:43,560 --> 00:20:47,199 Speaker 1: an excuse that he gives, is that there are fewer 325 00:20:47,280 --> 00:20:51,200 Speaker 1: notable women in the tech space already. Right, we already 326 00:20:51,200 --> 00:20:54,520 Speaker 1: talked about the aforementioned issues with diversity and representation in 327 00:20:54,560 --> 00:20:58,160 Speaker 1: the industry. There is no denying there are fewer women 328 00:20:58,280 --> 00:21:02,040 Speaker 1: in the industry than there are men. Further, he says, 329 00:21:02,440 --> 00:21:05,919 Speaker 1: these notable women are in high demand. That you have 330 00:21:06,960 --> 00:21:10,480 Speaker 1: hundreds or thousands of tech conferences, all of which are 331 00:21:10,960 --> 00:21:15,200 Speaker 1: trying to put forth the effort of being more diverse. 332 00:21:15,800 --> 00:21:20,240 Speaker 1: And meanwhile, you have a relatively small pool of notable 333 00:21:20,280 --> 00:21:25,440 Speaker 1: women speakers who could attend your conference. So you have 334 00:21:25,800 --> 00:21:31,040 Speaker 1: this issue where maybe you can't secure a significant number 335 00:21:31,119 --> 00:21:34,639 Speaker 1: of women to appear at your conference. So if that, 336 00:21:34,760 --> 00:21:37,800 Speaker 1: in fact is the case that led to this, it 337 00:21:38,160 --> 00:21:40,560 Speaker 1: meant there was a real problem for devternity, right. They 338 00:21:40,640 --> 00:21:44,159 Speaker 1: just couldn't schedule enough prominent women developers to create a 339 00:21:44,240 --> 00:21:48,520 Speaker 1: diverse lineup of speakers. So what do you do if 340 00:21:48,520 --> 00:21:52,399 Speaker 1: you can't find qualified speakers to headline your event? Well, 341 00:21:53,600 --> 00:21:57,760 Speaker 1: how about you invent some people. So allegedly that's what 342 00:21:57,800 --> 00:22:01,560 Speaker 1: Sizov's chose to do. On twenty four, twenty twenty three, 343 00:22:01,600 --> 00:22:06,480 Speaker 1: an engineer named gregly aroz And again apologize for the pronunciation, 344 00:22:07,119 --> 00:22:10,919 Speaker 1: posted to x that is the platform formerly known as Twitter, 345 00:22:11,280 --> 00:22:16,400 Speaker 1: and said quote, imagine a tech conference having no CFP 346 00:22:17,000 --> 00:22:20,760 Speaker 1: as they reach out to speakers directly. They successfully attract 347 00:22:20,760 --> 00:22:23,240 Speaker 1: some of the most heavy hitter men speakers in tech 348 00:22:23,640 --> 00:22:27,160 Speaker 1: and three women speakers. Now, imagine my surprise that two 349 00:22:27,280 --> 00:22:31,440 Speaker 1: of those women are fake profiles. They do not exist, 350 00:22:31,920 --> 00:22:39,480 Speaker 1: nada end quote. Yikes. So his accusation was a really 351 00:22:39,600 --> 00:22:43,760 Speaker 1: serious one. I mean, a conference posting even a single 352 00:22:43,920 --> 00:22:47,399 Speaker 1: fake profile for a keynote speaker would be a huge 353 00:22:47,440 --> 00:22:51,320 Speaker 1: gamble because it's really just a matter of time before 354 00:22:51,520 --> 00:22:55,040 Speaker 1: someone does even a little bit of digging and then 355 00:22:55,119 --> 00:22:57,439 Speaker 1: finds out that the keynote speaker who is listed on 356 00:22:57,440 --> 00:23:02,040 Speaker 1: a website doesn't appear to you know, actually exist. And 357 00:23:02,119 --> 00:23:07,360 Speaker 1: apparently Devternity had done it twice. But wait, it gets weirder. 358 00:23:07,880 --> 00:23:12,120 Speaker 1: So a supposed pair of women who worked for Coinbase 359 00:23:12,640 --> 00:23:17,760 Speaker 1: named Anna Boyko and Natalie Stradler were listed among the 360 00:23:17,760 --> 00:23:22,880 Speaker 1: speakers for this Devternity conference, but according to Oroz, they 361 00:23:22,920 --> 00:23:27,000 Speaker 1: don't actually exist. Further, Roz said that a woman named 362 00:23:27,080 --> 00:23:32,560 Speaker 1: Elena Procada, a Prokoda supposedly a senior engineer for wasapp, 363 00:23:33,160 --> 00:23:37,760 Speaker 1: was also most likely fictional. He said he couldn't prove it, 364 00:23:37,840 --> 00:23:41,919 Speaker 1: but that he pointed out this person who's supposedly the 365 00:23:42,119 --> 00:23:46,000 Speaker 1: senior engineer or a senior engineer for WhatsApp had no 366 00:23:46,160 --> 00:23:50,440 Speaker 1: online profiles except for one. There's only one online profile 367 00:23:50,520 --> 00:23:54,199 Speaker 1: for this woman, and that was her profile as a 368 00:23:54,240 --> 00:24:01,200 Speaker 1: speaker for another conference called JD. Cohn jdkon. And it's 369 00:24:01,240 --> 00:24:05,040 Speaker 1: the same company that puts on JD Cohn that puts 370 00:24:05,119 --> 00:24:09,440 Speaker 1: on Devternity. The two conferences are run by the same organization. 371 00:24:10,240 --> 00:24:13,399 Speaker 1: So could it be that this underlying organization was just 372 00:24:13,520 --> 00:24:19,000 Speaker 1: manufacturing women to make it seem like their lineup was 373 00:24:19,040 --> 00:24:23,800 Speaker 1: more diverse. But it gets weirder. So then Sizov's responds 374 00:24:23,880 --> 00:24:28,480 Speaker 1: to Oroz's accusation and admits that at least one of 375 00:24:28,480 --> 00:24:32,280 Speaker 1: the profiles that had been listed for Devternity was a 376 00:24:32,320 --> 00:24:36,600 Speaker 1: fake profile. But Sizov said there was a good reason 377 00:24:36,640 --> 00:24:40,600 Speaker 1: for this. They originally created the fake profile for the 378 00:24:40,640 --> 00:24:44,600 Speaker 1: purposes of testing the website early on. That this was 379 00:24:44,760 --> 00:24:47,639 Speaker 1: just to make sure the formatting and everything was attractive, 380 00:24:47,720 --> 00:24:49,480 Speaker 1: that it looked the way and behave the way they 381 00:24:49,520 --> 00:24:52,560 Speaker 1: wanted it to. So they built a fake profile for 382 00:24:52,600 --> 00:24:56,760 Speaker 1: that purposes and then they just plane darn forgot to 383 00:24:57,119 --> 00:25:00,800 Speaker 1: delete the fake profile, and that when and he found 384 00:25:00,800 --> 00:25:03,920 Speaker 1: this out back in October that this fake profile was 385 00:25:03,960 --> 00:25:07,280 Speaker 1: still up on the site while the conference was approaching. 386 00:25:07,760 --> 00:25:10,879 Speaker 1: He decided to keep it up there because he was 387 00:25:10,920 --> 00:25:14,480 Speaker 1: trying to get an actual, real human woman developer to 388 00:25:14,600 --> 00:25:18,320 Speaker 1: fill a spot on the speaker list, and then presumably 389 00:25:18,680 --> 00:25:21,960 Speaker 1: he would have swapped out the fake profile for the 390 00:25:22,000 --> 00:25:24,280 Speaker 1: real profile of the person who had agreed to be 391 00:25:24,359 --> 00:25:28,600 Speaker 1: a speaker at Devternity, except he couldn't get a real 392 00:25:28,640 --> 00:25:32,600 Speaker 1: life woman to agree to that. He also mentioned that 393 00:25:32,640 --> 00:25:35,359 Speaker 1: one person who had dropped out of the event ahead 394 00:25:35,359 --> 00:25:40,479 Speaker 1: of time was a tech influencer named Julia Kirsina who 395 00:25:40,640 --> 00:25:45,680 Speaker 1: uses the handle coding Unicorn on Instagram. So on Instagram 396 00:25:45,720 --> 00:25:48,160 Speaker 1: she had more than one hundred and fifteen thousand followers, 397 00:25:48,200 --> 00:25:52,520 Speaker 1: although when I tried to check that today I couldn't 398 00:25:52,560 --> 00:25:55,840 Speaker 1: access it at all. It was giving me an error message. Now, 399 00:25:55,920 --> 00:25:58,920 Speaker 1: to be fair, I don't I'm not on Instagram anymore. 400 00:25:59,200 --> 00:26:02,760 Speaker 1: So I was just using the web based portal to 401 00:26:03,200 --> 00:26:05,200 Speaker 1: look at this, and it could be that that was 402 00:26:05,240 --> 00:26:07,600 Speaker 1: where the problem was. So y'all, if you want to 403 00:26:07,680 --> 00:26:10,520 Speaker 1: check and see if Coding Unicorn is still on Instagram, 404 00:26:10,560 --> 00:26:15,679 Speaker 1: feel free anyway. The profile shows an attractive young woman 405 00:26:16,040 --> 00:26:20,320 Speaker 1: who often is posing with computers and stuff. Some of 406 00:26:20,359 --> 00:26:24,520 Speaker 1: the posts show her being rather flirty or sexy, and 407 00:26:24,600 --> 00:26:29,840 Speaker 1: apparently she had previously agreed to appear at Defternity but 408 00:26:29,960 --> 00:26:33,639 Speaker 1: had to drop out. Actually, she had been listed to 409 00:26:33,920 --> 00:26:38,480 Speaker 1: appear at previous conferences, but as far as investigators or 410 00:26:38,520 --> 00:26:43,720 Speaker 1: rather journalists could find, she never actually gave any speeches. 411 00:26:44,280 --> 00:26:47,560 Speaker 1: So this was someone who had been listed to appear 412 00:26:47,640 --> 00:26:51,240 Speaker 1: at conferences in the past and then apparently just never 413 00:26:51,480 --> 00:26:56,359 Speaker 1: showed up. And further investigation suggested that the reason for 414 00:26:56,400 --> 00:27:01,440 Speaker 1: this is that there is no Julia Kirsina. One journalist 415 00:27:01,560 --> 00:27:05,600 Speaker 1: reached out to her supposed alma mater to verify that 416 00:27:05,680 --> 00:27:09,120 Speaker 1: she had in fact attended that particular university and achieved 417 00:27:09,119 --> 00:27:12,800 Speaker 1: a degree in the subject that was listed in her profile, 418 00:27:12,920 --> 00:27:15,359 Speaker 1: and was told that there was no record of such 419 00:27:15,400 --> 00:27:20,280 Speaker 1: a student. So that's a big red flag. And other 420 00:27:20,760 --> 00:27:26,600 Speaker 1: investigations suggested that the account coding unicorn isn't run by 421 00:27:26,640 --> 00:27:29,080 Speaker 1: a woman at all, but rather by and I bet 422 00:27:29,480 --> 00:27:33,800 Speaker 1: some of you have guessed this already, Sizov's the guy 423 00:27:33,840 --> 00:27:37,160 Speaker 1: who organized the Defternity conference in the first place. Now, 424 00:27:37,200 --> 00:27:40,359 Speaker 1: to be clear, as I record this, he hasn't said 425 00:27:40,400 --> 00:27:44,320 Speaker 1: that he was behind that account. But several folks appointed 426 00:27:44,359 --> 00:27:47,080 Speaker 1: to signs that indicate he's very much involved in that. 427 00:27:47,560 --> 00:27:50,840 Speaker 1: Everything from IP address logs that suggest he was the 428 00:27:50,880 --> 00:27:56,520 Speaker 1: one controlling Coding Unicorn to the fact that posts that 429 00:27:56,640 --> 00:28:00,680 Speaker 1: he made on his own social profiles ended up being 430 00:28:00,760 --> 00:28:05,600 Speaker 1: repurposed as posts in the Coding Unicorn account. That's a 431 00:28:05,600 --> 00:28:08,240 Speaker 1: suggestion that, you know, it's one person who's just doing 432 00:28:08,240 --> 00:28:10,800 Speaker 1: all this. So it's led more than a few people 433 00:28:10,840 --> 00:28:16,040 Speaker 1: to accuse him of catfishing, of creating this account to 434 00:28:16,160 --> 00:28:20,119 Speaker 1: try and create credibility where there is non. Now, in 435 00:28:20,160 --> 00:28:22,800 Speaker 1: the wake of these discoveries, a bunch of speakers withdrew 436 00:28:22,840 --> 00:28:25,400 Speaker 1: from Defternity, and now the event appears to be shut 437 00:28:25,480 --> 00:28:27,359 Speaker 1: down this year. If you go to the website, it 438 00:28:27,520 --> 00:28:31,399 Speaker 1: says the event will not go on as planned as 439 00:28:31,440 --> 00:28:34,760 Speaker 1: for jd Con twenty twenty four, which is the next 440 00:28:34,800 --> 00:28:37,840 Speaker 1: event for that particular conference. I'm not sure if that 441 00:28:37,920 --> 00:28:39,640 Speaker 1: event is going to move forward or not. When I 442 00:28:39,760 --> 00:28:42,160 Speaker 1: try to check the website while I was working on 443 00:28:42,200 --> 00:28:44,719 Speaker 1: this episode, it was just giving me an error message 444 00:28:44,760 --> 00:28:48,080 Speaker 1: that there was no site there. So right now I imagine 445 00:28:48,160 --> 00:28:50,720 Speaker 1: they're kind of in damage control. At the very least. 446 00:28:51,320 --> 00:28:54,479 Speaker 1: One thing that makes the Defternity story even worse is 447 00:28:54,480 --> 00:28:58,600 Speaker 1: that the conference has what calls a Hollywood policy, as 448 00:28:58,640 --> 00:29:02,760 Speaker 1: in don't call up, We'll call you, which means they 449 00:29:02,800 --> 00:29:08,680 Speaker 1: don't accept submissions from potential speakers. Instead, Defternity says it 450 00:29:08,760 --> 00:29:11,400 Speaker 1: reaches out to people to ask them to be part 451 00:29:11,440 --> 00:29:15,560 Speaker 1: of the conference, they don't accept submissions. This is what 452 00:29:16,520 --> 00:29:19,920 Speaker 1: Gregly was referencing earlier when he said there was no CFP. 453 00:29:20,640 --> 00:29:25,120 Speaker 1: There's no way to submit yourself to be considered as speaker, 454 00:29:25,440 --> 00:29:28,080 Speaker 1: and I imagine that severely limits the number of people 455 00:29:28,120 --> 00:29:31,480 Speaker 1: Devternity could consider for the conference. It probably means that 456 00:29:31,480 --> 00:29:36,160 Speaker 1: the organization only targets a small pool of potential speakers 457 00:29:36,200 --> 00:29:39,360 Speaker 1: every year. It's impossible to be aware of everyone who's 458 00:29:39,400 --> 00:29:42,720 Speaker 1: doing great workout there. So this issue is one that 459 00:29:42,760 --> 00:29:45,040 Speaker 1: I think largely could have been avoided if they had 460 00:29:45,120 --> 00:29:49,040 Speaker 1: just used a submissions policy and accepted submissions and started 461 00:29:49,080 --> 00:29:52,920 Speaker 1: to look through which ones are qualified and interesting. Anyway, 462 00:29:53,920 --> 00:29:56,360 Speaker 1: I want to conclude this episode by once again stressing 463 00:29:56,680 --> 00:30:00,120 Speaker 1: that diversity is a good thing for everybody if if 464 00:30:00,120 --> 00:30:03,720 Speaker 1: it's handled properly. If you're just doing it for the optics, 465 00:30:04,400 --> 00:30:08,080 Speaker 1: chances are you're not really making things better for anyone, 466 00:30:08,480 --> 00:30:10,440 Speaker 1: and in fact, you could just be setting yourself up 467 00:30:10,440 --> 00:30:13,800 Speaker 1: for a big pr nightmare at the very least. But 468 00:30:14,400 --> 00:30:16,480 Speaker 1: if you do it right, if you do it with 469 00:30:16,680 --> 00:30:20,240 Speaker 1: intention and sincerity, and you're willing to learn from mistakes 470 00:30:20,280 --> 00:30:23,400 Speaker 1: and correct those mistakes, and you really push for it, 471 00:30:23,960 --> 00:30:25,880 Speaker 1: you can bring in and boost points of view that 472 00:30:25,920 --> 00:30:29,920 Speaker 1: will benefit the organization as a whole, and all boats 473 00:30:29,960 --> 00:30:32,640 Speaker 1: get lifted as a result. And even if you are 474 00:30:33,000 --> 00:30:36,000 Speaker 1: obsessed with optics, well, it sure does look good when 475 00:30:36,000 --> 00:30:39,040 Speaker 1: a company handles diversity and representation in a sincere and 476 00:30:39,120 --> 00:30:42,920 Speaker 1: dedicated way. So you still achieve that goal. Yeah, it's 477 00:30:42,920 --> 00:30:45,760 Speaker 1: a lot of work, and it's ongoing work. It's not, again, 478 00:30:45,880 --> 00:30:51,640 Speaker 1: something that's just easily done. It requires tons of revision 479 00:30:52,080 --> 00:30:58,360 Speaker 1: and correction and real dedication. But the benefits are huge 480 00:30:59,000 --> 00:31:03,760 Speaker 1: for all concerned. So again, interesting tech story. I think 481 00:31:03,760 --> 00:31:07,400 Speaker 1: it brings up something important that all organizations really need 482 00:31:07,440 --> 00:31:13,000 Speaker 1: to consider, and hopefully was at least interesting because golly, 483 00:31:13,240 --> 00:31:17,720 Speaker 1: when I read about devternity and this apparently fake Instagram 484 00:31:17,800 --> 00:31:20,600 Speaker 1: influencer and all of these things, it just made my 485 00:31:20,680 --> 00:31:23,640 Speaker 1: head spin. It's been quite a couple of weeks for 486 00:31:24,280 --> 00:31:28,040 Speaker 1: weird tech news. Y'all. I guess people are really deciding 487 00:31:28,040 --> 00:31:30,280 Speaker 1: to let it out for the end of twenty twenty three, 488 00:31:30,920 --> 00:31:34,080 Speaker 1: so tomorrow we should have another news episode of tech Stuff. 489 00:31:34,080 --> 00:31:36,320 Speaker 1: I just want to let y'all know, if you hadn't 490 00:31:36,360 --> 00:31:40,000 Speaker 1: heard already, I am going on vacation next week. So 491 00:31:40,400 --> 00:31:44,200 Speaker 1: next week we are going to have some classic episodes 492 00:31:45,200 --> 00:31:48,480 Speaker 1: and maybe some other surprises in there. But just wanted 493 00:31:48,520 --> 00:31:50,440 Speaker 1: to make you aware of that and that I will 494 00:31:50,440 --> 00:31:53,560 Speaker 1: be back the following week and then we'll be heading 495 00:31:53,600 --> 00:31:55,880 Speaker 1: into the holiday season, so we'll see how that goes. 496 00:31:56,440 --> 00:31:58,960 Speaker 1: I hope you are all well, and I'll talk to 497 00:31:59,000 --> 00:32:09,280 Speaker 1: you again really soon. Tech Stuff is an iHeartRadio production. 498 00:32:09,560 --> 00:32:14,600 Speaker 1: For more podcasts from iHeartRadio, visit the iHeartRadio app, Apple Podcasts, 499 00:32:14,720 --> 00:32:16,720 Speaker 1: or wherever you listen to your favorite shows.