1 00:00:00,080 --> 00:00:04,520 Speaker 1: Yeah. Welcome to How to Citizen with Baritune Day, a 2 00:00:04,600 --> 00:00:08,400 Speaker 1: podcast that reimagine citizen as a verb, not a legal status. 3 00:00:09,240 --> 00:00:11,639 Speaker 1: This season is all about tech and how it can 4 00:00:11,680 --> 00:00:15,960 Speaker 1: bring us together instead of tearing us apart. We're bringing 5 00:00:16,000 --> 00:00:18,720 Speaker 1: you the people using technology for so much more than 6 00:00:18,800 --> 00:00:29,760 Speaker 1: revenue and user growth. They're using it to help us citizen. Today, 7 00:00:29,920 --> 00:00:34,440 Speaker 1: we're continuing our conversation about data. Last time with Kasha, 8 00:00:34,760 --> 00:00:37,680 Speaker 1: we looked at the way data scientists categorize and use 9 00:00:37,800 --> 00:00:41,440 Speaker 1: data to make algorithms and other technologies. The hope is 10 00:00:42,040 --> 00:00:45,199 Speaker 1: if the data set used is properly labeled and vetted, 11 00:00:45,560 --> 00:00:48,440 Speaker 1: the better the tech eventually becomes, and we can make 12 00:00:48,440 --> 00:00:51,640 Speaker 1: tools that are smarter and more importantly, more equitable. And 13 00:00:51,720 --> 00:00:54,240 Speaker 1: the algorithm comes out the end, and there's a decision 14 00:00:54,240 --> 00:00:56,120 Speaker 1: that's made you get the loan. You didn't get the loan. 15 00:00:56,560 --> 00:01:00,760 Speaker 1: The algorithm recognizes your speech, doesn't recognize your speech, sees you, 16 00:01:00,880 --> 00:01:04,520 Speaker 1: doesn't see you. People think, oh, just change the algorithm. 17 00:01:04,240 --> 00:01:06,360 Speaker 1: Oh no, you have to go all the way back 18 00:01:06,400 --> 00:01:10,280 Speaker 1: to the beginning. Cash is right. We can't just change 19 00:01:10,280 --> 00:01:13,560 Speaker 1: the algorithm. We have to go further back and examine 20 00:01:13,600 --> 00:01:17,120 Speaker 1: how data is collected in the first place. So consider 21 00:01:17,200 --> 00:01:25,440 Speaker 1: this episode Data Justice Part two. Right now, it's possible 22 00:01:25,520 --> 00:01:28,920 Speaker 1: that data is the most valuable resource on the planet. 23 00:01:29,840 --> 00:01:33,520 Speaker 1: We use it to drive advertising, medical research, product development. 24 00:01:33,800 --> 00:01:37,920 Speaker 1: Its applications are endless, and I'm not being hyperbolic. In 25 00:01:38,040 --> 00:01:42,440 Speaker 1: terms of financial value, data currently beats out coal oil. 26 00:01:43,120 --> 00:01:47,200 Speaker 1: It's a commodity. That race to collect and monetize data 27 00:01:47,400 --> 00:01:51,800 Speaker 1: has transformed into a veritable gold rush, or I guess 28 00:01:51,800 --> 00:01:56,600 Speaker 1: I should say data rush. Now. In its most basic form, 29 00:01:56,800 --> 00:02:01,480 Speaker 1: data is information, but beyond that, it also helps define 30 00:02:01,680 --> 00:02:05,360 Speaker 1: parts of who we are and the smallest part of us. 31 00:02:06,200 --> 00:02:09,600 Speaker 1: There's a market for that too. Those tiny nuggets of 32 00:02:09,720 --> 00:02:14,600 Speaker 1: data are precious and extremely personal. They live inside of us. 33 00:02:15,240 --> 00:02:20,080 Speaker 1: I'm talking about genetic data. Are geno that O G code. 34 00:02:21,520 --> 00:02:25,520 Speaker 1: The business around genetic data is huge, and I don't 35 00:02:25,639 --> 00:02:27,919 Speaker 1: mean just the twenty three and me tests you gave 36 00:02:27,960 --> 00:02:31,200 Speaker 1: your family member last holiday season. I'm talking about a 37 00:02:31,240 --> 00:02:37,200 Speaker 1: global industry worth billions of dollars. Bio data companies initially 38 00:02:37,240 --> 00:02:40,160 Speaker 1: set out to make genetic testing approachable and affordable for 39 00:02:40,200 --> 00:02:43,760 Speaker 1: the general public, and at first, these little tubes they 40 00:02:43,760 --> 00:02:47,560 Speaker 1: were intended to assess our risk for genetic diseases. But 41 00:02:47,720 --> 00:02:51,640 Speaker 1: since then they've really grown in popularity by offering us 42 00:02:51,720 --> 00:02:57,119 Speaker 1: a window into our past, our geographical ancestry. And when 43 00:02:57,160 --> 00:03:00,720 Speaker 1: we spit in the tube and contribute to these data sets, Yo, 44 00:03:00,800 --> 00:03:05,560 Speaker 1: it feels revelatory and super scientific. Like I found out 45 00:03:05,600 --> 00:03:09,760 Speaker 1: I was exactly twenty five point three percent Nigerian WHOA. 46 00:03:11,240 --> 00:03:13,960 Speaker 1: I even hosted a podcast partnered with twenty three in 47 00:03:14,040 --> 00:03:17,200 Speaker 1: Me called spit you might have heard it, and I 48 00:03:17,240 --> 00:03:19,360 Speaker 1: got to talk to all kinds of people about how 49 00:03:19,440 --> 00:03:22,600 Speaker 1: DNA can give us a new perspective on our relationships 50 00:03:22,639 --> 00:03:25,639 Speaker 1: with each other and with our health. At the time, 51 00:03:25,760 --> 00:03:28,360 Speaker 1: I was thrilled to share these new ideas about an 52 00:03:28,400 --> 00:03:31,400 Speaker 1: emerging science. It was a shiny and new way of 53 00:03:31,440 --> 00:03:34,960 Speaker 1: looking at ourselves and our ancestors. But my guest today 54 00:03:35,200 --> 00:03:43,600 Speaker 1: made me rethink everything. Who does data ultimately benefit? If 55 00:03:43,600 --> 00:03:47,360 Speaker 1: the data is not benefiting the people, the individuals, the 56 00:03:47,400 --> 00:03:51,680 Speaker 1: communities that provided that data, then who are we using 57 00:03:51,680 --> 00:03:54,520 Speaker 1: it for? Who are we protecting and who are we 58 00:03:54,600 --> 00:03:59,120 Speaker 1: uplifting at the cost of others? Justice. Crystal Socie is 59 00:03:59,160 --> 00:04:04,080 Speaker 1: an Indigenous meticists and bioethicist at the Native Biodata Consortium. 60 00:04:04,120 --> 00:04:07,400 Speaker 1: She used to work in precision medicine, developing targeted therapies 61 00:04:07,440 --> 00:04:10,560 Speaker 1: and treatments focused on cancer. But that experience led her 62 00:04:10,600 --> 00:04:14,440 Speaker 1: to co found the nonprofit Consortium with other Indigenous scientists, 63 00:04:14,680 --> 00:04:17,600 Speaker 1: and they built a network for Native researchers to collaborate 64 00:04:17,960 --> 00:04:23,240 Speaker 1: and protect their data heritage to already underserved and underrepresented 65 00:04:23,279 --> 00:04:26,560 Speaker 1: people's data could be part of what gives these communities 66 00:04:26,600 --> 00:04:33,920 Speaker 1: determination and agency over their own personhood and knowledge. Hello, Hello, 67 00:04:34,120 --> 00:04:36,240 Speaker 1: welcome to how does Citizen. Thank you so much for 68 00:04:36,240 --> 00:04:39,200 Speaker 1: the kind invite. You are so welcome. We're very excited 69 00:04:39,240 --> 00:04:41,640 Speaker 1: to have you as a part of Crystal joined me 70 00:04:41,720 --> 00:04:44,919 Speaker 1: from Phoenix, Arizona, which is the ancestral homeland of the 71 00:04:45,000 --> 00:04:49,400 Speaker 1: authom Pepash and Hagm people's. I joined her from northeast 72 00:04:49,480 --> 00:04:52,200 Speaker 1: Los Angeles, the homeland of the Tongua people's who are 73 00:04:52,240 --> 00:04:55,880 Speaker 1: also known as the Keeach. I want to start with 74 00:04:55,920 --> 00:04:59,039 Speaker 1: your ted talk called d n A is not our identity. 75 00:04:59,600 --> 00:05:03,440 Speaker 1: I in here to Vanderbilt to pursue a PhD in genetics, 76 00:05:03,640 --> 00:05:06,000 Speaker 1: and because of that, many people come up to me 77 00:05:06,200 --> 00:05:10,400 Speaker 1: with this question in mind, who am I? Really? What 78 00:05:10,440 --> 00:05:13,920 Speaker 1: they're asking is who am I? In context? It is 79 00:05:14,000 --> 00:05:17,040 Speaker 1: direct to consumer genetic test kit. Now you probably have 80 00:05:17,080 --> 00:05:19,000 Speaker 1: been made the concept. You spend a hundred to two 81 00:05:19,080 --> 00:05:23,080 Speaker 1: hundred dollars, You spent a whole lot of saliva, more 82 00:05:23,080 --> 00:05:24,800 Speaker 1: than you ever thought you can ever produce in your 83 00:05:24,920 --> 00:05:28,880 Speaker 1: entire life, and you mail it out thirty days later 84 00:05:28,920 --> 00:05:31,919 Speaker 1: you get a result, and that result is a percent 85 00:05:32,320 --> 00:05:36,760 Speaker 1: estimation of ancestral background. First of all, nice work. And 86 00:05:37,160 --> 00:05:40,919 Speaker 1: that question of who am I is actually mired in 87 00:05:41,080 --> 00:05:44,800 Speaker 1: something else because according to ancestry d m A, the 88 00:05:44,920 --> 00:05:48,360 Speaker 1: number one question is why isn't my Native American ancestry 89 00:05:48,360 --> 00:05:54,120 Speaker 1: not showing up? Oh? Okay, I only have ten minutes, 90 00:05:54,120 --> 00:05:57,400 Speaker 1: not ten years, to unpack the assertedness of that claim, 91 00:05:57,920 --> 00:06:01,480 Speaker 1: or to tell you how dangerous it is to equate 92 00:06:01,520 --> 00:06:05,240 Speaker 1: indigenousity in a false way. And I think a lot 93 00:06:05,279 --> 00:06:07,600 Speaker 1: of us get the opposite message. You know, there's a 94 00:06:07,680 --> 00:06:11,120 Speaker 1: lot of DNA testing. It's become like a party favor 95 00:06:11,240 --> 00:06:14,680 Speaker 1: in some ways to define yourself by the data in 96 00:06:14,760 --> 00:06:19,159 Speaker 1: your genetics and run these tests, so people think the opposite. Actually, 97 00:06:19,160 --> 00:06:22,520 Speaker 1: my DNA is my identity. That's what all the messaging says. 98 00:06:22,880 --> 00:06:25,880 Speaker 1: So tell me why you say our DNA is not 99 00:06:26,000 --> 00:06:30,200 Speaker 1: that our genetics are only half of the story. And 100 00:06:30,240 --> 00:06:34,160 Speaker 1: in fact, when we talk about health inequities and communities 101 00:06:34,240 --> 00:06:37,119 Speaker 1: of color, we really have to talk about the other 102 00:06:37,520 --> 00:06:42,680 Speaker 1: structural barriers that relate to health and disease. So, for instance, 103 00:06:42,760 --> 00:06:46,280 Speaker 1: with the COVID pandemic, there was a lot of press 104 00:06:46,279 --> 00:06:50,279 Speaker 1: about how these are rates for initially so much higher 105 00:06:50,440 --> 00:06:54,160 Speaker 1: and tribal communities such as my own, but those are 106 00:06:54,200 --> 00:06:56,760 Speaker 1: not due to biological differences. They were due to things 107 00:06:57,040 --> 00:07:00,960 Speaker 1: like our water rights being usurped from us, that we 108 00:07:01,000 --> 00:07:04,000 Speaker 1: didn't have water to wash our hands, which is a 109 00:07:04,080 --> 00:07:09,200 Speaker 1: key preventative measure for curbing viral transmission. Also, the fact 110 00:07:09,360 --> 00:07:13,680 Speaker 1: that we have to drive ours one way just to 111 00:07:13,720 --> 00:07:18,880 Speaker 1: get to a preventative health clinic. Technology is limiting our 112 00:07:18,920 --> 00:07:24,640 Speaker 1: ability to detect these genetic differences across the genome. We 113 00:07:24,680 --> 00:07:29,480 Speaker 1: don't have that much information yet, and to reduce all 114 00:07:29,520 --> 00:07:33,960 Speaker 1: of these differences to just biology is just ignoring the 115 00:07:34,040 --> 00:07:38,840 Speaker 1: beautiful diversity that's within all of our cultures worldwide. And 116 00:07:39,040 --> 00:07:41,680 Speaker 1: that's just talking about the health component. I haven't even 117 00:07:41,800 --> 00:07:46,760 Speaker 1: gotten into the genetic ancestral components. We'll get into that. 118 00:07:46,880 --> 00:07:50,360 Speaker 1: Because this idea that genetics that we interpret them in 119 00:07:50,400 --> 00:07:54,760 Speaker 1: a reductive and deterministic way, i e. The genome tells 120 00:07:54,840 --> 00:07:59,120 Speaker 1: us everything, that data set is us. You're rejecting that, 121 00:07:59,280 --> 00:08:02,400 Speaker 1: and you're reject it in the health conversation. But you know, 122 00:08:02,440 --> 00:08:06,000 Speaker 1: as a Native American in particular, why are you rejecting 123 00:08:06,040 --> 00:08:10,920 Speaker 1: this idea of genetic determinism for ancestry. Well, let's think 124 00:08:11,040 --> 00:08:14,840 Speaker 1: back just a few years ago. This is Elizabeth Warren. 125 00:08:15,040 --> 00:08:18,800 Speaker 1: What are the facts you can absolutely have a Native 126 00:08:18,800 --> 00:08:21,960 Speaker 1: American ancestry in your pedigree. Oh my gosh, it feels 127 00:08:21,960 --> 00:08:24,240 Speaker 1: like actually forever ago. But a few years ago when 128 00:08:24,280 --> 00:08:29,360 Speaker 1: Elizabeth Warren announced that she had a DNA test that 129 00:08:29,440 --> 00:08:36,680 Speaker 1: showed her supposed Native American ancestry. And Okay, let's unpack 130 00:08:36,760 --> 00:08:39,200 Speaker 1: this a little bit further. Okay, let's go there, let's 131 00:08:39,240 --> 00:08:43,280 Speaker 1: get it. Let's go yes right now. Due to historical 132 00:08:43,400 --> 00:08:46,800 Speaker 1: distrust and I'm not talking about centuries ago or decades ago, 133 00:08:46,840 --> 00:08:49,960 Speaker 1: I'm talking about just in the past few years, Native 134 00:08:49,960 --> 00:08:54,800 Speaker 1: Americans in the US have largely not contributed their genetic 135 00:08:54,840 --> 00:09:01,880 Speaker 1: information to research or willingly to genetic ancestry tests. So 136 00:09:02,040 --> 00:09:05,760 Speaker 1: because of that, there's not that much information that links 137 00:09:06,160 --> 00:09:10,440 Speaker 1: specific genetic factors to a particularly US tribal nation. So 138 00:09:10,480 --> 00:09:13,360 Speaker 1: where do they get that information from. They get it 139 00:09:13,559 --> 00:09:18,760 Speaker 1: from openly available bio markers from large scale diversity projects 140 00:09:18,880 --> 00:09:23,040 Speaker 1: about twenty years ago. We're here to celebrate the completion 141 00:09:23,080 --> 00:09:26,320 Speaker 1: of the first survey of the entire human genome. Without 142 00:09:26,360 --> 00:09:29,480 Speaker 1: a doubt, this is the most important, most wondrous map 143 00:09:30,080 --> 00:09:34,840 Speaker 1: ever produced by humankind. Today's announcement represents more than particularly 144 00:09:34,840 --> 00:09:40,720 Speaker 1: the Human Genome Diversity Project and also National Geographic Magazine 145 00:09:40,720 --> 00:09:44,640 Speaker 1: did the Genographic Project. And these were two projects that 146 00:09:44,679 --> 00:09:49,480 Speaker 1: were meant to sample worldwide populations, particularly indigenous peoples before 147 00:09:49,559 --> 00:09:54,000 Speaker 1: we disappeared. And I'm doing the air quotes, so there's 148 00:09:54,040 --> 00:09:58,640 Speaker 1: this huge rush to sort of sample as many indigenous 149 00:09:58,720 --> 00:10:00,959 Speaker 1: peoples before we were wiped off the base of the 150 00:10:01,040 --> 00:10:05,160 Speaker 1: planet due to colonial factors, no, not caring the fact 151 00:10:05,200 --> 00:10:07,400 Speaker 1: that our ways of life were disappearing. Now, we wanted 152 00:10:07,400 --> 00:10:10,320 Speaker 1: their genomes before we were dead and gone. And this 153 00:10:10,360 --> 00:10:14,040 Speaker 1: is like the same rhetoric that colonists have been trying 154 00:10:14,080 --> 00:10:17,800 Speaker 1: to cause our extinction for centuries, and this is just 155 00:10:17,840 --> 00:10:21,240 Speaker 1: like the genetic version of that. So what ended up 156 00:10:21,240 --> 00:10:28,319 Speaker 1: happening is researchers went into remote communities in central Amazon 157 00:10:28,480 --> 00:10:34,480 Speaker 1: area in Mexico in Central America, and they collected bio 158 00:10:34,520 --> 00:10:37,440 Speaker 1: markers from indigenous peoples, and they promised that they were 159 00:10:37,440 --> 00:10:41,640 Speaker 1: going to bring them medicines and deliver cures for conditions 160 00:10:41,679 --> 00:10:46,959 Speaker 1: that besieged them. They took their blood, and did they 161 00:10:47,120 --> 00:10:52,160 Speaker 1: uphold their promises. No. So when you think about tests 162 00:10:52,720 --> 00:10:57,880 Speaker 1: like twenty three and ancestry, they're using these openly sourced 163 00:10:57,880 --> 00:11:05,200 Speaker 1: bio markers from disinphra chries, disempowered, exploited indigenous groups that 164 00:11:05,240 --> 00:11:09,199 Speaker 1: are south of the US border, and they're using these 165 00:11:09,240 --> 00:11:13,480 Speaker 1: markers and also Asian biomarkers, which is a totally different 166 00:11:13,480 --> 00:11:21,320 Speaker 1: part of the world to infer statistically statements about US ancestry, 167 00:11:21,440 --> 00:11:26,400 Speaker 1: forgetting the fact that every single indigenous group has their 168 00:11:26,440 --> 00:11:32,800 Speaker 1: own distinct cultural and genetic histories. And those bio markers 169 00:11:32,880 --> 00:11:38,280 Speaker 1: that supposedly realified a story by Lizeth Warren, those biomarkers 170 00:11:38,360 --> 00:11:40,679 Speaker 1: don't really show anything except for the fact that she 171 00:11:40,960 --> 00:11:46,920 Speaker 1: perhaps had a statistical relationship with one of maybe fifty 172 00:11:47,000 --> 00:11:51,840 Speaker 1: people in the Mexico region of the globe. Has nothing 173 00:11:51,840 --> 00:11:55,920 Speaker 1: to do with the answert she is trying to claim. 174 00:11:56,000 --> 00:12:01,080 Speaker 1: And it's also horrible because Native American rights are tied 175 00:12:01,640 --> 00:12:07,319 Speaker 1: to blood quantum rules and blood quantum rules are usually 176 00:12:07,720 --> 00:12:12,640 Speaker 1: derived by a person's lineage, like can they prove that 177 00:12:12,720 --> 00:12:17,720 Speaker 1: they have a direct grandmother or a parent or more 178 00:12:18,000 --> 00:12:21,079 Speaker 1: even distant ancestor who was a member of that community. 179 00:12:22,920 --> 00:12:27,840 Speaker 1: But these are kinship structures derived from genealogy, not by blood. 180 00:12:28,080 --> 00:12:30,920 Speaker 1: It's just a misno more that they're called blood quantum rules. 181 00:12:31,480 --> 00:12:34,640 Speaker 1: But let's remember the reason why blood quantum rules started. 182 00:12:34,920 --> 00:12:39,160 Speaker 1: It was a means for deluding our rights and our 183 00:12:39,200 --> 00:12:44,640 Speaker 1: claims to sovereign resources that are actually supposed to be 184 00:12:44,679 --> 00:12:47,880 Speaker 1: given to us by treaty in exchange for our lands. 185 00:12:49,880 --> 00:12:54,160 Speaker 1: And by using these unequated rules, were deluding our rights 186 00:12:54,200 --> 00:12:58,040 Speaker 1: to those claims and then to reify it by these 187 00:12:58,120 --> 00:13:02,320 Speaker 1: genetic ancestry tests. It's just horrible. So I want to 188 00:13:02,559 --> 00:13:06,840 Speaker 1: pause and rewind for a few thoughts. The theft of 189 00:13:07,040 --> 00:13:15,120 Speaker 1: blood is literally vampiric, right, and the idea of continuing 190 00:13:15,720 --> 00:13:19,520 Speaker 1: to extract and exploit, which are fancy words for steel, 191 00:13:20,120 --> 00:13:24,440 Speaker 1: is a continuation of colonial behavior. So it sounds to 192 00:13:24,440 --> 00:13:27,000 Speaker 1: me like what you're describing as a as a vampiric 193 00:13:27,640 --> 00:13:32,040 Speaker 1: genetic colonialism. It's interesting that you use that term vampiric 194 00:13:32,160 --> 00:13:36,840 Speaker 1: because when these large scale diversity projects were announced, global 195 00:13:36,880 --> 00:13:40,120 Speaker 1: Indigenous populations at least six hundred of them actually went 196 00:13:40,160 --> 00:13:44,120 Speaker 1: to the United Nations and actually asked for the cessation 197 00:13:44,200 --> 00:13:48,920 Speaker 1: of these studies, and particularly called the genographic project a 198 00:13:49,040 --> 00:13:54,200 Speaker 1: vampire project because it was akin to vampire bats coming 199 00:13:54,240 --> 00:13:57,160 Speaker 1: in the middle of the night, stealing their blood and 200 00:13:57,200 --> 00:14:00,360 Speaker 1: then leaving when the morning came. And that's what felt like, 201 00:14:05,960 --> 00:14:09,720 Speaker 1: what do Indigenous people lose out on if they don't 202 00:14:09,800 --> 00:14:12,240 Speaker 1: have their data in their own hands, if they don't 203 00:14:12,280 --> 00:14:16,640 Speaker 1: have that data sovereignty. Well, first of all, if they 204 00:14:16,640 --> 00:14:19,680 Speaker 1: are not in charge of their data and they don't 205 00:14:19,720 --> 00:14:23,600 Speaker 1: have the same saying data decisions, then how are they 206 00:14:23,640 --> 00:14:27,640 Speaker 1: going to call for accountability to ensure that they're able 207 00:14:27,680 --> 00:14:31,760 Speaker 1: to benefit from the collection of their information. But then 208 00:14:31,880 --> 00:14:35,200 Speaker 1: also we have to worry about whether or not d 209 00:14:35,360 --> 00:14:40,280 Speaker 1: NA claims to indigenousity will be undermined. So, for instance, 210 00:14:40,600 --> 00:14:45,440 Speaker 1: there's a number of scholars that are tracking descendants of 211 00:14:45,640 --> 00:14:50,480 Speaker 1: Mate populations and First Nations Canada people that claim to 212 00:14:50,600 --> 00:14:55,080 Speaker 1: be members of tribal communities but really have no evidence, 213 00:14:55,720 --> 00:14:59,480 Speaker 1: but are able to claim rights that should be only 214 00:14:59,680 --> 00:15:03,320 Speaker 1: to Indigenous people's. And what that effectively does is that 215 00:15:03,560 --> 00:15:07,360 Speaker 1: undermines the strength of communities because it's not like these 216 00:15:07,480 --> 00:15:12,400 Speaker 1: rights to resources are unlimited. It's very finite. There's like 217 00:15:12,440 --> 00:15:14,880 Speaker 1: a flip side version of this with the one drop 218 00:15:15,000 --> 00:15:19,160 Speaker 1: rule in US history about who's black, and that was 219 00:15:19,320 --> 00:15:22,360 Speaker 1: used as a weapon to kind of exclude people from 220 00:15:22,400 --> 00:15:25,880 Speaker 1: the resource of the majority population. Well, you're not white, 221 00:15:26,160 --> 00:15:29,600 Speaker 1: therefore you go to the back of everything, every list. 222 00:15:30,200 --> 00:15:32,800 Speaker 1: And there's an irony in the modern times when we 223 00:15:32,800 --> 00:15:36,560 Speaker 1: have so much language about inclusivity and equity and diversity, 224 00:15:36,600 --> 00:15:41,480 Speaker 1: like the whole trifecta, that the resources of a colonial 225 00:15:41,600 --> 00:15:47,120 Speaker 1: government will be used to determine membership in an indigenous community, 226 00:15:47,160 --> 00:15:50,440 Speaker 1: and that at the very heart of sovereignty, probably more 227 00:15:50,480 --> 00:15:56,320 Speaker 1: than water or land, is self definition, right, self determination. 228 00:15:56,600 --> 00:15:59,080 Speaker 1: And so if this outside authority, which you've got mad 229 00:15:59,120 --> 00:16:02,760 Speaker 1: reason not to trust sto already now usurps your own 230 00:16:02,920 --> 00:16:08,280 Speaker 1: membership rules based on questionable and certainly in complete science, 231 00:16:09,920 --> 00:16:11,960 Speaker 1: that's real, messed up. That's that's where I landed. That's 232 00:16:12,160 --> 00:16:14,680 Speaker 1: all that built up to. That's just real. Know that 233 00:16:14,760 --> 00:16:19,240 Speaker 1: the cruelest joke is that US indigenous groups have taken 234 00:16:19,280 --> 00:16:21,920 Speaker 1: this system that was meant to delude us from our 235 00:16:22,040 --> 00:16:26,240 Speaker 1: rights to resources, and now we have used that same 236 00:16:26,320 --> 00:16:30,800 Speaker 1: system to define ourselves and even exclude others that should 237 00:16:30,840 --> 00:16:35,080 Speaker 1: be a part of our community. That is just intrinsic 238 00:16:35,080 --> 00:16:41,960 Speaker 1: colonialism reflected upon ourselves and the worst way possible. Wow Wow, 239 00:16:43,840 --> 00:16:52,920 Speaker 1: We'll be right back. What does your denay and Navajo 240 00:16:52,960 --> 00:16:57,400 Speaker 1: identity mean to you beyond your DNA? So if I 241 00:16:57,440 --> 00:17:02,040 Speaker 1: were to give my full introduction, Dini bazade I would 242 00:17:02,040 --> 00:17:11,760 Speaker 1: say can the Cheney, initially the Nain and then Crystal 243 00:17:11,840 --> 00:17:16,199 Speaker 1: Sissy Hitia. So what I have provided to you in 244 00:17:16,240 --> 00:17:21,879 Speaker 1: the first two three sentences as a description of my 245 00:17:22,000 --> 00:17:26,040 Speaker 1: four clans, so everyone that my mother, my father, and 246 00:17:26,080 --> 00:17:30,000 Speaker 1: my grandparents are related to. And then I introduced myself. 247 00:17:31,640 --> 00:17:37,240 Speaker 1: So I have given you my entire lineage through the 248 00:17:37,600 --> 00:17:41,760 Speaker 1: time since Navajo people have existed, everyone that I'm related to, 249 00:17:42,680 --> 00:17:48,240 Speaker 1: And by listening to my introduction and introducing ourselves to 250 00:17:48,280 --> 00:17:53,080 Speaker 1: each other, we get kinship ties like oh, this person 251 00:17:53,240 --> 00:17:56,800 Speaker 1: is my same clan, we are brothers and sisters, or 252 00:17:56,920 --> 00:17:59,840 Speaker 1: they are a related clan, this person is my cousin 253 00:18:00,359 --> 00:18:02,800 Speaker 1: in a way, and so we don't have like the 254 00:18:02,880 --> 00:18:07,480 Speaker 1: same nuclear family structures as we do in dominant cultures. 255 00:18:07,880 --> 00:18:13,560 Speaker 1: We have expanded kinship structures, and that's just beautiful because 256 00:18:13,720 --> 00:18:18,480 Speaker 1: it means that our family is just more as an 257 00:18:18,480 --> 00:18:23,280 Speaker 1: expanded unit. It also strikes me that the way we 258 00:18:23,320 --> 00:18:27,000 Speaker 1: introduce ourselves in dominant culture in the West is first person, 259 00:18:27,080 --> 00:18:31,680 Speaker 1: singular and disconnected from others. It's like, I'm very Toonday, 260 00:18:31,800 --> 00:18:36,880 Speaker 1: whatever next. And so your introduction and self definition was 261 00:18:36,960 --> 00:18:40,200 Speaker 1: in relation to those who may not even still be here. 262 00:18:45,760 --> 00:18:47,240 Speaker 1: I want to I want to get a little more 263 00:18:47,280 --> 00:18:50,480 Speaker 1: context on you. Tell me about your home. What was 264 00:18:50,520 --> 00:18:53,480 Speaker 1: it like growing up and where did you grow up? Okay, 265 00:18:53,520 --> 00:18:56,760 Speaker 1: these are great questions, and I first want to tackle 266 00:18:56,840 --> 00:19:00,840 Speaker 1: the assumption that all Indigenous peoples are based in their 267 00:19:00,880 --> 00:19:03,960 Speaker 1: home communities, because we are not. We have been forcibly 268 00:19:04,040 --> 00:19:09,680 Speaker 1: displaced economically and just geographically. And in this case, that 269 00:19:09,840 --> 00:19:14,240 Speaker 1: is my family history. My mother comes from Chanta, Arizona, 270 00:19:14,400 --> 00:19:17,320 Speaker 1: the northern region that's just pretty much closely Utah border. 271 00:19:17,520 --> 00:19:20,360 Speaker 1: And then my father comes from the Loop area, which 272 00:19:20,359 --> 00:19:24,280 Speaker 1: is in central Arizona. And he actually worked in the 273 00:19:24,359 --> 00:19:28,040 Speaker 1: Phoenix Indian Medical Center, which is the largest Indian health 274 00:19:28,080 --> 00:19:31,760 Speaker 1: service clinic in the entire US, but it's based in Phoenix. 275 00:19:32,240 --> 00:19:34,800 Speaker 1: But he wasn't a doctor. Neither of my parents went 276 00:19:34,960 --> 00:19:39,200 Speaker 1: to college, so I'm a first generation student. Even though 277 00:19:39,240 --> 00:19:43,600 Speaker 1: I was more economically well situated than other members of 278 00:19:43,600 --> 00:19:46,880 Speaker 1: my family. I lived in the ghetto of West Phoenix, 279 00:19:48,840 --> 00:19:51,639 Speaker 1: and anyway, I was like the only Native kid in 280 00:19:51,920 --> 00:19:55,200 Speaker 1: all my levels of school. It wasn't until I hit 281 00:19:55,840 --> 00:20:00,119 Speaker 1: really high school and college that I started interacting with 282 00:20:00,240 --> 00:20:05,400 Speaker 1: other Native American students, and the reception was cold. Like 283 00:20:05,480 --> 00:20:09,720 Speaker 1: I was non rez I was non res Translate that 284 00:20:09,840 --> 00:20:13,000 Speaker 1: for me, Oh, I was not born and raped. I 285 00:20:13,080 --> 00:20:16,040 Speaker 1: was on a reservation. I didn't have to say little experience. 286 00:20:16,760 --> 00:20:20,600 Speaker 1: I was not as hardcore as they were. I was 287 00:20:20,640 --> 00:20:23,280 Speaker 1: an apple right on the outside, white on the inside. 288 00:20:23,640 --> 00:20:26,920 Speaker 1: Oh you had apples, we had oreos. Black on the outside, 289 00:20:26,920 --> 00:20:29,320 Speaker 1: white on the inside. It's why is it always food? 290 00:20:32,119 --> 00:20:36,000 Speaker 1: I went to one of the top ranked biomedical research 291 00:20:36,080 --> 00:20:41,840 Speaker 1: universities in the world, and God, it was blatantly obvious that, 292 00:20:42,000 --> 00:20:45,439 Speaker 1: despite my accomplishments, that I was recruited because I was brown, 293 00:20:45,520 --> 00:20:49,120 Speaker 1: and particularly because I was Native American. They weren't interested 294 00:20:49,160 --> 00:20:52,159 Speaker 1: in my training or interested in the fact that I 295 00:20:52,240 --> 00:20:57,840 Speaker 1: wanted to contribute something to my own people. You've heard 296 00:20:57,840 --> 00:21:02,119 Speaker 1: the term quarter like crisis. Okay, it was about that age, 297 00:21:02,400 --> 00:21:05,520 Speaker 1: and I was questioning a lot about my identity as 298 00:21:05,600 --> 00:21:10,480 Speaker 1: an Indigenous person occupying a white dominated space of academia 299 00:21:10,640 --> 00:21:16,440 Speaker 1: and science as a budding researcher, did I have the 300 00:21:17,200 --> 00:21:21,080 Speaker 1: wherewithal to make it as a scientist. And then also 301 00:21:21,520 --> 00:21:24,199 Speaker 1: just in terms of my own life land, if I 302 00:21:24,240 --> 00:21:28,440 Speaker 1: were to start again from scratch as a graduate student, 303 00:21:29,040 --> 00:21:33,680 Speaker 1: would I be successful or am I just delaying some 304 00:21:34,760 --> 00:21:40,160 Speaker 1: inevitable truth that maybe I wasn't good enough. That type 305 00:21:40,160 --> 00:21:43,320 Speaker 1: of reckoning is hard to do when in you're young 306 00:21:43,359 --> 00:21:46,280 Speaker 1: and mid twenties, and that's something that I feel like 307 00:21:46,520 --> 00:21:49,560 Speaker 1: scholars of color have at some point in their careers. 308 00:21:53,280 --> 00:21:58,000 Speaker 1: But what spurred my change was the realization that if 309 00:21:58,000 --> 00:22:00,280 Speaker 1: I were to complete my PhD in cancer by ology, 310 00:22:00,359 --> 00:22:05,080 Speaker 1: and if I were to invent something that fundamentally changed 311 00:22:05,560 --> 00:22:10,960 Speaker 1: cancer therapies, and whatever I invented went through all of 312 00:22:11,000 --> 00:22:14,600 Speaker 1: the phases of clinical trialing and made it to market, 313 00:22:14,920 --> 00:22:19,160 Speaker 1: there was like a heart wrenching feeling that it wouldn't 314 00:22:19,200 --> 00:22:25,360 Speaker 1: benefit my own people, That it would benefit rich, effluent 315 00:22:25,480 --> 00:22:30,000 Speaker 1: people first, long before it would benefit my own people. 316 00:22:31,720 --> 00:22:35,600 Speaker 1: As a brown person, and the sciences like I need 317 00:22:35,640 --> 00:22:39,399 Speaker 1: to do better, and I actually ended up switching my 318 00:22:39,520 --> 00:22:45,159 Speaker 1: field to bioethics and genetics and definitely feel the direct 319 00:22:45,200 --> 00:22:52,600 Speaker 1: impact of my research. Now, you so generously described me 320 00:22:52,800 --> 00:22:56,000 Speaker 1: as a scientist and an activist in your introduction of me, 321 00:22:56,480 --> 00:23:00,560 Speaker 1: and being called a scientists activists is actually some thing that, 322 00:23:00,800 --> 00:23:03,560 Speaker 1: depending on who you're talking to, can either be a 323 00:23:03,600 --> 00:23:08,960 Speaker 1: compliment or an insult, because supposedly people feel like science 324 00:23:08,960 --> 00:23:14,639 Speaker 1: should be objective and that there is no room for 325 00:23:15,440 --> 00:23:21,640 Speaker 1: conversations about racism and inequities in science, and to state 326 00:23:21,760 --> 00:23:25,959 Speaker 1: anything otherwise is apparently anti science. I can't tell you 327 00:23:26,000 --> 00:23:29,760 Speaker 1: how many times I've been called as a scientist anti 328 00:23:29,760 --> 00:23:35,119 Speaker 1: science by people who had no idea anything about science 329 00:23:35,160 --> 00:23:41,679 Speaker 1: as a field. We have to really question when we 330 00:23:41,720 --> 00:23:47,960 Speaker 1: over it economize science versus anti science, or even science 331 00:23:48,760 --> 00:23:55,120 Speaker 1: as being equated with objectivity, because when we take it apart, 332 00:23:55,840 --> 00:24:01,360 Speaker 1: when we're talking about humans, humans are messy. Science itself 333 00:24:01,720 --> 00:24:07,600 Speaker 1: is messy. It is not objective. It is completely dependent 334 00:24:07,800 --> 00:24:12,600 Speaker 1: on biases. Decisions that are made at the federal level 335 00:24:12,840 --> 00:24:17,159 Speaker 1: of what types of science are worthwhile for funding or 336 00:24:17,240 --> 00:24:22,040 Speaker 1: in terms of what types of research is deemed worthwhile 337 00:24:22,119 --> 00:24:26,520 Speaker 1: those are non objective decisions. Yeah, we all bring our 338 00:24:26,560 --> 00:24:29,240 Speaker 1: perspective to that stuff and have the idea there's a 339 00:24:29,280 --> 00:24:32,760 Speaker 1: neutral thing floating out in the demilitarized zones of all 340 00:24:32,800 --> 00:24:35,880 Speaker 1: of our minds called science is a myth. So thank 341 00:24:35,920 --> 00:24:39,560 Speaker 1: you for breaking that down. A lot of folks who 342 00:24:39,600 --> 00:24:43,000 Speaker 1: look at the tech world they see algorithmic bias. They 343 00:24:43,080 --> 00:24:47,000 Speaker 1: see hiring algorithms which don't have the right data leading 344 00:24:47,000 --> 00:24:50,359 Speaker 1: to the exclusion of women. They see policing algorithms sending 345 00:24:50,400 --> 00:24:53,240 Speaker 1: people back to prison who really are ready to come 346 00:24:53,400 --> 00:24:59,959 Speaker 1: back home. There's medical research value to having diversity of data. 347 00:25:00,080 --> 00:25:03,400 Speaker 1: Yet you've raised a lot of red flags around this 348 00:25:03,480 --> 00:25:08,479 Speaker 1: call for data diversity. Why should we be concerned and 349 00:25:08,520 --> 00:25:11,840 Speaker 1: what's your experience as an indigenous person taught you? So? 350 00:25:12,000 --> 00:25:16,000 Speaker 1: Diversity and inclusion is not the same as equity. We 351 00:25:16,080 --> 00:25:20,199 Speaker 1: have to make sure that those terms are disentangled and 352 00:25:20,240 --> 00:25:23,480 Speaker 1: that we really pay close attention to what we mean 353 00:25:23,520 --> 00:25:27,040 Speaker 1: by equity. But then the question is to what end 354 00:25:28,400 --> 00:25:34,840 Speaker 1: and who actually benefits? And in the scenarios that I described, 355 00:25:35,800 --> 00:25:39,800 Speaker 1: twenty plus years after data has been extracted from Indigenous peoples, 356 00:25:40,680 --> 00:25:43,960 Speaker 1: the people who have largely benefited are not the community 357 00:25:43,960 --> 00:25:48,280 Speaker 1: members that provided the blood. It's for profit companies like 358 00:25:48,359 --> 00:25:55,440 Speaker 1: ancestry and three and me. Ancestry since seventeen they posted 359 00:25:56,000 --> 00:26:01,520 Speaker 1: every holiday quarter profits over a billion dollars, So this 360 00:26:01,600 --> 00:26:06,080 Speaker 1: is reflected of people wanting to gift direct to consumer 361 00:26:06,080 --> 00:26:12,080 Speaker 1: genetic ancestry tests as Christmas gifts, literally paying to give 362 00:26:12,200 --> 00:26:16,399 Speaker 1: up your data to feed the algorithms that these companies 363 00:26:16,440 --> 00:26:20,600 Speaker 1: are trying to develop. And then recently Ancestry was acquired 364 00:26:20,720 --> 00:26:25,800 Speaker 1: by a venture capitalist firm for six billion dollars. Now 365 00:26:26,000 --> 00:26:30,400 Speaker 1: twenty and three and Me also has interest in collecting 366 00:26:30,680 --> 00:26:35,960 Speaker 1: Bible markers for Native American people's. These companies and other 367 00:26:36,160 --> 00:26:42,320 Speaker 1: genetic ancestry companies have expressed interest in creating Native American 368 00:26:42,520 --> 00:26:49,359 Speaker 1: specific platforms so that they can more accurately assess what 369 00:26:49,560 --> 00:26:55,639 Speaker 1: percentage membership you are by blood. If we think about 370 00:26:56,640 --> 00:26:59,840 Speaker 1: what we know about genetic variants contribute to disease, the 371 00:27:00,080 --> 00:27:03,560 Speaker 1: lois hanging fruit has already been picked. We already know 372 00:27:03,640 --> 00:27:10,280 Speaker 1: the common variants contributing to things like gastric cancers and diabetes. 373 00:27:11,119 --> 00:27:15,359 Speaker 1: The next sort of innovation is going to be in 374 00:27:15,520 --> 00:27:19,240 Speaker 1: rare variants or in variants that haven't been yet discovered 375 00:27:19,520 --> 00:27:23,560 Speaker 1: in populations like our own and that's also like stay 376 00:27:23,640 --> 00:27:28,119 Speaker 1: tuned to the term like discovery, right, because these terms 377 00:27:28,119 --> 00:27:34,879 Speaker 1: are very intricately aligned with colonial language of discovery and 378 00:27:35,080 --> 00:27:39,159 Speaker 1: of our people's Hello Columbus. Yeah, exactly. We have to 379 00:27:39,359 --> 00:27:42,480 Speaker 1: really think about these direct parallels when we talk about 380 00:27:43,240 --> 00:27:49,920 Speaker 1: vanishing populations and we talk about discovering variation. So it's 381 00:27:49,960 --> 00:27:54,280 Speaker 1: interesting a lot of these drives for collecting data from 382 00:27:54,920 --> 00:27:59,760 Speaker 1: diverse groups is tied with these long term aims and 383 00:28:00,640 --> 00:28:04,760 Speaker 1: some today down the road, precision and genomic medicine is 384 00:28:04,840 --> 00:28:09,440 Speaker 1: going to improve health for all, but it's the pathway 385 00:28:09,520 --> 00:28:12,800 Speaker 1: is not clear. I want to give a really old 386 00:28:12,840 --> 00:28:16,200 Speaker 1: reference to a South Park episode? Can I do that? Yes? 387 00:28:16,359 --> 00:28:19,400 Speaker 1: You just you opened up my heart? I love South Park. 388 00:28:19,520 --> 00:28:23,520 Speaker 1: Let's go okay, So keep in mind I haven't watched 389 00:28:23,520 --> 00:28:26,320 Speaker 1: the South Park in years, but there's a classic episode 390 00:28:26,320 --> 00:28:29,680 Speaker 1: with the underpants gnomes co lactant kind of pants. Just phose? When? 391 00:28:30,320 --> 00:28:35,480 Speaker 1: So what's phase two? H what's phase two? Well? The 392 00:28:35,840 --> 00:28:41,320 Speaker 1: thrill of profit? I don't get it. Yes, yes, phase 393 00:28:41,400 --> 00:28:49,400 Speaker 1: one collect underpants, phase two question mark phase three profits. Yes, yeah, 394 00:28:49,520 --> 00:28:56,320 Speaker 1: it's the same thing with precision genomics. It's like step 395 00:28:56,400 --> 00:29:01,880 Speaker 1: one collect bio markers. From underrepresented people Step two question 396 00:29:01,920 --> 00:29:04,680 Speaker 1: marks Scept three. They're supposed to be some benefits to 397 00:29:04,760 --> 00:29:08,720 Speaker 1: prove to the individuals both no clear pathway and in actuality, 398 00:29:09,000 --> 00:29:12,280 Speaker 1: the real direct benefit is to drug companies because they 399 00:29:12,320 --> 00:29:16,320 Speaker 1: have a vested commercial interest in profiting from They're the 400 00:29:16,440 --> 00:29:23,200 Speaker 1: underpants gnomes of genetics. Oh my gosh, you gotta keep 401 00:29:23,280 --> 00:29:27,800 Speaker 1: using that. That's amazing. Crystal's got more thoughts on democracy 402 00:29:27,840 --> 00:29:31,560 Speaker 1: and self determination that real citizen talk after the break. 403 00:29:40,680 --> 00:29:42,960 Speaker 1: A lot of this stuff you were saying about ownership 404 00:29:43,120 --> 00:29:46,480 Speaker 1: of data, and you know, without it you don't have accountability. 405 00:29:46,960 --> 00:29:50,560 Speaker 1: For one, I'm like, is she talking about indigenous genomic data? 406 00:29:50,600 --> 00:29:53,640 Speaker 1: Is she talking about my Facebook data? Right? The parallels 407 00:29:53,640 --> 00:29:58,400 Speaker 1: are really really obvious. But we're not talking about Facebook, 408 00:29:58,440 --> 00:30:01,200 Speaker 1: but we we still are in some ways that we're 409 00:30:01,200 --> 00:30:05,880 Speaker 1: still talking about self determination and power exactly. But Facebook 410 00:30:05,960 --> 00:30:10,680 Speaker 1: data usually has the risks centered on the individual. So 411 00:30:10,920 --> 00:30:15,200 Speaker 1: your search history usually uniquely identifies you your own personal 412 00:30:15,240 --> 00:30:21,719 Speaker 1: preferences with genomic information. Though genomic data, that's biological information 413 00:30:21,880 --> 00:30:26,280 Speaker 1: that links you and everyone you're related to. Let's think 414 00:30:26,320 --> 00:30:30,840 Speaker 1: about third party ancestry test sites. So these are a 415 00:30:30,920 --> 00:30:35,320 Speaker 1: third party databases in which people can take their results 416 00:30:35,320 --> 00:30:39,800 Speaker 1: from twenty three and ancestry and then deposited into a 417 00:30:39,920 --> 00:30:44,480 Speaker 1: free database. And these databases are of interest for law 418 00:30:44,600 --> 00:30:50,280 Speaker 1: enforcement agencies, and in fact, law enforcement agencies they used 419 00:30:50,480 --> 00:30:54,000 Speaker 1: databases like this to identify the Golden State killer. The 420 00:30:54,120 --> 00:30:58,040 Speaker 1: answer was and always was going to be in the 421 00:30:58,160 --> 00:31:02,080 Speaker 1: d N A we knew we could and should solve 422 00:31:02,160 --> 00:31:08,200 Speaker 1: it using the most innovative DNA technology available at this time. 423 00:31:08,880 --> 00:31:11,760 Speaker 1: We found the needle in the haystack, and it was 424 00:31:11,880 --> 00:31:16,680 Speaker 1: right here in Sacramento. Now, think about our communities, communities 425 00:31:16,720 --> 00:31:19,600 Speaker 1: a color like yours and mine, or like indigenous communities 426 00:31:19,600 --> 00:31:25,280 Speaker 1: in particular. We have larger family sizes, smaller generation gaps 427 00:31:26,560 --> 00:31:31,320 Speaker 1: one person's DNA. I have a hundred first cousins alone. 428 00:31:32,000 --> 00:31:35,200 Speaker 1: I can't imagine how many third cousins I have. So 429 00:31:35,480 --> 00:31:38,760 Speaker 1: I would be upset if you know some person I've 430 00:31:38,760 --> 00:31:42,520 Speaker 1: never been met before decided to give up their information, 431 00:31:42,680 --> 00:31:47,960 Speaker 1: my information to a pharmaceutical company or another company. And 432 00:31:48,160 --> 00:31:51,000 Speaker 1: I'd be further upset if that information was used by 433 00:31:51,000 --> 00:31:55,000 Speaker 1: a federal agency to aid in racial genetic profiling. YO, 434 00:31:55,280 --> 00:31:58,280 Speaker 1: I'm right there with you, and I think the idea 435 00:31:58,400 --> 00:32:03,440 Speaker 1: that my scent is not mine alone to give because 436 00:32:03,520 --> 00:32:07,360 Speaker 1: others are implicated in the consequences of that decision. Makes 437 00:32:07,360 --> 00:32:10,720 Speaker 1: a ton of sense, and it's so intimate. If the 438 00:32:10,760 --> 00:32:16,600 Speaker 1: first step was acknowledge the economic value of the data, 439 00:32:16,800 --> 00:32:23,440 Speaker 1: then there's presumed compensation do for use of this, and 440 00:32:23,520 --> 00:32:28,640 Speaker 1: potentially even collective compensation because the connections are beyond the 441 00:32:28,680 --> 00:32:32,000 Speaker 1: individual in this case, What do you think about the 442 00:32:32,040 --> 00:32:35,400 Speaker 1: implication that people should be paid because of the economic 443 00:32:35,480 --> 00:32:40,200 Speaker 1: value of their data. So, I know ephesis in general 444 00:32:40,720 --> 00:32:45,480 Speaker 1: do not like these conversations of attaching commercial value to 445 00:32:46,080 --> 00:32:49,600 Speaker 1: We're making ethicis mad? Okay? Yeah, But I I want 446 00:32:49,640 --> 00:32:51,920 Speaker 1: to add the flip side of this, because we know 447 00:32:52,400 --> 00:32:56,960 Speaker 1: that commercial exploitation is tied with genomic data exploitation. Therefore, 448 00:32:57,640 --> 00:33:01,680 Speaker 1: if we are able to attach a commercial value to 449 00:33:02,040 --> 00:33:05,880 Speaker 1: indigenous DNA, which is a scarce commodity that's incredibly important, 450 00:33:06,480 --> 00:33:09,640 Speaker 1: then we should be able to create a dollar value 451 00:33:10,200 --> 00:33:16,040 Speaker 1: on the exploitation of our people's DNA. And that is 452 00:33:16,040 --> 00:33:19,000 Speaker 1: a call to justice. And we really should be talking 453 00:33:19,040 --> 00:33:23,040 Speaker 1: about benefit sharing as a means of profit sharing and 454 00:33:23,080 --> 00:33:26,240 Speaker 1: calling onto companies like drug companies that if you want 455 00:33:26,280 --> 00:33:30,080 Speaker 1: to collect our information and profit from it, then you 456 00:33:30,160 --> 00:33:33,440 Speaker 1: need to be sure that the people contributing that information 457 00:33:33,920 --> 00:33:37,120 Speaker 1: also benefit and if you can't give us a portion 458 00:33:37,200 --> 00:33:40,840 Speaker 1: of that profit, then we need to call into question 459 00:33:41,240 --> 00:33:48,080 Speaker 1: your practices. Yo, can you tell me about where you're 460 00:33:48,080 --> 00:33:54,280 Speaker 1: working now and what's the Native Bio Data Consortium. The 461 00:33:54,400 --> 00:33:59,800 Speaker 1: Native Bio Data Consertion is an Indigenous led research nonprofit 462 00:34:00,640 --> 00:34:06,240 Speaker 1: that started off as a biological and data repository. What 463 00:34:06,280 --> 00:34:12,920 Speaker 1: we wanted was to ensure that samples that were collected 464 00:34:12,960 --> 00:34:18,440 Speaker 1: from mimmunity members actually benefited those community members. And we 465 00:34:18,560 --> 00:34:22,760 Speaker 1: wanted to create a research institution in which the research 466 00:34:22,840 --> 00:34:27,640 Speaker 1: questions were driven by community members interests. And these type 467 00:34:27,640 --> 00:34:32,960 Speaker 1: of research questions probably more proximately relate to differences in 468 00:34:33,280 --> 00:34:37,120 Speaker 1: disease and conditions in their communities than a research question 469 00:34:37,160 --> 00:34:41,200 Speaker 1: that is driven by an outside researcher. So they're the 470 00:34:41,239 --> 00:34:48,240 Speaker 1: ones that understand that environmental changes have contributed to health. 471 00:34:49,280 --> 00:34:53,880 Speaker 1: They're the ones that understand that lifestyle and diet changes 472 00:34:53,960 --> 00:34:57,320 Speaker 1: that have been imposed upon them are going to contribute 473 00:34:57,320 --> 00:35:00,880 Speaker 1: to differences in health that perhaps the West turned starchy 474 00:35:00,920 --> 00:35:05,040 Speaker 1: diets are different from the more agrarian lifestyles that they 475 00:35:05,080 --> 00:35:09,080 Speaker 1: had for centuries beforehand. These are factors that are often 476 00:35:09,200 --> 00:35:12,400 Speaker 1: missing when we just look at precision medicine in a 477 00:35:12,480 --> 00:35:18,000 Speaker 1: genemic only framework, we're missing those cultural factors. If you're 478 00:35:18,120 --> 00:35:22,120 Speaker 1: asking the wrong question, it doesn't matter how precise your 479 00:35:22,160 --> 00:35:27,719 Speaker 1: answer is. Yes. What else are you spearheading? With the consortium? 480 00:35:27,800 --> 00:35:33,880 Speaker 1: We are also spearheading a lot of education initiatives. We 481 00:35:34,000 --> 00:35:39,000 Speaker 1: just finished a summer program called Indigit Data. Indigit Data, 482 00:35:39,280 --> 00:35:42,680 Speaker 1: I love that good job with the name, Thank you. 483 00:35:43,480 --> 00:35:46,400 Speaker 1: We were just so fortunate that we were able to 484 00:35:46,880 --> 00:35:51,520 Speaker 1: secure funding to create this one week workshop in which 485 00:35:51,600 --> 00:35:55,960 Speaker 1: we were able to bring together undergraduate and graduate Indigenous 486 00:35:56,000 --> 00:35:59,880 Speaker 1: students and talk to them about data science and careers 487 00:35:59,880 --> 00:36:02,960 Speaker 1: and data science, but then also data ethics and what 488 00:36:03,120 --> 00:36:06,279 Speaker 1: it means to actually assert indigenous data sovereignties in their 489 00:36:06,280 --> 00:36:11,080 Speaker 1: own communities. And it was amazing. We had, like the 490 00:36:11,120 --> 00:36:13,800 Speaker 1: first parts of the morning were devoted to guest lectures 491 00:36:13,800 --> 00:36:18,400 Speaker 1: who are all Indigenous, who are all amazing scholars leading 492 00:36:18,440 --> 00:36:22,560 Speaker 1: their own fields using data science and their own particularly 493 00:36:22,680 --> 00:36:26,640 Speaker 1: unique ways. And then in the afternoon we talked coding, 494 00:36:27,920 --> 00:36:34,160 Speaker 1: like actually coded using environmental data that we collected and 495 00:36:34,239 --> 00:36:37,760 Speaker 1: sequenced from tribal lands, and it was just so cool 496 00:36:37,840 --> 00:36:40,400 Speaker 1: to get the students to sort of get their hands 497 00:36:40,960 --> 00:36:45,000 Speaker 1: dirty in a sense with the data, and it really 498 00:36:45,040 --> 00:36:49,160 Speaker 1: just opened their eyes to the larger questions that we 499 00:36:49,239 --> 00:36:52,600 Speaker 1: discussed earlier, which is the fact that data is power. 500 00:36:55,040 --> 00:37:00,400 Speaker 1: Data is power. Data is also linked to disempower mournment. 501 00:37:01,200 --> 00:37:04,520 Speaker 1: And if we want to change the narrative, then we 502 00:37:04,600 --> 00:37:08,440 Speaker 1: need to change the next generation of data scientists that 503 00:37:08,480 --> 00:37:16,480 Speaker 1: come from our communities. What's the overall goal of educating 504 00:37:16,560 --> 00:37:22,319 Speaker 1: indigenous data scientists. De Colonization has to be done by 505 00:37:22,880 --> 00:37:29,960 Speaker 1: historically colonized people, no one else. So it's really interesting 506 00:37:30,239 --> 00:37:35,320 Speaker 1: when you have white academics who are looking to scholars 507 00:37:35,320 --> 00:37:38,200 Speaker 1: of color to figure out how to de colonize their 508 00:37:38,280 --> 00:37:44,000 Speaker 1: syllabi and oh my gosh, I have a brief story. 509 00:37:44,360 --> 00:37:47,080 Speaker 1: So I did a Twitter conference called de Colonized DNA 510 00:37:47,480 --> 00:37:50,799 Speaker 1: and it was in lined with National DNA DA and 511 00:37:50,840 --> 00:37:56,279 Speaker 1: it was really to bring voices of disenfranchised communities and 512 00:37:56,520 --> 00:38:00,759 Speaker 1: representative scholars to talk about how janet mix could be 513 00:38:00,960 --> 00:38:05,839 Speaker 1: reductionistic and you know, reinforce these power dynamics that really 514 00:38:05,880 --> 00:38:08,960 Speaker 1: need to be changed. And after that, a lot of 515 00:38:09,160 --> 00:38:12,640 Speaker 1: white people and a lot of journalists who work for 516 00:38:13,160 --> 00:38:16,879 Speaker 1: education journals reached out to me and they asked me, well, 517 00:38:16,880 --> 00:38:22,120 Speaker 1: what can we learn from indigenous peoples and what recommendations 518 00:38:22,120 --> 00:38:27,400 Speaker 1: would you give to white scholars for decolonizing their curricula. 519 00:38:28,120 --> 00:38:32,239 Speaker 1: And I'm like, step down, give your place up, and 520 00:38:32,320 --> 00:38:36,359 Speaker 1: allow a scholar of color, a colonized person, to take 521 00:38:36,400 --> 00:38:39,160 Speaker 1: your place, because ultimately these narratives need to come from us, 522 00:38:39,160 --> 00:38:43,240 Speaker 1: not you. Unsurprisingly, none of those interviews made it depress. 523 00:38:46,560 --> 00:38:51,320 Speaker 1: What we're about at large in this season is thinking 524 00:38:51,360 --> 00:38:55,960 Speaker 1: about how we use technology that serves people, not the 525 00:38:56,000 --> 00:38:59,279 Speaker 1: other way around, and that serves collective power, not just 526 00:38:59,360 --> 00:39:03,640 Speaker 1: selfish and of visual power. And when it comes to technology, 527 00:39:03,719 --> 00:39:08,760 Speaker 1: there's so much good intention around sort of pro civic, 528 00:39:08,880 --> 00:39:13,760 Speaker 1: pro democratic, small D movements like open source and open 529 00:39:13,880 --> 00:39:17,440 Speaker 1: data and community sharing. And there's this lens that says like, 530 00:39:17,520 --> 00:39:23,960 Speaker 1: democratizing access to technology makes it equal, makes that tool 531 00:39:24,080 --> 00:39:27,600 Speaker 1: more available to all, which is good. What are your 532 00:39:27,680 --> 00:39:31,960 Speaker 1: thoughts on that in terms of the future of technology 533 00:39:32,000 --> 00:39:35,799 Speaker 1: and and how it affects our access to power? Oh 534 00:39:35,800 --> 00:39:39,720 Speaker 1: my gosh, I have rebel against the phrase democratizing data 535 00:39:39,880 --> 00:39:45,360 Speaker 1: or democratizing science. What type of democracy are we talking 536 00:39:45,400 --> 00:39:49,359 Speaker 1: about here? Are we talking about the American system of democracy, 537 00:39:49,400 --> 00:39:54,000 Speaker 1: because the garbage fire that was the last election year 538 00:39:54,120 --> 00:39:56,600 Speaker 1: should show that this is not a model by which 539 00:39:56,719 --> 00:40:00,799 Speaker 1: we should follow. Any means of any past w equality. 540 00:40:01,719 --> 00:40:06,520 Speaker 1: To preference the American system of democracy over other forms 541 00:40:06,520 --> 00:40:11,879 Speaker 1: of democracy is a form of white colonial thinking. There 542 00:40:11,880 --> 00:40:14,920 Speaker 1: were other forms of democracy that we need to consider, 543 00:40:15,000 --> 00:40:18,600 Speaker 1: like indigenous systems of democracy that have long existed before 544 00:40:18,800 --> 00:40:26,320 Speaker 1: the American system of democracy. We also have to think 545 00:40:26,719 --> 00:40:35,000 Speaker 1: that any system that advocates for benefiting most is going 546 00:40:35,040 --> 00:40:41,960 Speaker 1: to disenfranchise small, underrepresenting communities like our own, like indigenous peoples, 547 00:40:43,200 --> 00:40:48,240 Speaker 1: it's going to continue to disenfranchise minority groups and substantiate 548 00:40:48,480 --> 00:40:53,959 Speaker 1: those power and balances. Democracy does not necessarily mean equality 549 00:40:54,520 --> 00:41:01,400 Speaker 1: or equity, and these definitions of equity should be community 550 00:41:01,520 --> 00:41:05,960 Speaker 1: driven and they're culturally specific. They should not be determined 551 00:41:06,080 --> 00:41:12,240 Speaker 1: by the dominant cultures. But let's also think carefully about 552 00:41:12,280 --> 00:41:16,960 Speaker 1: equality and equity. We have a lot of d e 553 00:41:17,080 --> 00:41:21,759 Speaker 1: I efforts, diversity inclusion and equity efforts across academia and 554 00:41:21,800 --> 00:41:25,680 Speaker 1: across industries. And we get the diversity part and we 555 00:41:25,719 --> 00:41:29,120 Speaker 1: get the inclusion part. Sure, you want our people's data, 556 00:41:29,360 --> 00:41:33,759 Speaker 1: that's that's nothing new. The equity portion though is key, 557 00:41:34,000 --> 00:41:37,200 Speaker 1: and equity is not equality. A seat at the table 558 00:41:37,280 --> 00:41:42,640 Speaker 1: is not the same thing as a voice at the table. Yeah, 559 00:41:42,960 --> 00:41:45,960 Speaker 1: you've done a lot, and I want to share that 560 00:41:46,120 --> 00:41:49,880 Speaker 1: burden and that opportunity. We set up this show to 561 00:41:50,040 --> 00:41:53,719 Speaker 1: encourage people to do things, and on the topic of 562 00:41:54,640 --> 00:41:58,799 Speaker 1: balancing power and on the topic of data more specifically. 563 00:41:59,560 --> 00:42:02,480 Speaker 1: But lay out your thoughts on what we should be 564 00:42:02,560 --> 00:42:08,600 Speaker 1: doing as kindly as I can state this. If you 565 00:42:08,680 --> 00:42:13,640 Speaker 1: are a member that has been historically empowered at this 566 00:42:13,719 --> 00:42:17,520 Speaker 1: point in time, especially when it comes to topics related 567 00:42:17,520 --> 00:42:21,520 Speaker 1: to racism and inequality, and I say this as kindly 568 00:42:21,600 --> 00:42:24,080 Speaker 1: as I can, you need to sit down and shut 569 00:42:24,160 --> 00:42:30,920 Speaker 1: up and listen to the scholars, the people of color, 570 00:42:31,680 --> 00:42:38,360 Speaker 1: the communities and do what they recommend and follow their lead. 571 00:42:39,360 --> 00:42:43,400 Speaker 1: Decision making authorities need to shift to those that have 572 00:42:43,480 --> 00:42:49,520 Speaker 1: been historically disenfranchised. That's how we get changed. If you 573 00:42:49,600 --> 00:42:54,200 Speaker 1: want to advocate for change, you also need to provide 574 00:42:54,280 --> 00:42:57,439 Speaker 1: room for dissenting voices, even if it's hard to hear 575 00:42:58,480 --> 00:43:03,879 Speaker 1: mm hmm. Thank you, Crystal so see so much for 576 00:43:03,920 --> 00:43:09,400 Speaker 1: your time, your teachings, your talent, and your portmanteaus. I 577 00:43:09,480 --> 00:43:19,600 Speaker 1: really appreciate you. Thank you that word too. Crystal wants 578 00:43:19,640 --> 00:43:23,520 Speaker 1: people to listen. It's not enough just to raise awareness though, 579 00:43:25,200 --> 00:43:28,920 Speaker 1: when we think back to the citizening principles, Crystal wants 580 00:43:28,960 --> 00:43:31,960 Speaker 1: people to show up, but also to make space for 581 00:43:32,000 --> 00:43:34,320 Speaker 1: those who haven't been able to show up, who haven't 582 00:43:34,320 --> 00:43:39,480 Speaker 1: been able to citizen due to historical and systemic oppression. Now, 583 00:43:39,480 --> 00:43:42,000 Speaker 1: as we're building out these new systems built on data 584 00:43:42,040 --> 00:43:44,800 Speaker 1: and new technologies, we have to make sure we aren't 585 00:43:44,800 --> 00:43:49,240 Speaker 1: repeating the old methods of extraction and exploitation and disenfranchisement. 586 00:43:50,480 --> 00:43:53,760 Speaker 1: Neither a majority white government nor a majority white business 587 00:43:54,160 --> 00:43:57,800 Speaker 1: should be determining the tribal membership status of an Indigenous person. 588 00:43:58,480 --> 00:44:02,760 Speaker 1: That don't make any damn sense. We have this opportunity 589 00:44:02,800 --> 00:44:06,680 Speaker 1: to close gaps and undo harms caused by justice sort 590 00:44:06,719 --> 00:44:10,239 Speaker 1: of thinking, and start taking justice into account when we 591 00:44:10,360 --> 00:44:14,800 Speaker 1: use data. And after reflecting on both of these conversations 592 00:44:14,960 --> 00:44:18,319 Speaker 1: with Crystal and with Kasha, I think the answer to 593 00:44:18,440 --> 00:44:23,600 Speaker 1: data justice actually goes both ways. Yeah, we need more 594 00:44:23,680 --> 00:44:27,719 Speaker 1: diverse data sets, just like we need more diverse corporate boards, 595 00:44:27,760 --> 00:44:30,680 Speaker 1: but not just that, you know, we also need to 596 00:44:30,760 --> 00:44:33,960 Speaker 1: change the way corporations wield power, and we need a 597 00:44:34,040 --> 00:44:39,160 Speaker 1: data ecosystem where people have agency over their data, specifically 598 00:44:39,200 --> 00:44:42,800 Speaker 1: those people who have been cut out of or abused 599 00:44:42,840 --> 00:44:48,359 Speaker 1: by our current system. One of our pillars of How 600 00:44:48,400 --> 00:44:52,440 Speaker 1: to Citizen is restoring power to the people, and one 601 00:44:52,520 --> 00:44:55,360 Speaker 1: of the biggest gaps of tech is that it's used 602 00:44:55,360 --> 00:44:58,840 Speaker 1: to disempower folks from literally showing up for themselves and 603 00:44:58,920 --> 00:45:02,880 Speaker 1: for others. Because how we get misrepresented in data and 604 00:45:02,920 --> 00:45:08,000 Speaker 1: the effects that that can have on our choices. Both 605 00:45:08,080 --> 00:45:12,080 Speaker 1: Kasha and Crystal show us examples of people taking that 606 00:45:12,200 --> 00:45:17,800 Speaker 1: power back. When thinking about this show How to Citizen, 607 00:45:18,200 --> 00:45:21,440 Speaker 1: I think we need to keep breathing new life into 608 00:45:21,480 --> 00:45:25,600 Speaker 1: this citizen verb and apply the lessons of our guests 609 00:45:25,760 --> 00:45:30,520 Speaker 1: and evolve as well. So citizen, what does that mean? 610 00:45:31,760 --> 00:45:34,640 Speaker 1: I think it should also mean that we explicitly seek 611 00:45:34,760 --> 00:45:39,120 Speaker 1: to distribute power and resources to those long excluded from 612 00:45:39,200 --> 00:45:45,880 Speaker 1: systems of citizen it. Next week we dive into the 613 00:45:45,960 --> 00:45:49,719 Speaker 1: Mystic and I learned about the link between chicken farms, 614 00:45:49,880 --> 00:45:53,680 Speaker 1: blockchain logic, and tarot. I know that sounds like a 615 00:45:53,719 --> 00:45:57,640 Speaker 1: word salad, but trust me, it's a dope conversation. Picture 616 00:45:57,680 --> 00:46:00,919 Speaker 1: of the chicken how many steps it took? Because they're 617 00:46:01,000 --> 00:46:04,239 Speaker 1: like a phenomener like All the Chicken Got It sent 618 00:46:04,280 --> 00:46:08,799 Speaker 1: thousand steps in Let's Eat Chickens and blockchain Come on now, 619 00:46:10,760 --> 00:46:16,919 Speaker 1: m And now it's that time in the episode where 620 00:46:16,960 --> 00:46:20,239 Speaker 1: we share some actions that you can take. First, up 621 00:46:20,719 --> 00:46:25,920 Speaker 1: a thinking exercise, ask yourself, how much is my data 622 00:46:25,960 --> 00:46:28,640 Speaker 1: privacy worth to me? And how do I feel about 623 00:46:28,680 --> 00:46:32,799 Speaker 1: non consensual surveillance based on my data? Now, adding the 624 00:46:32,840 --> 00:46:36,319 Speaker 1: element of genetic information, how would you feel if any 625 00:46:36,360 --> 00:46:40,480 Speaker 1: of your biological relatives donated genetic info tied to you 626 00:46:40,560 --> 00:46:44,680 Speaker 1: that could be bought and sold. Next, I want you 627 00:46:44,719 --> 00:46:49,319 Speaker 1: to get informed about exploitative data collection historically and right 628 00:46:49,320 --> 00:46:52,760 Speaker 1: now we've linked to three articles in the show notes 629 00:46:52,800 --> 00:46:56,680 Speaker 1: and on our site. And then finally, here are some 630 00:46:56,719 --> 00:47:00,680 Speaker 1: ways to publicly participate. You can help empower or indigenous 631 00:47:00,719 --> 00:47:04,400 Speaker 1: scientists working with tribal communities to ensure that the benefits 632 00:47:04,400 --> 00:47:08,120 Speaker 1: of biomedicine and public health go to indigenous people by 633 00:47:08,160 --> 00:47:12,239 Speaker 1: making a donation to the Native Biodata Consortium check them 634 00:47:12,239 --> 00:47:16,120 Speaker 1: out online, And help protect yourself and slow the market 635 00:47:16,160 --> 00:47:19,240 Speaker 1: for selling all of our data by installing the Global 636 00:47:19,320 --> 00:47:22,960 Speaker 1: Privacy Control. This is a feature of certain web browsers 637 00:47:22,960 --> 00:47:25,239 Speaker 1: that lets you signal to a site. You know, don't 638 00:47:25,280 --> 00:47:28,240 Speaker 1: be trading all my information and it's backed by law. 639 00:47:28,920 --> 00:47:31,400 Speaker 1: We've got linked to all this in the show notes 640 00:47:31,760 --> 00:47:35,600 Speaker 1: and on our website at how to citizen dot com. 641 00:47:35,719 --> 00:47:38,560 Speaker 1: Follow us on Instagram at how the Citizen and tag 642 00:47:38,680 --> 00:47:43,240 Speaker 1: us in your post about data or exploitation or anything. 643 00:47:43,239 --> 00:47:45,120 Speaker 1: We're not anything I don't want like posts about you 644 00:47:45,160 --> 00:47:47,640 Speaker 1: trying to do a TikTok dance or something, So one 645 00:47:47,719 --> 00:47:49,480 Speaker 1: or two of those might be fun make my life 646 00:47:49,520 --> 00:47:58,279 Speaker 1: more interesting. Thanks for listening, and keep citizen. How the 647 00:47:58,360 --> 00:48:00,839 Speaker 1: Citizen with baritune Day is a duction of I Heart 648 00:48:00,920 --> 00:48:05,000 Speaker 1: Radio Podcasts and dust Light Productions. Our executive producers are 649 00:48:05,080 --> 00:48:08,600 Speaker 1: Me Barrett tune Day, Thurston, Elizabeth Stewart, and Misha Usa. 650 00:48:09,040 --> 00:48:13,040 Speaker 1: Our senior producer is Tamika Adams, our producer is Ali Kilts, 651 00:48:13,440 --> 00:48:17,080 Speaker 1: and our assistant producer is Sam Paulson. Stephanie Cohen is 652 00:48:17,080 --> 00:48:20,720 Speaker 1: our editor, Valentino Rivera is our senior engineer, and Matthew 653 00:48:20,800 --> 00:48:24,440 Speaker 1: Laie is our apprentice. Original music by Andrew Eapen, with 654 00:48:24,480 --> 00:48:28,600 Speaker 1: additional original music for season three from Andrew Clawson. Additional 655 00:48:28,600 --> 00:48:32,480 Speaker 1: production help from Arwin Knicks. This episode was produced and 656 00:48:32,560 --> 00:48:35,959 Speaker 1: sound designed by Tamika Adams. Special thanks to Joel Smith 657 00:48:36,000 --> 00:48:38,759 Speaker 1: from I Heart Radio and Rachel Garcia at dust Light 658 00:48:38,840 --> 00:48:39,240 Speaker 1: Production