 Good afternoon, everyone. My name is Shobita Parthasarathy, and I am Professor of Public Policy and Director of the Science, Technology, and Public Policy Program here at the Ford School. STBP is an interdisciplinary university-wide program dedicated to training students, conducting cutting-edge research, and informing the public and policymakers on issues at the intersection of technology, science, equity, society, and public policy. If you'd like to learn more about our program, please visit our website, stpp.fordschool.umich.edu. For students interested in our graduate certificate program, there'll be a virtual information session on Monday, February 13th at noon, and you can sign up on our website. The next deadline to apply for the certificate is on March 1. I want to start today's event by acknowledging that the University of Michigan resides on the ancestral, traditional, and contemporary lands of the Anishinaabeg, the three-fire Confederacy of the Ojibwe, Odawa, and Potawatomi Nations, as well as the Wyandot Nation. In particular, the University resides on land gifted by the three nations and the Wyandot Nation, along with many other indigenous nations, in the 1817 Treaty of Fort Megs. We acknowledge that the language of gift in the original treaty entails mutual relationships between treaty parties, respect, and obligation on the part of the settlers, us. We advocate for indigenous struggles against ongoing settler colonization and strive for a decolonized future. My hope is that today's event is one step in that direction. I'm delighted to introduce Crystal Sose as our guest speaker. Dr. Sose is an indigenous geneticist and bioethicist of the Dine Navajo Nation. She's currently a president's post-doctoral fellow transitioning to an assistant professor in the School of Life Sciences at Arizona State University. As an advocate for indigenous genomic data sovereignty, she co-founded the first US indigenous-led biobank, a 501C3 nonprofit research institution called the Native Biodata Consortium, focusing on indigenous population genetics and bioethics. Her research interests are in integrating genomic and data approaches to assess indigenous variation contributing to health inequities. At the beginning of her research career, Dr. Sose developed and patented a combined targeted ultrasound imaging and chemotherapeutic drug delivery device for treating early metastasis in cancer. She soon switched fields to genetic epidemiology, public health, and bioethics after seeing the disparities in emerging genomics technologies. In other endeavors, Dr. Sose has co-led an ongoing longitudinal genetic study in a North Dakota tribal community and is currently global chair in Enrich, which stands for equity in indigenous research and innovation coordinating hub. She has master's degrees in bioethics from ASU and in genetic epidemiology from Vanderbilt and a PhD in genomics and health disparities also from Vanderbilt. After her talk, Dr. Sose will be in conversation with Joden Platt, an assistant professor of learning health sciences at Michigan Medicine. She's also a faculty affiliate of the STPP program. Dr. Platt's research focuses on informed consent in cancer and genomic studies and the ethical, legal, and social implications of public health genetics, newborn screening, and learning health systems. She is the founder and former chair of the American Public Health Association's Genomics Forum. This event is hosted by the Science, Technology, and Public Policy program in the Ford School of Public Policy. It is co-sponsored by a variety of programs across the University of Michigan, including the Center for Bioethics and Social Science and Medicine, the Science, Technology, and Society program, the Center for Racial Justice, the Inspire Rackham Interdisciplinary Workshop, Precision Health, the Life Sciences Institute, and the Ethical, Legal, and Social Implications of Genomics and Genetics Research. I also wanna thank our STPP staff who made this event possible, Kristen Bergard, Molly Kleinman, Mariam Negaran, and Annabella Vidrio. Dr. Sose will talk for 30 minutes, or sorry, 40 minutes. I'm not shrinking your time, I promise you. Then Dr. Platt will start us off with a few questions, and then we'll open it up to the audience. For those of you who are with us in person, you got note cards and pencils when you came in. You can write questions on your cards, and Molly and Kristen, who will raise their hands, I think Kristen is still maybe outside, she'll make herself known, will come around to collect them. If you need more note cards, you can just flag them down. For those of you watching online, you can send your questions to STPP at umesh.edu, and we will get them. Dr. Sose, I'm very excited for your talk and conversation with Dr. Platt today, and I will turn it over to you. I've had so much for that kind, kind introduction. I've had also for the great, warm and wonderful welcome. Shea, Crystal Sose, Shea. Shea. Shea. Shea. Shea. Shea. Shea. Shea. Thank you so much. So happy to be here to talk with you about community approaches to equity and genomics in health. So take a look at pretty much any publication about indigenous peoples in genomics, and this pretty much sums up the introduction. Then in order to ameliorate health disparities in indigenous communities, dot, dot, dot, we need more indigenous participation in genomic studies, as many genesis and genomicists. Yet, despite over a decade of efforts to increase the representation in data sets, still we have less than 1% of those research participants are of indigenous by affiliation. This leads us to a very important question, which I'm astounded, it was actually a surprise when I presented this statistic to a Society of Genealogists a couple of years ago. That we could talk about genetics all we want, but that doesn't change the simple fact that we also have to pay due diligence to the importance of structural factors as it relates to health. Right before the pandemic in 2018, the spending for an Indian Health Service participants was 2.4 times less than the national per capita average. And yet we tell indigenous participants all the time that they're going to miss out on the benefits of precision health if they do not engage. But this doesn't give adequate attention to the fact that there are reasons why indigenous peoples don't engage in genetics relating to power imbalances that created research harms to begin with. So it's really interesting that when we think about the field in terms of our research aims, be it in increasing diversity in data sets, be it improving machine learning models and algorithms, be it just improving health for indigenous peoples, that we always talk about this rhetoric of needing more data from indigenous peoples that are openly accessible. Yet this ignores the fact that there are recent and past unethical examples relating to conduct, misconduct of data collected from our communities, which has strained the trust relations resulting in tribal nation policies that restrict open data sharing. There are complex and sustained reasons why indigenous peoples may choose not to engage in research. These are incredibly interrelated and each one of these bullet points could take a whole semester just to unpacked. But just in general, you can sort of categorize reasons why indigenous peoples don't engage in terms of cultural and political reasons, but central to both of these circles is distrust, distrust of the research and distrust of the researchers. And it's really interesting that when we talk about research ethics as it relates to human subjects or human participants, as they should be called, we often talk about research ethics as start being at Tuskegee. And almost think of the Belmont principles as a panacea for preventing additional research harm. Yet we only have to look in the last 20 years alone to really understand that these ethical research controversies are occurring and they keep occurring and they're almost cyclical in nature such that we had in early 2000s news of samples from an indigenous community in British Columbia being used without tribal consent. And yet in 2018, we have concerns about the recent and ongoing large scale diversity project all of us research program encountered concerns from indigenous nations that they were also recruiting indigenous peoples without consent. And it's really interesting that we had these questions of who owns samples. They are now turning into who owns or stewards data. We still see examples of which tribal consent is being bypassed and we're starting to see the shift back to broad informed consent, which I'll talk about in a moment. But I really want to talk about this particular foundational data set that informs quite a bit of our precision health research today because I know that many PhD students are graduating from data in data science fields without understanding the history of the data sets that they utilize. So I want to start with just one example, which is the human genome diversity project which when it started in 1993 announced itself as a plan to map genetic variation of human populations worldwide. So this aim is about incredibly similar to any large scale diversity project you see today, but even then there was something insidious about this aim in which researchers in publications describe this as a race to sequence and sample indigenous peoples before they vanished. So the concern was that indigenous peoples their numbers were being reduced due to colonialism and also just plain concern that we were going to be extinct due to assimilation into other cultures, but it wasn't really an aim to prevent those colonial factors from impacting our ways of life and our existence. No, it was just a race to sample data before we disappeared. Unsurprisingly, global indigenous peoples worldwide didn't like this project and even then cited concerns related to gene patenting and bio-commercialization of indigenous peoples DNA. In 2005, HDDP published its quote unquote successful recruitment of 51 global populations to include five indigenous groups. And if you look at the map, those five indigenous groups are kind of in that magenta fuchsia color. And if you notice anything about the geography you notice that these are indigenous groups outside of the U.S. In groups in areas where indigenous peoples' own existence were disenfranchised by the colonial governments and yet this is where the data is being extracted and that data we can see in papers are being exported out of these countries to countries in the global North to include U.S. and also Europe. In response to this, again, we have now a response by indigenous peoples worldwide and a petition started of over 600 nations worldwide who called the United Nations to recommend cessation of these large-scale diversity projects. Again, citing concerns related to co-optation commercialization of their data. So brings us to the end of this little example of 2008, 2012 when we had the beginning of the emergence of these genetic ancestry tests. 23 in me and ancestry. 23 in me when it launched was announced Time Magazine's invention of the year. And yet, if you look at the histories of the data sets that they used to inform those tests, what do we see? We see human genome diversity project, hat map, 1,000 genomes, a number of which also utilize indigenous peoples from the global South. And this is an incredibly impactful quote because when we're talking about data as a good or resource, we're really thinking about data as emerging technologies utilizing particularly sequence data. And I love this quote by Dr. Caleb Fox who is a native Hawaiian geneticist who wrote, raw data, including digital sequence information derived from human genomes have emerged as a top global commodity. And we see the emergence of these data sets really quickly, genetic ancestry companies have amassed data sets in the millions. Such that one individual contributes to over 200 research studies to tell you a little bit of who is utilizing these data sets. But also here is we see the commercialization and use of those data sets. Ancestry posted a billion dollars in revenue every holiday quarter since 2017. So when you think about gifting the gift of one's history underneath your Christmas tree and a holiday season, those are contributing to sales from individuals buying in to this concept. And they turned around and sold to a venture capital firm called Blackstone for $4.7 billion in 2020. And it's really interesting because 23Me was under fire by the FDA because there was this interesting pivot. They amassed a lot of their data in their data set by utilizing genetic ancestry as a form of entertainment. But then turned around and have been selling that data and licensing it to pharmaceutical companies for like for instance, GlaxoSmithKline gaining rights to mine other data sets for drug targets at the rate of $300 million in investment. So there's an interesting question. Was this the aim all along to collect data and commercialize it and license it? Possibly, but then also we have to ask what has been the role of those indigenous peoples data during this entire process? And this is an interesting question because ancestry runs its own vlog. I don't know who uses vlogs still nowadays, but on YouTube for instance, they have a vlog to help you sort of unpack your own individual results. And their number one question according to their own vlog is why is my native American ancestry not showing up? Okay, so this is something that I think we have to realize is that these companies are promising an interchangeability of ancestral and personal identity. They're promoting this problematic causal relationship between genetics and self-identity. And drawing on public faith and science and scientific authority, consumers now think that they can make these statements about their own identities while believing them to be validated by science. So this is an interesting notion in which many people believe that indigeneity is grounded in biology, but that's not factual. Indigenous peoples existed predating genetics. We have our own cultural and social and political constructs relating to indigeneity and who we claim and who claims us, but those are not grounded in biology. They are grounded in cultural affiliations and who we claim for ourselves. And this is an excellent quote by Dr. Kim Tallbear who wrote the book, Native American DNA, sort of unpacking this conflated relationship between indigeneity and biology. But it's really interesting when we as geneticists start thinking about bucketing individuals in singular race categories. And we do this so often in genetics such that now we're constantly thinking and placing one Native American individual in association with one tribal affiliation. And this is just wrong. Populations are not stagnant. Genetic drift is a thing that we as geneticists need to realize and we do ourselves a disservice to our field when we default to these colonial definitions of indigeneity. Navajo people, Dine people are a very recent tribe. We acknowledge in our origin narratives that we landed in the southwestern region of Arizona relatively recently compared to our other neighbors who lived in the desert for far longer than we did, right? And we acquired our individuals through neighboring tribes and created clan systems that acknowledged this genetic heterogeneity. We had the Yemez Pueblo Clan, the Mexican Peoples Clan, the Yut Clan. Yet it only was until like 1940s when we had this very, this legal stamp that we had to create our legal structure that we started becoming this unified Dine people. And now we have these weird notions of genetic purity that doesn't acknowledge our genetic heterogeneity's past. And when we default to, and do not acknowledge these multiple tribal identities, we're also enabling the system of empowerment that enabled the disenfranchisement of our lands and our ways of life. And we fundamentally just do poor science when we reify an indigeneity as a biological construct. And when we focus on, for instance, Southwestern indigenous peoples because they are more pure and we ignore indigenous peoples in the Southeast because they are too admixt, we are contributing to those conflated factors, those conflated narratives that are being told about our genomes. And when we talk about populations as being ideal populations, we're objectifying peoples. And when we talk about founder populations as opposed to founder events, we're also whitewashing genocide as bottleneck events. And this is a point that I should not have to argue with journal editors to be able to use genocide to describe the real historical events that happen to us, that we have to use the word instead bottleneck events to sort of make our reviewers feel comfortable with themselves. So this gives me to a point about genomic narratives, the stories we tell about peoples' genomes because you probably heard that we Native Americans are predisposed genetically to alcoholism. And in fact, you do a search on PubMed and you come down to over 150 plus peer reviewed articles that looks at this claim using genetics to explore the rates of alcoholism in our communities. Yet, if you look at epidemiological data, you show that the problems of alcoholism is the same or even less in our communities. Another tale that we tell ourselves is that we as DINF people or Native people or indigenous peoples are predisposed to type two diabetes. And yet you have a population whose existence predated the Mexico-US border, this arbitrary line that bisected this nation. And you have two different instances of type two diabetes. One side, which had a very colonial diet imposed upon this, them, and the other side that retained their traditional lifestyles. And yet we tell ourselves that these people were predisposed to type two diabetes, ignoring these other factors related to colonial disparities contributing to health. And this focus on genetic sequencing diverts attention from other basic health requirements like food security and employment. I can tell you that genetic epidemiologists, we focus on genomic data and data from the electronic health records because honestly it's the easiest, it's relatively easy to sequence across the genome or pull up a person's record. But it's really hard to do the legwork of actually going into communities with a survey tool that we validated for its cultural competency and actually start like really looking at social and structural determinants related to health as opposed to just looking at already clean and curated data sets. And we do ourselves a disservice to the science and the narratives that we tell with people's DNA. And I really wanna point back to this question of like who is actually benefiting? In 2007, a New York Times reporter went back to the Christiana, which was one of those Amazonian tribes that contributed their data to genetic science. And the reporter asked these people, what was it that researchers promised you in exchange for your genomic material? And they said, we were promised medicines. We were promised therapies and therapeutics to cure the things that were affecting our peoples. He unfortunately had to tell them that there are people's access to the genomic information and materials being sold by Corial cell repositories at the rate of $75 to $85 a vial. Facilitating a lot of industry related to biomedicine but not really contributing back and really fulfilling the promises that were given to the indigenous peoples in the first place. So this really calls to question data, data for whom and data for whose benefit. And it's really interesting because I talk to a lot of scientists all the time and I ask them, what are you doing to directly ensure that your research is impacting those communities? And we have this phase one of collecting DNA but then supposedly somehow it's supposed to translate to community benefit but how you get to point A or phase one to phase three you kind of throw your hands up in the air and you hope that some way and somehow some fashion the paper that you publish will contribute to some sort of actual tangible benefit to communities but really what happens is we're kicking our social responsibility like a can down the road. We're hoping that somebody else picks it up and actually creates the benefit in place of the scientist's responsibility perhaps to actually ensure that those benefits are really fulfilling the promises that we made to them to begin with. And as I have shown you unfortunately sometimes the first entities to benefit from access to indigenous peoples DNA is not the indigenous peoples or even the researchers but oftentimes the companies for commercial gain. So we really have to think in terms of equity that we're also ensuring that the benefits from that research are distributed fairly. Now I'm gonna slightly troll the NHGRIs. They have a strong Twitter and LinkedIn game and I love their ability to turn a meme but a lot of their calls which are very funny really call to the increased need for inclusion and diversity in data sets but if we're not careful we might conflate or confuse or just flat out ignore that E and DEI efforts and we cannot do that because we already had studies in the past recently that called for increased inclusion of diverse peoples and large scale data sets and that was HGDP, 1000 Genomes. And if we're not careful and we ignore that E in equity we can unfortunately resemble the same outcomes of those previously ambasted projects and we want to change that cycle. And this brings me to another point that's often stated is that in order to facilitate data sharing and data harmonization data sets in this big data era that we find ourselves in we have done something interesting. We've removed the human element from primary data that requires informed consent into secondary data that only needs to be broadly consented once. We've literally removed the human component to it and that secondary data is now doesn't even need consenting it's considered non-human subjects research and it's legislated and regulated as such and as somebody that comes from Arizona State University it's part of my training, I have to absolutely state that I am literally an epicenter of an example as to why broad consenting models is just not the way forward. We have an example in which a university researcher promised they brokered a trust relationship with the Havasupai Nation which resides at the base of the Grand Canyon that they were gonna utilize their people samples to study type two diabetes. And then the community members unfortunately found out through a doctoral students dissertation defense that their people's samples are being used to study other things like schizophrenia, the word inbreeding had been used even though they have an exogamous form of marriage through their clansis and to ensure that they are not marrying their relations and also just the way that their peoples were depicted was enough to be incite ire. But really central to this was this question about informed consent. If the community consents to their use in one thing but finds out that their samples and data are being used to study something else then that's a violation of trust and this caused enough of a concern for indigenous tribal, this tribal nation community to sue the Arizona Board of Regents which governs a three state university and really resulted in a black mar on Arizona State University's reputation and also really added fuel to the fire in that indigenous peoples worldwide were already questioning whether or not they should even proceed or engage in genetics research. And after this landed internationally you can imagine that a lot of tribal nations decided not to engage. This is incredibly interesting because broad consenting at that time was the norm and after this point in time we kind of see a shift to study specific informed consent in which every single change or iteration of the study protocol or the aim or research questions were involved reconsenting participants but especially at this point in time where we didn't have this acknowledgement for the need for community engagement or even the funding to be able to do this logistically researchers found this process to be too cumbersome but on the other side community members felt like not just defaulting to broad consent granted too much research authority and agency upon the researchers to define what constitutes the scientific good or the global good and ignoring the actual good for the communities. So what ends up happening is in this big data era of harmonization as I said we did something very interesting decided to reclassify secondary data as non-human subjects data and then to facilitate the harmonization across data sets and we shifted also back to broad consenting. You can kind of see now where I'm going in terms of the cyclical nature of history repeating itself and if we don't want the same thing to happen again we have to do something to change. Now this is incredibly important because now we have these newer higher throughput sequencing projects to collectivize genomic information from peoples worldwide to include indigenous peoples to create different reference sequence projects and also this human pan genome project and yet again we're starting to have the same question broad consenting, open data. How do we prevent these harms from occurring in a way and how do we ensure that the research if it happens is responsive to indigenous peoples concerns? Now in theory we have a whole discourse of community engaged approaches and ethical frameworks to sort of move the field responsibly and I'm one of the co-authors of this 2018 paper but it's really interesting because when I entered the field of genetic epidemiology and tried to introduce the topic of community engagement my mentors were kind of like, huh, what, why? Why would you even do that? And it's incredibly interesting because community engaged research practices have been in the fields since the 1970s and rooted in environmental action and justice research and yet only recently have we talked about the importance of community engagement in biomedical research in the last few years and really the underlying principles is that at every single stage of the research process or even before the research questions are even discussed you involve the communities at every stage. So in practice, however, this is a little bit hard to implement. So recently we have the common rule which is grounded in the Belmont Principles and operationalized as a set of practical guidelines for conducting human participants research and recently in 2017 there were some changes and updates to this which is calls for the federal nation's recognition of tribal nations to self-govern but unfortunately that only relates to the federally recognized tribes. What happens to the native Hawaiians who don't have that same recognition? What happens to indigenous peoples from unrecognized or state recognized tribal nations? What happens to indigenous migrants who are outside of the US who come to the United States for a better life in their minds? What happens to urban peoples that live outside of their communities because they were economically displaced and can't find jobs locally because of the resource extraction that occurred in their lands? And also of those 574 federally recognized tribes only very few of them have their own IRBs and they rely on outside agencies to review their research for them and whether or not those are appropriate means are under question. But it's also really interesting because in order to be recognized as an IRB tribes need an FWA, a federal wide assurance policy and absent this it is really easy to just circumvent that tribal nations IRB or IRB research review board. On top of all that under the single IRB mandate which is meant to streamline research by calling one IRB the IRB record, what can happen is when it comes to tribal nations IRBs or regulatory structures, it's kind of like a don't know what happens because all a university needs to do is just create their own internal policy that states that they refuse to cede review to any external IRB to include tribal IRB. So this is a huge problem and it results in loopholes in respecting indigenous data sovereignty and that we just have a lot of policies that need to be developed from the tribal nation side in order to ensure that that respect is actually respected. What can happen though is we could talk about the importance of community engagement all we want. We could talk about the helping facilitate these tribal policies at the tribal nation level but because you have such widespread displacement of indigenous peoples because of forced assimilation termination policies by the US over several, over a century, you now have over 80% of indigenous peoples residing in urban and suburban areas outside of tribal lands. So you see a lot of recruitment of indigenous peoples in cities just completely bypassing that community engagement. And the other question too that we have to ask is what does this mean in terms of intellectual property? Because universities through the Beidou Act can claim ownership from inventions resulting to federally funded research and have established these large legal teams. Corporations as we know have large legal teams but indigenous peoples don't. They don't have that same legal capacity. And also it requires that their existence is even legally recognized by the colonial government to begin with. There's that question of ownership is something that is not really a construct within indigenous peoples worldviews and their own a communitarian ethics. And then that notion of novelty is really hard to prove. There's a notion of a concept of novelty that needs to be proven in order to claim intellectual property even for protections. And that's really hard if you're talking about stayed into indigenous knowledges that have existed in our communities for millennia. So unfortunately this type of legal system is really hard to ensure that these protections can even be in place, nonetheless not be usurped by other types of legal structures. So the question is what can we do? One, we can rethink informed consent. Already if we look at a standard informed consent form you see that the language revolving around risks and benefits really prioritize individual agency autonomy which is already culturally incongruent and inconsistent with indigenous communitarian ethics. Also we have to acknowledge that we have perpetuated this myth of de-identified data. We have told ourselves that if we just remove personal identifiable information that we remove the risk of being re-identified and especially for DNA that's just not the case. And we not only run the risk of re-identifying individual DNA but also everyone they're biologically related to which absolutely opens up the risk especially for members of small identifiable populations thinking along the lines of genomic racial profiling and this unfortunately has occurred. So really honestly the ethical standard for individual informed consent is just incongruent and inadequate for our communities especially as it relates to DNA. So we need to rethink this concept. We need to think about fully informing communities because anything less than that it fails on a responsibility for informed consent. It's failing on those Belmont principles and failing on our legal responsibility for our research subjects and participants. And while it's legal to halt considerations of group harms at the tribal jurisdictional borders we have to think beyond what's just legally the bare minimum and think about what's ethical. Because as I mentioned before it's really easy to just disrespect indigenous data sovereignty by bypassing and undermining their legal authority. And this can just be something like a small text in the informed consent form that just states that if you're a member of a small population that your data could be used to make general statements about the entirety of your respective group. So this another thing that we can do is recognize indigenous genomic data sovereignty which is the right of indigenous nations and peoples to exercise authority agency autonomy related to their data. And understand that this is an intrinsic right not a clonially defined right. And also we can reconsider data ethics. So it's really interesting a couple of summers ago that the NAH announced two large initiatives to think about artificial intelligence and machine learning approaches for health disparities and they're incredibly well funded. And their ethical course really centered the fair principles of data governance findability accessibility interoperability usability. My critique and criticism about fair is if you read them it really centers this notion of research ethics on researcher access to data. And in a way it becomes researchers defining the rules of their own engagement. But we really there was nothing in fair to talk about community responsibility and that responsibility back to society and ensuring that there's benefits back to the communities that provide and the people that provided that data. So I really advocate for care and think that both approaches are what we need to move forward all of our emerging data tools related to data governance. One thing you also do is work with indigenous communities and also organizations that are trying to advance research for indigenous peoples. And that's the native bio data consortium. We are a 501c3 non-profit that started in 2018 with a budget of zero dollars. Now we have thankfully a budget of multimillion dollars a year which is amazing. But the reason why we started was because we talked to the tribal leaders and asked them why don't you engage in federally funded research? And they stated we don't trust the databases because we don't have any oversight or agency and even knowing who has access to our peoples data. So we wanted to provide an alternative an alternative in which the data set databases are governed on how is on tribal lands and also improves the quality of the research because who better than the community members themselves to inform the questions related to research about the changing world around them than them because they have seen it all and they will make better research questions than a non-community member who doesn't have those same lived experiences. We also should need to ensure that data directly benefits indigenous peoples. We can no longer ask for access to indigenous data without calling for direct and proximal benefits to them. We also need to increase transparency of industry partners in private and public funded projects and also ensure that indigenous nations are equally empowered for IP claims. We can also develop digital tools to protect indigenous genomic data sovereignty. So we can think about labeling or digital markers that define attribution access and provenance which is really amazing and cool in terms of how we utilize our data and metadata. We can create dynamic consent portals as a means to broker against those broad consenting models and then we have emerging tools in blockchain and also federated learning systems to facilitate secure and community consented data sharing and also governance. So this is our data portal. We have invested $700,000 in counting to create this dynamic consent portal and data management system which is the first of its kind for tribal nations which is amazing and we plan to license it for free to all tribal nations worldwide so they can manage their own data. And this is important because you're not only facilitating reducing that logistic burden of recontenting participants but you're also allowing them to have trust in the system. They can actually see who's actually seeing their data and also have lay summaries of the research that is resulting from their data. I also want to caution against pinpointing indigenous data sovereignty as like the antithesis to open data, that's not what I'm saying. Unfortunately, that tends to be the case because I think we think too easily in binaries that anything anti-open data must be anti-open data and therefore sometimes anti-progress and therefore indigenous data sovereignty gets equated as anti-progress, it's not the case. We're just wanting to make the system more fair by having communities have more of a say in the process of the data that is about them and also what's stated about them using their data. So here's a paper about block chaining, another one about federated learning. I'm trying to get through this because I'm not aware of the time. But really, I think the more exciting part about this is really thinking about data as a resource. When we think about the water that's been usurped from literally the ground beneath our feet, when we think about the fact that many of our valuable ores have been taken away from us by early mining rights, what really we have is this emerging data economy and that's something that we can actually rebuild for communities to become more self-sufficient and more economically self-sustaining. When we look at, this is a paper on the left about from Science Magazine that showed using data that indigenous peoples across the contiguous US have lost 99% of their historical lands. Yet under 67% of those lands have access to broadband internet. Actually only 50% of that actually meets FCC guidelines for what constitutes basic sufficient internet. That picture down below is actually, she could honestly be my cousin, even before COVID hit, the digital divide was so severe that my cousins would have to go like an hour one way to a McDonald's parking lot, just to be able to access Wi-Fi to do her homework. So you can imagine when COVID landed and the lack of Wi-Fi infrastructure in the communities really impacted students' education. And you can imagine what this means also for trying to build those data infrastructures and how challenging that is. So if we're talking about moving forward, what we're really talking about is a revolution, building not only indigenous data sovereignty, but also using machine learning approaches for our purposes, expanding that broadband data capacity and also perhaps cloud solutions. So it would be really cool if we thought about data hubs that were cloud based in our indigenous languages, but also it talks about training indigenous peoples. So I'm gonna selfishly talk about two things, the summer internship for indigenous peoples in genomics, which is based in trying to talk about genomics and data ethics to tribal members and also indigenous data. Singh has expanded worldwide indigenous data. That's us with our students in our laboratory in central South Dakota. Talking about how we were the first indigenous data organization to even introduce alumina sequencers to a tribal community that's rural based, which is awesome. And then also selfishly, I know that University of Michigan has a relationship with some of the indigenous nations for the North. So if you're in community or in concert with an indigenous nation, we have indigenous training opportunities to sort of build opportunities for bioinformatics and also those data economies. If you happen to be in the area in ASU end of March, we have an indigenous AI symposium that I highly encourage you to attend. But lastly, I just wanted to leave with a grounding comment that if we wanna drive innovation and equity, we can't just think about equity in terms of advancing access to these technologies. We had to think about ensuring that there's benefit equity, decision-making equity and engagement equity, and equity is both a process and an outcome and involves uplifting indigenous data sovereignty and rising against power dynamics to ensure equitable opportunity and access for communities. I love this, because we're not just talking about power in terms of statistical power, but also power in terms of abolishing those power dynamics. And these are the same communities who have been overly researched and left behind in health. Yeah, thank you. All right, that was great. Thank you. And I think we'll also sort of be able to open up for questions pretty much anytime you do, you're doing the questions. Yeah, so whenever you wanna start with that, that's great. And yeah, thank you. No, thank you. Every time I give that talk, it always expands, but also I find with new audiences, I still have to give that sort of research ethics 101 in order to ground the importance. So it's always a challenge of how do you cram everything that everyone wants to hear about and learn about relating to indigenous communities, but try to keep it under time. So thank you so much. Yeah, you did great. And that's the benefit of having the Q and A, because anything you didn't get to, we can sort of raise in this context. And if we wanna hear it from the folks here, we can do that. I have some questions that people brought in sort of before you got here, which you've seen, and a few that came up as you were talking. But maybe starting with that notion of like, there's the sort of bioethics that we have to go back to the 101. But like the data ecosystem is really broad these days, there's the research places, but there's all those other companies and places that people want to use data and get access to it. How do you see the sort of issue of sort of, how do you trace the accountability all the way, all the, like everywhere? It's really hard. So because indigenous peoples largely have not engaged in genomics research, there aren't that many data sets. So those data sets are almost hoarded like gold. So we have the openly available ones that are publicly available on websites that I mentioned. But what's also of concern are the ones that we don't know about. So there's something called legacy samples. These are samples that were collected prior to when tribal nations created the IRBs and IRBs. And because they already were consented directly with the participant and predate these IRBs, they are almost hoarded and almost traded with other researchers. And they don't want to disclose that they have access to these data sets. So it's really interesting because some of those same researchers, particularly in the ancient DNA paleogenic space will publish, we need increased transparency of researchers and their data sets. But at the same time, they're not disclosing to communities what they have in their collections. And this is a problem too in museums and archives. This lack of disclosure of community affiliations because the concern is if we tell tribal communities that we have for instance, our ancestors in our collections, they're gonna ask for those to be repatriated or rematriated and then we won't have anything left to do their research. So that's why it's really important like I tried to mention that it's interesting the sovereignty is not necessarily like a movement against open data but there are ways that we can think about progressing their research so it's equitable for everyone. And then if it's not equitable then we shouldn't be doing it. Yeah, yeah. Talk a little bit more about the sort of trust, how you see trust, the sort of the trust issue, the sort of building breaking, the sort of dynamic nature of it and how there's a number of questions here. It's sort of like, what can I do as a young researcher? What can I do as an old researcher? What can I do as a anybody, just a person, right? Yeah. So one of the ways to build trust is increasing your public visibility in the space. And I think that's something that we all can do better at and not just thinking about, oh, I published this in a journal, therefore it's out there. It's really not. Because one, there are paywalls but then also there's that language barrier. I can read papers that are in chemistry. They're totally outside my domains and they'll just completely, it's not in a language that's decipherable to me because I'm not in it. You can imagine what that's like for somebody who's a member of the lay public or a tribal member or something. So I think really thinking in terms of how we educate and how we disseminate our findings is really important and one way to build trust. But then also just being honest, like what are the limitations of the research? I think not, we have a tendency, I think, when we enter into a new relationship of any kind to sort of present only the positive views of ourselves. But that can translate to over expectations that are hard to live up to. And I think that's something that science is suffering from is that sort of that public veneer and that image because anything that is anti that then becomes anti to progress or just anti science. Yeah, yeah, it's hard. It is. Yeah, so you mentioned a few alternatives to informed consent. Are there, some of it was, can you sort of go over those again? Yeah. Because it's important, because it is sort of like all this stuff sort of gets thrown into the consent form and as we're sort of going back and forth between broad consent and specific consent and the sort of some of the contracts change over time. Yeah. So are you even unpacking your own sort of approach to broad consent in your data bank? Yeah. Okay, so broad consenting, you tell a participant you not only agreed to this particular research study but also you allow us to use your data for future purposes and you're not really clear or explicit in terms of timelines or who has access to that data or for what purposes. This more, what I'm calling a specific informed consent is you are very explicit on you are consenting to this study at this point in time for this particular research question and then being very clear in the limitations but then if you get more funding in the future and you want to slightly investigate a slightly different research question or you want to use somebody's data for a slightly different purpose that would involve reconsenting a participant and then dynamic consent it really does is greatly facilitated by like an online portal or some sort of data management device. That one might be for instance, it's not quite broad consenting because it's not, you're not asking them to consent to any purpose or research aim but you might ask the participant, hey, like what are the types of studies that you are comfortable with and not comfortable with and then only their sample or data will be included on those types of studies and then you give also the option for the participant to sort of change their mind in the future or even like change more like about their contact details and things. So broad consenting is something that I had like a, I've had both challenges with broad consenting and also dynamic consent the study specific informed consent part. So when I was in my rural community in North Dakota it was a small tribal community and I got to learn my research participants kind of on a one-on-one basis but that required me living there and it was cold and away from my family and also just driving around everywhere and sometimes individuals moved to highly geographic mobile population and that requires funding and it's hard to find funding for that kind of call and that purpose but that's something that the field needs to change and have recognition for. Broad consenting, I have had instances in which a researcher might have talked to a participant in 2002 and stated, oh, I'm going to use your data to study one phenotype but then now we're 20 plus years later obviously the technology has changed it's a lot more invasive in terms of what we can glean from that person's sequence data and I've heard researchers say, oh, well this participant already consented so it's fine, like no, absolutely not and it's not up to the researcher to decide that on behalf or for the participant without their input. So I think dynamic consenting is something but definitely like to explore other forms of adaptive consent and just try to see how it works. It works with the UK biobanks so far from what I can tell and other forms of patient advocacy groups so it would be very great to see it in a tribal context. Yeah, other questions? Hi, so I'm Margarita, I'm an STVP student and I'm going to be reading the question so we have one now. So some of the issues that you raised are problems in public policy and institutional capacity. So beyond entities like the native bio data consortium what can and should policymakers do and what should we as concerned citizens advocate for? So what kinds of policy or programs change? Okay, two separate questions. In terms of what we can do at the policy I think we have to acknowledge that we just need to change our system that there are limitations that have been in place that we're continuing to perpetuate. So for instance the Belmont principles are great but they were also drafted with this concept of individual autonomy agency in mind which is culturally inconsistent with other communities besides indigenous peoples but also the level of risk has changed. In the 1970s we were talking about largely tissue cultures collected from primarily human subjects which also means a change of human participants honestly. The level of risk is just different and yet we're trying to continue to make changes to an older faulty system when in reality we should probably think about more structural changes and also just ensuring too that our legal frameworks are equitable and who we want to engage because right now the system of innovation is privileging those entities that can create intellectual property and disenfranchising those entities that can't and that doesn't just include just as peoples but also individuals as well. So I think having this knowledge is both on the policy side but also the scientist side. I showed a journal article about the human pan genome project and in it had a quote by a researcher. Now everyone in that article was some who's like a quote unquote scientist but Keela Fox and I were quoted but this other person was quoted, oh well we nerd, nerdy scientists don't think about these questions so we need social scientists in the room to guide these things. Yes, you definitely need to talk outside of your discipline but also acknowledge that scientists are also grappling with these questions and you need to also uplift them into these same conversations and not just invite to the table but also a voice at the table. In terms of what individuals can do this is also on the side of researchers and entities as increasing transparency and trust and just giving more of knowledge in terms of what exactly are you asking of individuals. So who reads like the EULA? Who reads the end user license agreement when they do agree to like Facebook? If you actually read through it you might be astounded as to what you've consented to but that's your social media, right? That's completely different than if you're going into a healthcare facility with like for instance a suspicion of colorectal cancer because it runs in your family and you ask your provider to give you a clinical test like ColoGuard and you unwittingly by use of that test allow your data to be owned by a company for drug purposes. Like you should have a knowledge that that's what's happening. Your provider should have a knowledge that they're facilitating that co-optation and maybe we should call on increased data sharing policies that are transparent to the public. Like these are things that we maybe talk about at an academic level but we don't really talk about in terms of practice for the people that are actually utilizing these types of technologies. We have a couple more questions. So there's one question that relates to like the person wants to know more about your personal experience while doing research and how could you be like a researcher and being involved in this like ethical side of like data management. So it's like how were you able to merge these two components like ethical issues and research in your experience. Yeah, that's a great question. I apologize in advance as this comes long winded because I was a grad student for a really long time. So this started first at a as, because I was a lab bench based for a really long time just a fundamental disenfranchisement with the field of cancer biology because I felt like as an indigenous person in STEM when there's so few of us that I should ensure that my research benefits my people and I wasn't sure I could do that if my research was so oriented toward drug development because my people like I grew up in a household with family members that would split antibiotics sometimes just because there was an economic disenfranchisement disempowerment and also just a lack of being able to see a doctor on demand. So like trying to sell them on the next approaches for drugs was kind of hard to do. And then going back to ASU for my first masters in bioethics was when I learned about the aftermath of the Hava supination. And that's when I learned about the importance of not only being indigenous scientists but one that's also ethically minded because as I stated from the quote from the article a number of scientists don't understand that it's important to stay abreast of the latest sequencing technologies but it's also important to stay abreast of the advances in ethical methods and approaches. Like it's a toolkit that I think all researchers need to also keep on top of just like anything else in terms of genotyping sequencing technologies. And yet, so we have this like real arbitrary separation of disciplines where scientists and social scientists don't speak to each other. And yet now geneticists are talking about the importance of social determinants of health in genetics. Like I'm sorry, just open a journal article from a social scientist, particularly a person of color you know, a black or brown scholar in the 70s, 80s or 90s and you'll see that they've advanced a lot of this course already. Same thing for community engagement. So in terms of like data, like I had first learned about the need for being an indigenous geneticist just by the fact that we hear a lot about the lack of representation of indigenous peoples and data sets. But when I started really learning about the statistical tools and the inferences that were being made and how they were being made, I really caught that. Like for instance, a lot of the reference groups that are being used for indigenous peoples are based off of the Central South America. People with like distinct genetic histories and cultural ways of life than US people that some of like how health disparities, how some researchers were doing health disparities research as I saw in NIH reporter was that they were just had their own pet phenotype in mind as an outcome and regression model. And then they would throw in covariates related to race and ethnicity and then try to see if they found a difference in the stratum of indigenous peoples compared to others. Or if we didn't constitute like high enough sample power to detect associations we were just left out. So like it was really started to motivate this need for data equity and data justice. And it's now started to translate again into now algorithmic fairness, bias machine learning approaches and also the substantiation and the use of these tools on data sets that are already flawed. All right. Yeah, we have more questions here. I have no doubt. So, you know, like given that this like blood quantum is like so problematic, how can these indigenous data sovereignty can be used to expand federal tribal membership? Yeah, I first wanna, that's a great question. First wanna unpack an uncoupled genetic data with those types of claims because they're just not the same. I mean, one tribe in on the East Coast explored potentially using DNA as like genetic ancestry tests as a poor tribal membership. And they were quickly convinced not to do that just because of concerns of how this biological construction could be used against them to even disenfranchise them and also how flawed it is considering that's not how we define ourselves as indigenous peoples. Interestingly, I had a federal marshal in the state of Oklahoma, and I'm certainly asked to make a ruling, kind of ask, you know, how accurate are genetic ancestry tests and can they determine tribal affiliation status? And the answer is they can't right now. It can maybe in the future, but we again have told or advised some of these genetic ancestry companies that if they do that, they're gonna open themselves to massive amounts of lawsuits. So right now they're keeping their term general to like Northern indigenous populations without going to the tribal affiliation status. But the reason why the federal marshal reached out to me was because I guess four out of the five civilized tribes in Southeastern Oklahoma don't recognize the federal death penalty. So he had, he told me that he was getting test kits results daily from death row inmates that were trying to claim the membership they thought like affiliation to one of these tribal members to try to skirt that and be extradited. But again, that requires the legal requirement for the tribe to even recognize genetic ancestry tests. So this is separate from lineal like paternity tests, by the way, it's like a totally, it's a different technology and different purpose of use, but genetic ancestry is like a really flawed. Now, in terms of other data and indigenous data sovereignty for increasing numbers, like that is something definitely to explore. So some many tribal nations, they have their own role, like census rules and they have their own histories, for instance. Right now in terms of indigenous data sovereignty, one positive use I can find that's not like potential disenfranchising of indigenous peoples actually in terms of looking at histories. So my lab has four individuals and my first full-time hire was not a postdoc or even a research person. It was a community engagement coordinator, which I thought was really cool. And he's a member of one of the nations in Arizona. And he's now a PhD student. He got accepted into the anthropology program at ASU, which I'm really excited about. But he wants to explore his people's oral narratives compared against state records of his people's timelines. And you can imagine that state records and constructions by state agencies of their people's existence in this area might disenfranchise indigenous peoples' rights to resources by stating that they existed in their traditional homelands for a far shorter period of time than they did. So this is where like other types of data, be it anthropological, archeological, artifacts, those kind of things like and perhaps recalling them from museums or gaining access to them in terms of operationalizing indigenous data sovereignty could actually help their cause. So long-witted, I'm sorry. Okay, so this is very related to the last one. And so considering all of these criticisms on these genetic categories. So first, if you want to elaborate more on your perspective on these critiques, but mainly this question is asking how should we think about this specifically? The category of indigenous DNA itself and that data. The data. Okay. So there's DNA from indigenous peoples and then there's Native American DNA. And those are two separate concepts. And again, it relates to the fact that indigenous DNA is not a biological construct. It's social and cultural and political. So what happens unfortunately, and this is what happens in a lot of genetic data sets is first you might do, when you do like a population substructure analysis, you are also including a lot of sequence data from openly accessible data sources from indigenous peoples. Now these are from indigenous peoples in the global south. Again, completely distinct histories from people in the US. Some of these reference groups have as few as five individuals. So can you imagine five individuals are meant to be representative of like a diverse peoples? That's already one inferential statement that's made. Then what you're doing is you are sort of looking at percent similarity of this one individual in relation to all these other potential ancestral groups. And whether or not you're including the proper components in that analysis is a limitation. But then you're also, you're starting to make then what ends up happening is whether or not the individuals included in a genetic analysis is then determined by some arbitrary percent similarity. So for African Americans, for instance, this happens where they might be assessed against a population of Sub-Saharan Africa and also like a stuff for a European group. And then you have the spread of individuals and only those are like 80% similarity to the Sub-Saharan African people get included as Black or African American. But the rest of them get excluded. So their entire lived experience gets excluded and their lived experiences is not also contributing as relating to health inequities. And yet those people experience those health inequities. They experience these health outcomes and you're starting to then prioritize a narrative of genomic purity, right? And this is what happens with indigenous peoples as well where only for instance, I've had examples in which you might have individuals who are indigenous from the South Eastern tribe who aren't even asked to be included in data sets because they are too heavily admixt. But then, you know, scientists will then try to recruit indigenous peoples from the Southwest because they're considered more genetically pure or least admixt. And, you know, there's an example in which there's been a long ongoing longitudinal study related to American Indians in the contiguous U.S. and includes populations and three different parts of the U.S. And the group in the Southwestern part of the U.S. decided not to remain in the data set because they were heavily researched. So they excluded themselves from the data set and for genetic researchers that were usually utilizing this data set, they got upset. It's like, you were moved to release admixt indigenous peoples that ruins my statistics, that ruins my paper. They, well, maybe you were perpetuating an incorrect notion of what it means to be indigenous, to begin with, like, how about that? And what ended up happening is that this project pestering this indigenous group to re-consent, to participate, such to the point that their tribal lawyer issued a cease and desist letter to the project. And, you know, so this tells you a lot of things. It tells you that we have these preordained notions of what it means to be indigenous. It means that we're perpetuating these narratives using DNA and that we're also perhaps doing a disservice to the lived experiences of other individuals that their, whose health inequity states and their health statuses may not be linked with genetics. Okay, so we have one more question. In your perspective, what do you think technologists and developers building data management tools should know about data sovereignty and data justice? Oh, indigenous people should have rights to their own data, end of story. And it's not anti-science, it's not anti-progress, it's pro-justice. Well, on that note, that was beautifully put. Thank you so much, Crystal. Thank you. Thank you.