Oyate Data

Native youth represent an enormous pool of intelligence and talent, but it can be difficult to imagine a career in high-tech fields when they seem so far removed from our communities. Our solution introduces data science and programming skills to Native youth through experiential learning projects that add value to a Native organization and allow the students to think past the technical aspects of problem solving and embrace the skillsets that drive value-creation in the real world. Scaled globally, our solution will ensure that all indigenous students are exposed to data science, and, as such, will be enabled to pursue careers in related fields, engage in data-driven problem solving, and act as informed voices in the constantly evolving discourse around data sovereignty.

A former Native youth, our founder didn't see a single line of code until I got to college. For many, this wouldn't have mattered, but for me, the language of software is an extension of my internal thought processes; it's how I think! A great deal of luck was required to make the introduction between me and data science, and without that same privilege, I know that most of the Native youth of today will be excluded from these spaces and careers.

After extensive research, career resource website Zippia concluded that there are 0.0% American Indian or Alaska Native data scientists. While this figure is perhaps an underestimate (I'm an American Indian data scientist), it illustrates an important point. Native representation is almost non-existent in a field that is driving much of the innovation we see today. The longer these conversations continue without us, the more likely we are to be exploited by those who wield the power within the systems. There are examples all around us of AI solidifying racist practices (e.g. the justice system), but perhaps most horrifying, we are starting to see the siphoning of our cultural (language) data to the detriment of our communities.

Inspired by Mens et Manus, our students learn by doing. By connecting a cohort of students with a Native data scientist and a Native organization, we are able to provide experiential learning projects that go beyond the classroom. Altogether, the process can be laid out as follows:

1. The students are presented a problem by the Native organization and decide on an objective for analysis or product development.

2. The data scientist provides the background information necessary to complete these tasks.

3. The students are guided by the data scientist as they put their newly acquired knowledge to work. During this stage, the students, and data scientist, are in communication with the Native organization to fine-tune their output.

4. The finished analysis, or product, is presented back to the organization.

Altogether, the project brings previously daunting ideas to life, encourages community improvement, and introduces students to the exciting world of data science and technical projects.

Our solution currently focuses on the Native youth of South Dakota. Many, on the Rosebud Reservation for example, live in small communities without exposure to the ideas presented through our solution. We hope to show that these ideas are accessible and powerful. In general, we hope that the projects our students complete will provide value directly to the community while potentially inspiring those students to explore the idea of further study or sourcing new projects in their community with the new skills they've acquired.

Thinking big, creating a consortium of technical talent that can complete projects outside of the local community is an exciting prospect. Our focus will be on our students, but enabling these long-term arrangements of economic opportunity is something we're also thinking about.

We believe our solution is aligned with the fellowship for a few reasons:

• We're providing skill development in a field that is often overlooked in Indian Country and will open economic doors that have previously been shut.
• We're working to uplift our communities and peoples in order to increase their economic prospects.
• Data science and AI are frequently used tools for language preservation. In fact, our prototype cohort built a data collection website for that very purpose. We imagine many of our projects with deal with this issue.

We've explored the concept and have completed a prototype of the process, so we believe that we're in the pilot phase. Our prototype was a cohort of four Native students from MIT who took our course over their January-term and built a data-collection website for a current ICF Fellow, the Wakashan AI Consortium. We learned a great deal throughout this process, and we feel we are ready to roll this out in a community.

We are putting together a pilot in South Dakota that will hopefully launch this fall. We've approached a few community members about our ideas and are in talks about moving this project forward. This pilot will challenge us to reach a different population of students as compared to our MIT cohort, but we're excited to see what we can do.

Experiential learning is a powerful pedagogical approach as explored here and here. Currently, a large portion of tangible, technical education is delivered through robotics. While interesting, there is much more to science than robots. We prefer to explore the space of ideas, and we think our approach will reach those who feel similarly.

Through partnerships with local organizations, we create an environment of commitment and communication for both the students and the organization. The students will learn that their ideas can be made real, through code, and can drive positive impact in their communities. Importantly, upon completing their engagement, the students are left with a unique project that they can leverage for future internships or school and job applications. This is an important difference from similar programs, such as coding boot-camps, where most of the projects are quite similar to one another and the students aren't incentivized to solve real problems for an outside stakeholder.

Not technology exactly, but our first cohort of students were each able to create a data-collection website for the Wakashan AI Consortium. An example can be found here.

The largest challenge for us in the next year will be funding. Without that, our next cohort will have to provide their own laptops and internet. Perhaps not the end of the world for the next cohort (or two), but it would be fantastic if we were able to help students with those requirements.

In the long-term, sourcing projects could pose a challenge. We believe we can demonstrate the value of bringing data analysis into Indian country, but we’ll have to go above what is already being done and justify it’s use in situations that might not obviously come across as data projects.

To overcome our financing barrier, we’re exploring community partnerships to provide for our students. We have access to cheap laptops from a community partner, and that seems to be the largest expense that we could incur in the short-term which further reduces this barrier.

On project sourcing, we are currently expanding our network both locally and online as we seek out Native organizations that are considering data science. We hope that we can create relationships with a few organizations up-front, and that more will follow thanks to the outstanding work of our students. Additionally, we learned that jumping right into machine learning and artificial intelligence can be quite daunting for the students, and while they might learn about those ideas, value can be provided in many different ways. This perspective on our projects allows us to work with partners that could perhaps benefit most from a simple chart or analysis and reduces this barrier of sourcing by increasing those that we are able to work with.

Our founder, Mason Grimshaw, grew up on the Rosebud Reservation. Inspired by the resilience of his community, he hopes to give back in a big way.

Currently working as a data scientist, Mason didn’t see a line of code until he was in college, and when he did, it just clicked. Thinking in software and systems came naturally to Mason as it aligned well with his internal way of thinking. When these ideas connected to data science, they opened an enormous world of possibilities. He frequently lost card games to his Unci (grandma), and was constantly trying to think of ways to improve his strategies. Data science allows him to take that same skill-set and provide value at a much larger scale and in a variety of domains. He knows that this particular career and industry isn’t suited for everyone, but he frequently reflects on the fact that, without a great deal of luck, he might have never been introduced to something he is now dearly passionate about.

At Oyate Data, we understand that our people are fantastic storytellers, and at the end of the day, that’s what data science is. We want to provide exposure and excitement around this new-age twist on an ancient tradition, and we believe our team has the ability to do this.

We’re applying for this fellowship because we know it can help us reach more youth more effectively. As airtight a plan we might think we have, the mentorship and guidance from former fellows and MIT leaders will be crucial in solidifying our approach with best practices from projects all over the country and world.

We have a grip on the technical side of things, but could benefit from guidance with respect to pedagogical approaches (especially those that consider students from under-privileged backgrounds), and project sourcing. Additionally, we’re nerds, so having a public-facing presence is not our strong suit. We would hope that we could work with someone on the best way to showcase our students and their projects as a means to both celebrate the amazing work that they do and encourage other students to join us for future projects.