I am deeply motivated toward my interests in human-AI collaboration by my heritage as a member of the Seminole Nation of Oklahoma, and I am invested in fostering greater Indigenous representation in the next generation of AI researchers and engineers.

Reach out if you’re interested in studying Computer Science and AI in college, finding industry internships, getting involved in academic research, or applying to graduate school. I also recommend checking out https://indigenousinai.org/, and group of Indigenous AI/ML researchers who recently hosted a workshop at NeurIPS, and aim to continue their work applying AI/ML technology to solve problems in Indigenous communities and build greater Indigenous representation in the AI/ML field.

Check out my CV if you want to see this work in context with my professional, research, and teaching experience.

Human-AI Collaboration and Indigenous People

Human-AI collaboration is driven by the kinds of relationships and patterns of coordination people desire and expect to have with AI/ML agents, and different cultural beliefs and philosophies can inform widely diverging approaches to collaboration with these agents. Individual variation within any group further increases this range of collaboration preferences and behaviors. Building AI/ML agents that can recognize and adapt to these group-based and individual preferences is critical to effectively, safely, and respectfully interacting with all users, particularly those that are underrepresented in AI research and underrepresented in the populations often used to train AI agents.

Indigenous people especially are vastly underrepresented in the AI research community, and their varied cultural and philosophical beliefs are similarly underrepresented, which motivates my ongoing work toward increasing the representation of Indigenous people and Indigenous issues in AI research and industry. Greater representation of Indigenous people and issues injects fresh ideas and new perspectives, which can produce more creative and novel research, while also encouraging more informed and effective AI/ML applications to benefit the diverse populations in which we aim to deploy collaborative agents.

Indigenous Representation

Toward these goals of Indigenous inclusion, I’ve co-led a talk at the AISES (American Indian Science and Engineering Society) 2020 Conference advocating for greater representation in CS and AI as a benefit to Indigenous communities and as a benefit to the field via a greater diversity of ideas and perspectives. This builds on a talk I co-led at AISES 2019 about how AI can benefit Indigenous communities. I have served as a research mentor for Stanford’s AI4ALL program, which recruits talented and diverse high schoolers from backgrounds underrepresented in AI and helps prepare them for a fruitful future career in AI. I am also contributing to the visibility and outreach of the Indigenous community within AI/ML by presenting my ongoing research work at the “Indigenous in ML” NeurIPS Workshop. The greater experience and seniority acquired during a PhD and my future career afterwards will enable me to further engage the research community about Indigenous representation and to mentor younger Native students as they build careers in AI. Through my current and future work, I hope to introduce more Indigenous students to the field and contribute to a more diverse next generation of CS and AI researchers that can strengthen the research community and better represent and advocate for Indigenous perspectives.

Finally, I acknowledge that my experience as a white-passing Indigenous person is fundamentally different from others who are consistently perceived and stereotyped as Indigenous. I also know that tribes such as the Seminoles who faced relocation from the east coast have very different histories than those in the plains and southwest that still reside in their original homelands or those from California that faced the Spanish Mission system. For these reasons, I aim to include and uplift a wide variety of Indigenous perspectives beyond my own, and I will listen to these varied experiences in order to guide my AI research toward greater inclusion and problem-solving for all Indigenous people.

Relevant Experience

• Conference on Neural Information Processing Systems | Indigenous in ML Workshop
  • Title: Improving Human-AI Collaboration by Quickly Adapting to Diverse Human Collaboration Preferences
  • Experience: Presented ongoing work on human-AI collaboration with diverse user populations.
  • Takeaways: Connecting ML research experience to benefits for underrepresented communities and perspectives. Engaging with Indigenous leaders in AI/ML about uplifting Indigenous perspectives on nature of AI.
  • Date: December 2020
• American Indian Science and Engineering Society National Conference
  • Experience: Created and co-led a talk on Indigenous people and representation in AI research and industry. I connected the dual impacts of equal representation: communities benefit from career opportunities and more informed AI applications, and the field benefits from more creative research due to greater cultural-ideological diversity.
  • Takeaways: Engaging with attendees for future collaborations to improve Indigenous representation in CS and AI.
  • Date: October 2020
• American Indian Science and Engineering Society National Conference
  • Experience: Co-led a talk on Indigenous people and representation in AI research and industry.
  • Takeaways: Engaged with attendees on community needs and AI solutions.
  • Date: October 2019