I currently do not (nor plan to) use artificial intelligence tools (AI) in any of my research (even for coding) or in my personal life. 

I value the process of doing research -- and the frustration that can come with it. I love learning, and there are few greater feelings than figuring something out that I was previously struggling with. That breakthrough feeling is essential to learning and to doing research...those "aha!" moments are the most fulfilling ones when it comes to my work. That's how new methods, new data sets, and new results come into existence. Through the frustration of innovation!

I have tested AI for some tasks in the past, such as automatic transcription -- and found that they don't do the best job. I find myself having to go back and fix its many, many mistakes. And, to be candid...I type 100 words per minute! I always end up regretting not just doing the task myself.

I understand the appeal of using LLMs in a society where productivity is emphasized. But humans are not machines, we should resist being enticed into cognitively offloading our skills to a black box which we have little control or understanding of. 

When the academy is already struggling to connect with those outside of higher education when it comes to participating and engagement with science, I can't help but ask two (rhetorical) questions: 

1) How does offloading the time and effort of human labor help us in making our work more accessible and understandable for the public audience while maintaining the nuances of assumptions and considerations that go into our research?

2) What will you do when the power goes out? When your AI LLM of choice goes down?

My work lives in between two worlds, just like I do: between the computational processes and large amount of  coding I do to answer questions, and the relational necessity of contextualizing my findings in the real world. 

I see the computational methods as a tool to understand large amounts of data across a given area, like a city, state, or country -- and ultimately turn those findings into appealing and approachable visualizations that anyone can take something away from. I see my role in this process as being a translator and interpreter for these computational findings to non-academics, and try to facilitate conversations with community members about their reflections, follow-up questions, and critiques that I take away to improve my work for the next time. 

Sometimes, it's easy as a researcher to get bogged down by the work where my introverted self struggles to "touch grass". I spend countless hours behind a computer screen: reading, tinkering with code, analyzing, designing figures and maps, writing, reading more, editing...that said, showing up in community spaces is still important to me, and I try to be as transparent as possible about the considerations for our culture in my work as well as centering Native people's experiences and opinions when designing research projects.

When I'm in community, I try to listen rather than "yap" unless asked to. Though I am Native,often times I'm coming to community spaces that aren't Oneida, and aren't inherently for me. I want to respect that I'm a guest and am always thankful when I am welcomed into spaces I otherwise wouldn't get to learn about.

Community members who want to be more involved in my projects always deserve, and will receive recognition in my non-academic write-ups and presentations regardless. If community members are interested in learning about and navigating the journal submission process together, I am always happy to bring folks along for the ride if they are willing to contribute to the writing itself, or by reviewing the findings of the work. We are stronger together when everyone involved is recognized for how they contribute to research!