Most of my research falls under three main clusters. Here is a brief description of some of the work I’ve done under each.

AI Aesthetics

My work on AI art has been published in venues such as Journal of Aesthetics and Art Criticism and Ethics and Information Technology. My scholarship focuses on questions of authorship, evaluation, and appreciation in the case of AI art. I developed the framework of curation to explain how literary texts acquire meaning in the abscence of an author. AI-generated texts are discovered by curators, rather than created by authors.

Elsewhere, I contrast the aesthetic virtue of “open-mindedness” against the aesthetic vices of “snobbery” and “naïvety” to show how we might come to evaluate and appreciate works of AI art. As an artist, I am interested in exploring AI as a unique artistic medium.

Poetry & Performance Art

I often deploy poetry as a method of scholarly analysis. My poetry and related work has been publshed in venues such as Augur Magazine, WordCity Literary Journal, and PhiloSOPHIA. I have argued for the use of trans poetics as a way of doing trans philosophy that provides unique insights. I understand my poetic work as an extention of my more traditional scholarship.

In my book chapter “Starfish Collaborations,” I discuss my artistic practice alongside my art partner, Steph Elms. Together, we are working on the ongoing multimedia arts project GLITTERPUNK, which has been performed at several conferences.

Feminist Science Studies

My work draws from standpoint theory and feminist empiricism. My work in feminist science studies has been published in venues such as Big Data and Society and Philosophy and Technology. Notably, I have argued that research teams comprised of people with socially diverse identities produce stronger scientific and technological results than homogenous teams.

I have extended my work on social diversity to discuss how the use of AI in scientific research threatens scientific objectivity and to explore how LLMs reproduce racist biases. I am interested in whether AI models trained on diverse datasets might mitigate algorithmic bias. My project “Data Queery” and its consideration of 2SLGBTQIA+ identities can be seen as a first step addressing larger questions of diversity and algorithmic bias in general.