Katherine Elkins studies how artificial intelligence reshapes human meaning: how models interpret language, generate stories, encode values, simulate creativity, alter institutions, and change the conditions under which people judge, teach, remember, and know.
Humanistic methods for evaluating frontier language models in ethically and linguistically complex settings — ethics-based audits, FATE evaluation through the Notre Dame–IBM Technology Ethics Lab, syntactic framing fragility, and CAISI/NIST standards work.
The institutional conditions under which AI can serve public ends — comparative regulation, open-source and open-weight model risk, public AI infrastructure, and cultural data governance, including work connected to UNESCO’s AI, IP & Culture Repository co-design process.
Methods for tracing emotional structure across narrative, from The Shapes of Stories through later work on narrative intelligence — emotional arcs that reveal structures plot and character analysis miss.
What language models can generate, imitate, translate, and co-create — the writer’s Turing test, AI authorship, the AI Fiction Paradox, literary translation, and early live human-AI improvisation.
How knowing happens — consciousness, memory, perception, authorship, and interpretation across Proust, Wordsworth, Plato, Baudelaire, Kafka, Woolf, Maryse Condé, and contemporary AI.
Human-centered AI as an educational and institutional project — curriculum, the AI CoLab, student research, faculty governance, and what large language models do to the university.
Work done first. The dates establish priority; the Scholarly Reception page documents the uptake that followed.
2016
Human-centered AI curriculum and AI CoLab at Kenyon. Definition →
2019
First methodology for sentiment analysis of narrative. Reception →
2020
First writer’s Turing test of a large language model. Reception →
2023
Founding digital-humanities statement for human-centered AI. Reception →
2024
First ethics-based audit of moral reasoning in deployed LLMs. Reception →
2024
First systematic EU-China-US regulatory comparison after the EU AI Act. Reception →
The reception record shows how these strands have moved across AI safety, governance, computational humanities, cognitive science, and philosophy of information. For detailed citation evidence, named citers, and adoption across fields — including the Klowden/Tao citation of The Crisis of AI — see Scholarly Reception. Several strands of this research are co-authored with Jon Chun, including work on GPT-3 and creative writing, AI and narrative studies, explainable AI, open-source generative AI risk, comparative AI regulation, semantic ethical auditing, and human-centered AI curriculum.
Across these areas, the work connects humanistic methods to AI safety, governance, computational humanities, cognitive science, and philosophy of information. It began in literature and philosophy, with research on memory, consciousness, authorship, perception, lyric authority, and interpretation, then moved into computational humanities through sentiment analysis, narrative emotion modeling, and the study of story shape. As large language models became public infrastructure, the same questions became AI safety, governance, creativity, and higher-education questions.
See also Jon Chun’s research page for related co-authored and parallel work.