FAQ
FAQ
Frequently asked questions about Katherine Elkins' research, collaborations, AI safety methods, publications, and human-centered AI work.
Who is Katherine Elkins?
Katherine Elkins is a scholar of AI, literature, and philosophy and a Professor at Kenyon College. She co-founded the Human-Centered AI Lab, co-developed early human-centered AI curriculum work in 2016, co-leads the team representing the Modern Language Association at the U.S. AI Safety Institute Consortium, and is Co-Principal Investigator of the Schmidt Sciences HAVI project Archival Intelligence.
What defines the research program?
The work examines how AI reshapes interpretation, judgment, creativity, pedagogy, and institutional decision-making. It brings together AI safety, computational humanities, cognitive science, philosophy of information, philosophy of mind and literature, governance, and higher education, while asking how humanistic inquiry can inform the design, evaluation, and public understanding of AI systems.
What is the confidence-scoring method for auditing language models?
Introduced in Informed AI Regulation, the confidence score measures how firmly a language model commits to a moral judgment versus hesitates. It provides a way to compare normative certainty across model outputs and supports broader audit work on framing fragility, negation sensitivity, and evaluation design.
Why does the site emphasize interdisciplinary reach and translational uptake?
Because the work is organized around how ideas move between domains. The research travels across liberal arts colleges and R1 universities, across industry, government, nonprofit, and public-facing institutions, and across technical and humanistic audiences. That pattern helps clarify how a humane study of AI can take shape across multiple settings rather than inside a single discipline.
What is the human-centered AI curriculum?
At Kenyon College, Elkins and collaborators built a curriculum that treated undergraduates as serious participants in AI's ethical, interpretive, and social questions before "AI in the classroom" became a standard institutional vocabulary. That work continues to inform research, teaching, and public engagement on what AI changes in higher education.
What is the Archival Intelligence project?
Archival Intelligence is a Schmidt Sciences HAVI project developing AI-powered tools to rescue and restore endangered cultural archives, beginning with community collections in New Orleans. It joins cultural heritage work to questions of preservation, access, and responsible technical design.
Where can I find the full research and publication record?
The site organizes the work by research domain on the Research page, with additional context on the Media page. The broader publication record is available through Google Scholar, ORCID, and linked external profiles.