Affiliated Departments & Programs
Katherine Elkins works in both applied and theoretical humanities and social sciences with a focus on artificial intelligence, emotion, cognition, linguistics, ethics, and storytelling. She has published in both traditional humanities journals like Poetics Today, Narrative, PMLA, MLN, Philosophy and Literature and MLQ as well as in computing venues like Proceedings of ICML, Frontiers of Computer Science, the Journal of Cultural Analytics, the International Journal of Digital Humanities and the International Journal of Humanities and Arts Computing. She is the editor of Oxford University Press’s "Philosophical Approaches to Proust’s In Search of Lost Time" and author of "The Shapes of Stories: Sentiment Analysis for Narrative" (Cambridge UP), in which she developed the first methodology to surface emotional arc for narrative.
In 2016 she co-developed the first human-centered AI curriculum and co-founded Kenyon’s AI Lab. Recipient of Kenyon’s Senior Trustee Teaching Award, she was awarded an NEH Teaching Professorship for curriculum innovation and became one of the first in the world to advocate for leveraging AI in the service of humanities and social science research. Since then she has given over a dozen keynotes and appeared in various media outlets, where she talks about both the risks and opportunities of generative AI.
She is a member of Meta’s Open Innovation AI Research Community, the multi-national group Public AI, Women in AI, and AI in Education. She has also served as co-PI representing the Modern Language Association for the U.S. AI Safety Institute and for the IBM/Notre Dame Tech Ethics Lab. Her passion is supporting voices in AI and computing that are typically underrepresented, and she has mentored over 300 AI/ML student research projects that have been downloaded tens of thousands of times from digital Kenyon by over a thousand institutions in over one hundred countries.
Areas of Expertise
Human-centered AI, Multimodal and Multilingual Generative AI, Affective AI, Narrative, Translation, Explainable AI, Bias and Fairness, AI Regulation, AI Ethical Auditing and AI Safety
Education
2002 — Doctor of Philosophy from Univ. of California Berkeley
1990 — Bachelor of Arts from Yale University
Courses Recently Taught
This course equips students with computational methods spanning the humanities, social sciences, and data science. Through Python programming, data visualization, and modeling, students analyze everything from literary texts to social networks. The course examines how digital tools transform our understanding of human behavior and society while tackling crucial questions about AI, surveillance, automation, and transhumanism. By combining quantitative methods with critical analysis, the course prepares students to both understand and shape our increasingly algorithmic world. This course serves as the gateway course in the IPHS AI curriculum. We recommend that students without prior data science or programming experience take this course before enrolling in more advanced AI courses. \n\n
Cultural analytics is the study of culture using diverse sources and data-driven methods. We analyze language from texts to tweets and social networks from film to the Twitterverse. In this project-based course, students code ways to explore phenomena like the social networks in "Game of Thrones" and the classification of tweets as Trump or Trudeau. They apply what they have learned for a final project of their choice. Students new to coding should contact the instructor for information on how to complete a self-paced mini coding course before the start of the semester. This course does not count toward the completion of any diversification requirement. No prerequisite. Offered every other year.
This course explores artificial intelligence through both technical implementation and humanistic inquiry. Building on the programming foundations from IPHS 200, students learn to build and critically evaluate AI systems, from classical machine learning approaches to cutting-edge deep neural networks and large language models. Through hands-on projects, students create AI systems that generate music, analyze text, classify images, and more. The course pairs technical training with readings from philosophy, ethics, and critical theory to examine fundamental questions about creativity, intelligence, and what it means to be human in an age of artificial minds. The course emphasizes both technical competency and critical thinking, preparing students to be thoughtful practitioners and critics in our AI-driven future. Prerequisite: prior programming experience (such as IPHS 200); students will be implementing machine learning models and working with industry-standard AI tools.
This course, designed as a research and/or studio workshop, allows students to pursue their own interdisciplinary projects. Students are encouraged to take thoughtful, creative risks in developing their ideas and themes. Those engaged in major long-term projects may continue with them during the second semester. This course does not count toward the completion of any diversification requirement. No prerequisite. Junior standing.
Individual study in the Integrated Program in Humane Studies is reserved for juniors and seniors who have completed at least one course in the program. Individual study projects are designed to offer the opportunity for directed reading and research in areas not generally covered by the regular offerings of the program, or by the regular offerings of other programs or departments. Alternatively, such projects may offer the opportunity for more advanced research in areas already addressed in program offerings. In some instances, they may offer the possibility of studying languages not otherwise available, or not available at an advanced level, in the College curriculum (e.g., Old Icelandic, Old English). Students undertaking an individual study project are expected to meet with their advisors on a regular basis, ordinarily at least once a week. Individual study projects are expected to embody a substantial commitment of time and effort, which, at the discretion of the project advisor, may result in a major essay or research report. Students wishing to undertake such a project should first gain, if possible a semester in advance, the permission of a potential advisor or mentor and then submit a written prospectus of the project for the approval of both the prospective advisor and the program director. Because students must enroll for individual studies by the end of the seventh class day of each semester, they should begin discussion of the proposed individual study by the semester before, so that there is time to devise the proposal and seek departmental approval. This course does not count toward the completion of any diversification requirement.