Katherine Elkins

Affiliated Departments & Programs

Katherine Elkins’ current research investigates AI strengths and failures, and she serves as Principal Investigator for the NIST U.S. AI Safety Institute Consortium representing the 25,000-member Modern Language Association. She is also co-PI for a Schmidt Sciences Humanities and AI Virtual Institute grant — one of 23 teams selected worldwide — for "Archival Intelligence: Rescuing New Orleans' Endangered Heritage." Her work on open-source AI risk was selected for oral presentation at ICML, and she publishes in both AI venues (ICML, Frontiers in Computer Science) and leading humanities journals (PMLA, Poetics Today, Narrative, Philosophy and Literature, MLN, MLQ, Discourse, Journal of Cultural Analytics, Humanities, and International Journal of Digital Humanities). Author of The Shapes of Stories: Sentiment Analysis for Narrative (Cambridge University Press, 2022), she developed the first methodology for surfacing emotional arc in narrative. Her recent essays in PMLA and Poetics Today examine how large language models reshape authorship and the university.

Elkins began her career as a scholar of philosophy and literature, and her essays on Plato, Woolf, Kafka, Proust, Wordsworth, and Baudelaire investigate how literary form enacts a kind of knowing that philosophy, on its own terms, cannot attain. She is contributing editor of Philosophical Approaches to Proust's In Search of Lost Time (Oxford University Press, 2022) and won the A. Owen Aldridge Prize in comparative literature for her work on Baudelaire.

In 2016, alongside Jon Chun, she co-founded the world’s first human-centered AI curriculum and the AI CoLab. Their innovative approach to AI has been featured in both academic venues like the Journal of Humanities and Arts Computing and in industry. She serves as the AI industry expert for Bloomberg, is a featured speaker at OpenAI’s Higher Ed Forum, and has been interviewed by Forbes. Her recorded lectures on The Modern Novel and The Giants of French Literature reach worldwide audiences through Audible, and she received the Kenyon’s Trustee Teaching Award in 2014.

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

— Bachelor of Arts from Yale University

— Doctor of Philosophy from Univ. of California Berkeley

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

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: COMP 118, IPHS 200 or IPHS 391 (fall 2025).

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.