Katherine Elkins is a professor of comparative literature and humanities and director of the Integrated Program in Humane Studies. In 2016, six years before ChatGPT brought generative AI to public attention, she and Jon Chun founded the world's first human-centered AI curriculum and its research engine, the AI CoLab. Their 2023 article offered the first peer-reviewed account of what a human-centered AI curriculum looks like, and the program's more than 400 mentored student research projects have been downloaded over 107,000 times in 198 countries.

Elkins' own research asks where AI succeeds and where it fails. She leads the Modern Language Association team at the U.S. AI Safety Institute Consortium (NIST CAISI), serves as Principal Investigator of the Schmidt Sciences Archival Intelligence project, and is the author of "The Shapes of Stories" (Cambridge University Press, 2022). Her work appears in venues from ICML to PMLA and has been featured by Forbes, NPR and the Christian Science Monitor.

Download Elkins' academic CV.

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.

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