Knowledge Production and the University in the Age of AI
Knowledge Production and the University in the Age of AI
How should we at Harvard make knowledge in the future?
About the Seminar
At this time of seismic change for the university, and the research systems of which we are a central part, it is hard to find a moment to pause and ask: how should we make knowledge in the future?
To that end, the Harvard Department of the History of Science and the Harvard Data Science Initiative are convening a year-long seminar to explore the many ways in which AI is reshaping the production of knowledge in general and our shared educational project at Harvard in particular. Recognizing the existing robust coverage of AI in disciplinary and public contexts, we seek to complement those efforts by fostering a broader conversation about the implications and possibilities of AI for the university itself: how it may transform research priorities, professional identities, economic incentives, and the meaning of work. The year-long seminar will therefore prioritize rigorous in-depth, cross-disciplinary engagement from a novel set of perspectives. This is a university-wide initiative, crossing sciences, engineering, social science, governance, law, and humanities. It is an opportunity to initiate a sustained conversation on what current and anticipated technological changes will mean for all of us.
Above all, we hope that this seminar will be a place to generate and refine proactive visions for knowledge production grounded in collective expertise.
Seminar Meetings
The year-long seminar will meet on select Tuesdays from Noon to 1:30pm in Science Center 469. Each meeting opens with 20-minute remarks from a guest as provocation and inspiration for a conversation among the faculty and researchers on a specific aspect of AI in the university. The seminar, in a departure from sweeping discussions about the moral valence of AI or its precise application according to domain-specific rulebooks, seeks to foster concrete, cross-disciplinary dialogue about our current state of knowledge production.
A light lunch will be served. Please register to attend a meeting.
AI Seminar: Zoë Hitzig
AI Seminar: Moira Weigel
AI Seminar: Finale Doshi-Velez
AI Seminar: Elizabeth Lunbeck
AI Seminar: Jeff Lichtman
Gary King: When AI Makes Each of Us Better & All of Us Worse (and what to do about it)
Roy Perlis: AI, Medical Publishing, and Trust
Francesca Dominici: AI's Climate Catch-22
Karim Lakhani: When AI Is Smart, When It Is Wrong, and When It Replaces the Team
Christopher Stubbs: Generative AI and the Future of Science
Todd Essig: Will Love for Learning Matter Anymore?
Jonathan Zittrain: Sorting through AI's Big Picture Possibilities
Seminar Conveners
As historians of science and technology, we are reluctant to accept overblown claims of immediate or inevitable technological revolution at face value. However, Generative AI technologies are creating remarkable opportunities for innovation and social restructuring within the University.
The goal of the seminar is to build a critical mass of participants to sustain a meaningful conversation over the course of the year. The stakes for academic freedom—proactively choosing research questions and collectively directing our research enterprise—could not be higher.
By the conclusion of the seminar, we intend to synthesize insights into a collaboratively crafted white paper. This document will articulate distinct opportunities and offer recommendations for how Harvard might thoughtfully shape the evolving place of an evolving technology in our own work.
Academic freedom requires choosing: Where do we want to go, what do we want to do, and who do we want to be? We are eager to explore, to play, and to discover with AI in its many forms. Even when production processes and technical mechanisms are opaque and inaccessible, we, as users, consumers, partners, and researchers get to decide.
Peter L. Galison
Elizabeth Lunbeck
Marc Aidinoff
Seminar Sponsors
This seminar is co-sponsored by the Harvard Data Science Initiative and the Department of the History of Science.
