Call for Lightning Talks at DH2024: Teaching Machine Learning in the Digital Humanities

The “Teaching Machine Learning in the Digital Humanities” workshop at DH2024 invites proposals for lightning talks on the pedagogy of machine learning (ML) in the humanities.

We seek proposals from instructors, researchers, librarians, and students with experience teaching ML to humanities audiences. We welcome presentations on a wide range of topics related to teaching ML in the humanities, including:

• Effective teaching strategies: How can we best teach ML to humanists with diverse backgrounds and interests? What are the most effective ways to introduce the technical concepts of ML without overwhelming students?
• Pedagogical resources: What resources are available for teaching ML to humanists? What are the strengths and weaknesses of different resources? How can we develop new resources that meet the needs of humanities scholars?
• Assessment: How can we assess student learning in ML courses? What are the challenges of assessing students’ understanding of technical concepts and their ability to apply ML methods to their research?
• Ethics and responsible AI: How can we teach ML in a way that emphasizes the ethical implications of this technology? How can we help students to develop a critical understanding of the biases and limitations of ML?

Please apply using this form by June 10, 2024.

Presenters will be notified of the status of their proposals by July 1, 2024.

The workshop will be held on Tuesday, August 6, 2024, from 1:30 PM to 4:30 PM. The workshop will consist of up to eight lightning talks, each followed by a brief Q&A session. After the lightning talks, there will be a breakout session where participants can discuss specific topics in more depth.

Workshop Organizers
Melanie Walsh, University of Washington
Quinn Dombrowski, Stanford University
Zoe LeBlanc, University of Illinois, Urbana-Champaign
Andrew Janco, University of Pennsylvania
Toma Tasovac, DARIAH-EU
Natalia Ermolaev, Princeton University
Nick Budak, Stanford University