Date:
Location:
Please note change of location.
"Interpretability, or Learning to Listen to Algorithms"
Speaker:
Nick Seaver, Assistant Professor, Department of Anthropology and the Program in Science, Technology, and Society, Tufts University.
Co-sponsored by the Graduate School of Arts and Sciences and the School of Engineering and Applied Sciences, Harvard University.
Contact:
Shana Ashar
shana_ashar@hks.harvard.edu
Chair:
Sheila Jasanoff, Faculty Associate. Pforzheimer Professor of Science and Technology Studies, Harvard Kennedy School.
Lunch is provided if you RSVP via our online form by Thursday, September 27th.
Abstract:
How do algorithms work? As algorithmic systems—from Google’s search engine to Facebook’s newsfeed—have become objects of popular concern, this question has proven vexing. Not only are these black boxes hidden from public view and illegible to the untrained eye, they are also complex, distributed systems. With the advent of techniques like deep learning, algorithmic systems are often described as “uninterpretable”—so complex that it is impossible, even for insider experts, to explain their outputs. And yet, engineers, like ordinary users, are tenacious interpreters, eager to make sense of algorithmic behavior, regardless of its internal complexity. In this talk, I draw on ethnographic fieldwork with developers of algorithmic music recommenders in the US to theorize “interpretability,” describing how engineers interpret supposedly uninterpretable systems. Engineers are not uniquely able to “see” inside algorithmic black boxes but rather learn to listen to them, and their practices of trained subjective judgment are integral to the supposedly rational and quantitative operations of algorithmic systems.
Biography:
Nick Seaver is an assistant professor in the Department of Anthropology and the Program in Science, Technology, and Society at Tufts University. His research examines how technologists translate culturally significant concepts into software infrastructures. He has recently completed a long-term ethnographic project on the developers of algorithmic music recommender systems and is beginning a new project on the technocultural life of attention in machine learning.
