|4 May 2022||15:30–17:00||Online|
Speaker: Leo Impett (Lecturer in Digital Humanities and Convenor of the MPhil in Digital Humanities, University of Cambridge)
About this event
This paper will try to address the specifically visual component of “bias” in computer vision. Following Ted Underwood’s work on large language models, Leo will try to understand computer vision models as models of visual culture, looking at their use in both art-historical research and smartphone photography. Reflecting on the incompleteness and inadequacy of technical solutions for identifying and mitigating bias in computer vision, Leo will attempt to highlight the potential of using digital art history methods to study algorithms rather than art. This aims to open the door to digital art history projects that, by engaging critically with computer vision, enable new ways of thinking about visual culture as inscribed across pictures and algorithms.
The seminar Respondent will be Fabian Offert (University of California) and the Chair will be Dr Annja Neumann (University of Cambridge).
This event will be held virtually. All ticket holders have an opportunity to participate via question submission during the Q&A portion of the seminar.
About the speaker
Dr Leonardo Impett is a Lecturer in Digital Humanities and convenor of the MPhil in Digital Humanities. He was previously Assistant Professor of Computer Science at Durham University. Leonardo has a background in information engineering and machine learning, having worked or studied at the Cambridge Machine Learning Lab, the Cambridge Computer Lab’s Rainbow Group, and Microsoft Research Cairo. His PhD, with Sabine Süsstrunk and Franco Moretti at EPFL, was on the use of computer vision for the “distant reading” of the history of art. In 2018 Leonardo was a DH fellow at Villa I Tatti – the Harvard University Center for Italian Renaissance Studies; from 2018-2020, he was Scientific Assistant, then Scientist, at the Bibliotheca Hertziana – Max Planck Institute for Art History in Rome. Alongside his research in digital art history, he frequently works with machine learning in arts and culture, including for the Liverpool Biennial, the Royal Opera House, and the Whitney Museum of American Art.