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“Excavating AI” Re-excavated: Debunking a Fallacious Account of the JAFFE Dataset
- Author(s):
- Michael Lyons (see profile)
- Date:
- 2021
- Group(s):
- Archives, Digital Art History, Digital Humanists, Linked Open Data, Science and Technology Studies (STS)
- Subject(s):
- Affect (Psychology), Artificial intelligence, Critical theory, Data mining, Information technology--Moral and ethical aspects, Machine learning
- Item Type:
- Article
- Tag(s):
- facial expression, training sets, affective computing, dataset, Affect, Critical data studies, Information ethics
- Permanent URL:
- http://dx.doi.org/10.17613/bw0d-7y90
- Abstract:
- Twenty-five years ago, my colleagues Miyuki Kamachi and Jiro Gyoba and I designed and photographed JAFFE, a set of facial expression images intended for use in a study of face perception. In 2019, without seeking permission or informing us, Kate Crawford and Trevor Paglen exhibited JAFFE in two widely publicized art shows. In addition, they published a nonfactual account of the images in the essay “Excavating AI: The Politics of Images in Machine Learning Training Sets.” The present article recounts the creation of the JAFFE dataset and unravels each of Crawford and Paglen’s fallacious statements. I also discuss JAFFE more broadly in connection with research on facial expression, affective computing, and human-computer interaction.
- Metadata:
- xml
- Published as:
- Online publication
- Status:
- Published
- Last Updated:
- 2 years ago
- License:
- Attribution-NonCommercial-NoDerivatives
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