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Excavating 'Excavating AI': The Elephant in the Gallery
- 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):
- data ethics, dataset, digital ethics, Affect, Critical data studies, Information ethics, Theories of affect
- Permanent URL:
- http://dx.doi.org/10.17613/dpg9-r665
- Abstract:
- Two art exhibitions, "Training Humans" and "Making Faces," and the accompanying essay "Excavating AI: The politics of images in machine learning training sets" by Kate Crawford and Trevor Paglen, are making substantial impact on discourse taking place in the social and mass media networks, and some scholarly circles. Critical scrutiny reveals, however, a self-contradictory stance regarding informed consent for the use of facial images, as well as serious flaws in their critique of ML training sets. Our analysis underlines the non-negotiability of informed consent when using human data in artistic and other contexts, and clarifies issues relating to the description of ML training sets.
- Notes:
- PDF file with 15 pages, 4 figures
- Metadata:
- xml
- Status:
- Published
- Last Updated:
- 2 years ago
- License:
- Attribution
- Share this:
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2009.01215.pdf