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- Title
Reply to Letter to the Editor on "Not all biases are bad: equitable and inequitable biases in machine learning and radiology".
- Authors
Pot, Mirjam; Prainsack, Barbara
- Abstract
Keywords: Artificial intelligence; Bias; Inequity EN Artificial intelligence Bias Inequity 1 2 2 11/23/21 20211103 NES 211103 This is a Reply to the Letter to the Editor https://doi.org/10.1186/s13244-021-01022-5. This means that the datasets and algorithms used for machine learning (ML) are never straightforward representations of people, bodies, and populations [[3]], but they are "funhouse mirrors" [[4]] that reflect how access to resources are distributed in a population.
- Subjects
MACHINE learning; PUBLIC health ethics; HEALTH equity; HEALING; RADIOLOGY
- Publication
Insights into Imaging, 2021, Vol 12, Issue 1, p1
- ISSN
1869-4101
- Publication type
Article
- DOI
10.1186/s13244-020-00955-7