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Published on: January 11, 2020
Implicit data crimes: Machine learning bias arising from misuse of public data
Efrat Shimron1, Jonathan I Tamir2,3,4, Ke Wang1
1Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA 94720.
Abstract:
SignificancePublic databases are an important resource for machine learning research, but their growing availability sometimes leads to "off-label" usage, where data published for one task are used for another. This work reveals that such off-label usage could lead to biased, overly optimistic results of machine-learning algorithms. The underlying cause is that public data are processed with hidden processing pipelines that alter the data features. Here we study three well-known algorithms developed for image reconstruction from magnetic resonance imaging measurements and show they could produce biased results with up to 48% artificial improvement when applied to public databases. We relate to the publication of such results as implicit "data crimes" to raise community awareness of this growing big data problem.
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