Work

Implicit data crimes: Machine learning bias arising from misuse of public data

Year: 2022

Type: article

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.

Source: Proceedings of the National Academy of Sciences

DOI: 10.1073/pnas.2117203119

Authors: Efrat Shimron, Jonathan I. Tamir, Ke Wang, Michael Lustig

Institutions: University of California, Berkeley, The University of Texas at Austin

Language: English

Topic: Ethics and Social Impacts of AI

Subfield: Safety Research

Field: Social Sciences

Domain: Social Sciences

Open Access status: hybrid

Funders: National Science Foundation, National Institutes of Health, National Heart, Lung, and Blood Institute, National Institute of Biomedical Imaging and Bioengineering