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Table 1 Different types of bias in healthcare (authors’ depiction; see also [20, 33])

From: Not all biases are bad: equitable and inequitable biases in machine learning and radiology

Type of bias Bias is rooted in Example Effects
Data bias Low quality of datasets, due to   All types of biases can translate into machine biases and have effects on equity, accountability, and transparency
  (1) quantitative misrepresentation of certain patient groups in datasets and/or A set of training data for machine learning includes images of only (or mostly) males  
  (2) qualitative misrepresentation of certain patient groups in datasets (e.g. wrong labeling of images) A set of training data for machine learning includes images of socio-economically disadvantaged women as “healthy” controls, although some of them have diseases that were not diagnosed  
Cognitive bias Features in the human processing of knowledge and cultural factors Radiologists’ culturally informed stereotypes lead to a high rate of false negatives in breast images of socio-economically disadvantaged women