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Fig. 1 | Insights into Imaging

Fig. 1

From: Letter to the editor: “Not all biases are bad: equitable and inequitable biases in machine learning and radiology”

Fig. 1

An artificial intelligence powered diagnostic tool in radiology presented as a weighing scale for understanding. The tool reduces the weight of a rich person by 5 kg but uses the actual weight of a poor person. It is unfairly biased in favor of rich people leading to these patients being diagnosed as overweight less frequently. To resolve the bias, if one prioritizes the principle of equity, and reduces the weight of a poor person by 5 kg as well, the outcome of the scale is wrong for both groups of patients. If one focuses on obtaining a correct weight for all the patients, one can cancel the error on the biased scale or inform the user that the result may be biased. In conclusion, in terms of debiasing strategy, the patient’s medical interest prevails over the principle of social equity. AI Artificial intelligence

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