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

TY - JOUR

T1 - Not all biases are bad

T2 - equitable and inequitable biases in machine learning and radiology

AU - Pot, Mirjam

AU - Kieusseyan, Nathalie

AU - Prainsack, Barbara

N1 - Publisher Copyright: © 2021, The Author(s). Copyright: Copyright 2021 Elsevier B.V., All rights reserved.

PY - 2021/12

Y1 - 2021/12

N2 - The application of machine learning (ML) technologies in medicine generally but also in radiology more specifically is hoped to improve clinical processes and the provision of healthcare. A central motivation in this regard is to advance patient treatment by reducing human error and increasing the accuracy of prognosis, diagnosis and therapy decisions. There is, however, also increasing awareness about bias in ML technologies and its potentially harmful consequences. Biases refer to systematic distortions of datasets, algorithms, or human decision making. These systematic distortions are understood to have negative effects on the quality of an outcome in terms of accuracy, fairness, or transparency. But biases are not only a technical problem that requires a technical solution. Because they often also have a social dimension, the ‘distorted’ outcomes they yield often have implications for equity. This paper assesses different types of biases that can emerge within applications of ML in radiology, and discusses in what cases such biases are problematic. Drawing upon theories of equity in healthcare, we argue that while some biases are harmful and should be acted upon, others might be unproblematic and even desirable—exactly because they can contribute to overcome inequities.

AB - The application of machine learning (ML) technologies in medicine generally but also in radiology more specifically is hoped to improve clinical processes and the provision of healthcare. A central motivation in this regard is to advance patient treatment by reducing human error and increasing the accuracy of prognosis, diagnosis and therapy decisions. There is, however, also increasing awareness about bias in ML technologies and its potentially harmful consequences. Biases refer to systematic distortions of datasets, algorithms, or human decision making. These systematic distortions are understood to have negative effects on the quality of an outcome in terms of accuracy, fairness, or transparency. But biases are not only a technical problem that requires a technical solution. Because they often also have a social dimension, the ‘distorted’ outcomes they yield often have implications for equity. This paper assesses different types of biases that can emerge within applications of ML in radiology, and discusses in what cases such biases are problematic. Drawing upon theories of equity in healthcare, we argue that while some biases are harmful and should be acted upon, others might be unproblematic and even desirable—exactly because they can contribute to overcome inequities.

KW - Bias

KW - Equity

KW - Ethics

KW - Machine learning

KW - Radiology

UR - http://www.scopus.com/inward/record.url?scp=85101028235&partnerID=8YFLogxK

U2 - 10.1186/s13244-020-00955-7

DO - 10.1186/s13244-020-00955-7

M3 - Review article

AN - SCOPUS:85101028235

SN - 1869-4101

VL - 12

JO - Insights into imaging

JF - Insights into imaging

IS - 1

M1 - 13

ER -