Department of Global Public Health and Primary Care, University of Bergen, Bergen, Norway
Centre for the Study of Professions, Oslo Metropolitan University, Oslo, Akershus, Norway.
BMJ Health Care Inform. 2022 Apr;29(1). doi: 10.1136/bmjhci-2021-100445.
To demonstrate what it takes to reconcile the idea of fairness in medical algorithms and machine learning (ML) with the broader discourse of fairness and health equality in health research.
The methodological approach used in this paper is theoretical and ethical analysis.
We show that the question of ensuring comprehensive ML fairness is interrelated to three quandaries and one dilemma.
As fairness in ML depends on a nexus of inherent justice and fairness concerns embedded in health research, a comprehensive conceptualisation is called for to make the notion useful.
This paper demonstrates that more analytical work is needed to conceptualise fairness in ML so it adequately reflects the complexity of justice and fairness concerns within the field of health research.
展示如何在医疗算法和机器学习（ML）中协调公平理念，并将其与健康研究中更广泛的公平和平等健康话语联系起来。
本文采用的方法论方法是理论和伦理分析。
我们表明，确保全面的 ML 公平性的问题与三个困境和一个困境有关。
由于 ML 中的公平性取决于健康研究中内在正义和公平问题的联系，因此需要进行全面的概念化，以使这一概念变得有用。
本文表明，需要进行更多的分析工作来概念化 ML 中的公平性，以便充分反映健康研究领域中正义和平等问题的复杂性。
