Objective: 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.
Method: The methodological approach used in this paper is theoretical and ethical analysis.
Result: We show that the question of ensuring comprehensive ML fairness is interrelated to three quandaries and one dilemma.
Discussion: 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.
Conclusion: 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.
Keywords: artificial intelligence; delivery of health care; health equity; machine learning; public health.
© Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.