Can medical algorithms be fair? Three ethical quandaries and one dilemma.

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.