Algorithmic encoding of protected characteristics in chest X-ray disease detection models.

Journal: EBioMedicine
Published Date:

Abstract

BACKGROUND: It has been rightfully emphasized that the use of AI for clinical decision making could amplify health disparities. An algorithm may encode protected characteristics, and then use this information for making predictions due to undesirable correlations in the (historical) training data. It remains unclear how we can establish whether such information is actually used. Besides the scarcity of data from underserved populations, very little is known about how dataset biases manifest in predictive models and how this may result in disparate performance. This article aims to shed some light on these issues by exploring methodology for subgroup analysis in image-based disease detection models.

Authors

  • Ben Glocker
    Kheiron Medical Technologies, London, UK.
  • Charles Jones
    Department of Computing, Imperial College London, London, United Kingdom.
  • Mélanie Bernhardt
    Health Intelligence, Microsoft Research Cambridge, Cambridge, CB1 2FB, UK.
  • Stefan Winzeck
    University Division of Anaesthesia, Department of Medicine, University of Cambridge, United Kingdom (S.W.).