Algorithmic encoding of protected characteristics and its implications
on disparities across subgroups
- URL: http://arxiv.org/abs/2110.14755v1
- Date: Wed, 27 Oct 2021 20:30:57 GMT
- Title: Algorithmic encoding of protected characteristics and its implications
on disparities across subgroups
- Authors: Ben Glocker and Stefan Winzeck
- Abstract summary: Machine learning models may pick up undesirable correlations between a patient's racial identity and clinical outcome.
Very little is known about how these biases are encoded and how one may reduce or even remove disparate performance.
- Score: 17.415882865534638
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: It has been rightfully emphasized that the use of AI for clinical decision
making could amplify health disparities. A machine learning model may pick up
undesirable correlations, for example, between a patient's racial identity and
clinical outcome. Such correlations are often present in (historical) data used
for model development. There has been an increase in studies reporting biases
in disease detection models across patient subgroups. Besides the scarcity of
data from underserved populations, very little is known about how these biases
are encoded and how one may reduce or even remove disparate performance. There
is some speculation whether algorithms may recognize patient characteristics
such as biological sex or racial identity, and then directly or indirectly use
this information when making predictions. But it remains unclear how we can
establish whether such information is actually used. This article aims to shed
some light on these issues by exploring new methodology allowing intuitive
inspections of the inner working of machine learning models for image-based
detection of disease. We also evaluate an effective yet debatable technique for
addressing disparities leveraging the automatic prediction of patient
characteristics, resulting in models with comparable true and false positive
rates across subgroups. Our findings may stimulate the discussion about safe
and ethical use of AI.
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