  One of the most prevalent symptoms among the elderly population, dementia,
can be detected by classifiers trained on linguistic features extracted from
narrative transcripts. However, these linguistic features are impacted in a
similar but different fashion by the normal aging process. Aging is therefore a
confounding factor, whose effects have been hard for machine learning
classifiers (especially deep neural network based models) to ignore. We show
DNN models are capable of estimating ages based on linguistic features.
Predicting dementia based on this aging bias could lead to potentially
non-generalizable accuracies on clinical datasets, if not properly
deconfounded.
  In this paper, we propose to address this deconfounding problem with fair
representation learning. We build neural network classifiers that learn
low-dimensional representations reflecting the impacts of dementia yet
discarding the effects of age. To evaluate these classifiers, we specify a
model-agnostic score $\Delta_{eo}^{(N)}$ measuring how classifier results are
deconfounded from age. Our best models compromise accuracy by only 2.56\% and
1.54\% on two clinical datasets compared to DNNs, and their $\Delta_{eo}^{(2)}$
scores are better than statistical (residulization and inverse probability
weight) adjustments.
