Title: Isolating effects of age with fair representation learning when assessing dementia,
Abstract: 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 to isolate. In this paper, we show that deep neural network (DNN)
classifiers can infer ages from linguistic features, which is an entanglement
that could lead to unfairness across age groups. We show this problem is caused
by undesired activations of v-structures in causality diagrams, and it could be
addressed 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 disentangled from age. Our best models outperform baseline neural
network classifiers in disentanglement, while compromising accuracy by as
little as 2.56\% and 2.25\% on DementiaBank and the Famous People dataset
respectively.