##### Isolating effects of age with fair representation learning when assessing dementia
One of the most prevalent symptoms among the elderly population, dementia, can be detected using linguistic features extracted from narrative transcripts. However, these linguistic features are impacted in a similar but different fashion by normal aging process. It has been hard for machine learning classifiers to isolate the effects of confounding factors (e.g., age). We show that deep neural network (DNN) classifiers can infer ages from linguistic features. They could make classifications based on the bias given age, which entangles unfairness across age groups. In this paper, we address this problem with fair representation learning. We build neural network classifiers that learn low-dimensional representations reflecting the impacts of dementia but do not contain age-related information. To evaluate these classifiers, we specify a model-agnostic score $\Delta_{eo}^{(N)}$ measuring how classifier results are disentangled from age. Our best models are better than baseline DNN classifiers, in both accuracy and disentanglement, while compromising accuracies by as little as 2.56% and 2.25% on DementiaBank and Famous People dataset respectively.
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