Towards Fair ML‐based Language Assessment Methods for Detecting Alzheimer’s Disease

Alzheimer's disease (AD) tends to affect the parietal lobe, which is responsible for language functions in the patient's brain. Thus, one significant impairment associated with AD is language impairment which is the cause that patients with AD have deficits at the word level (vocabulary size in speech can be an early sign of cognitive impairment [1]), sentence‐level, and discourse‐level of their languages. Thus, language disorders can be considered as markers to diagnose AD in a patient at its earliest stage [2]. The recent progress in the machine learning (ML) domain has revolutionized the early detection of AD in particular developing and deploying ML‐based language assessment (MLLA) methods for detecting AD at its mild cognitive impairment stage [3]. The ML community needs to develop fair MLLA methods for detecting individuals with AD.