  We uncover an underlying bias present in the audio recordings produced from
the picture description task of the Pitt corpus, the largest publicly
accessible database for Alzheimer's Disease (AD) detection research. Even by
solely utilizing the silent segments of these audio recordings, we achieve
nearly 100% accuracy in AD detection. However, employing the same methods to
other datasets and preprocessed Pitt recordings results in typical levels
(approximately 80%) of AD detection accuracy. These results demonstrate a
Clever Hans effect in AD detection on the Pitt corpus. Our findings emphasize
the crucial importance of maintaining vigilance regarding inherent biases in
datasets utilized for training deep learning models, and highlight the
necessity for a better understanding of the models' performance.
