Rauschenberger, MariaBaeza-Yates, RicardoHansen, ChristianNürnberger, AndreasPreim, Bernhard2020-08-182020-08-182020https://dl.gi.de/handle/20.500.12116/33508When discussing interpretable machine learning results, researchers need to compare results and reflect on reliable results, especially for health-related data. The reason is the negative impact of wrong results on a person, such as in missing early screening of dyslexia or wrong prediction of cancer. We present nine criteria that help avoiding over-fitting and biased interpretation of results when having small imbalanced data related to health. We present a use case of early screening of dyslexia with an imbalanced data set using machine learning classification to explain design decisions and discuss issues for further research.enMachine LearningHuman-Centered DesignHCDinteractive systemshealthsmall dataimbalanced dataover-fittingvariancesinterpretable resultsguidelines.Text/Workshop Paper10.18420/muc2020-ws111-333