  Every step we take in the digital world leaves behind a record of our
behavior; a digital footprint. Research has suggested that algorithms can
translate these digital footprints into accurate estimates of psychological
characteristics, including personality traits, mental health or intelligence.
The mechanisms by which AI generates these insights, however, often remain
opaque. In this paper, we show how Explainable AI (XAI) can help domain experts
and data subjects validate, question, and improve models that classify
psychological traits from digital footprints. We elaborate on two popular XAI
methods (rule extraction and counterfactual explanations) in the context of Big
Five personality predictions (traits and facets) from financial transactions
data (N = 6,408). First, we demonstrate how global rule extraction sheds light
on the spending patterns identified by the model as most predictive for
personality, and discuss how these rules can be used to explain, validate, and
improve the model. Second, we implement local rule extraction to show that
individuals are assigned to personality classes because of their unique
financial behavior, and that there exists a positive link between the model's
prediction confidence and the number of features that contributed to the
prediction. Our experiments highlight the importance of both global and local
XAI methods. By better understanding how predictive models work in general as
well as how they derive an outcome for a particular person, XAI promotes
accountability in a world in which AI impacts the lives of billions of people
around the world.
