  Personal electronic devices including smartphones give access to behavioural
signals that can be used to learn about the characteristics and preferences of
individuals. In this study, we explore the connection between demographic and
psychological attributes and the digital behavioural records, for a cohort of
7,633 people, closely representative of the US population with respect to
gender, age, geographical distribution, education, and income. Along with the
demographic data, we collected self-reported assessments on validated
psychometric questionnaires for moral traits and basic human values and
combined this information with passively collected multi-modal digital data
from web browsing behaviour and smartphone usage. A machine learning framework
was then designed to infer both the demographic and psychological attributes
from the behavioural data. In a cross-validated setting, our models predicted
demographic attributes with good accuracy as measured by the weighted AUROC
score (Area Under the Receiver Operating Characteristic), but were less
performant for the moral traits and human values. These results call for
further investigation since they are still far from unveiling individuals'
psychological fabric. This connection, along with the most predictive features
that we provide for each attribute, might prove useful for designing
personalised services, communication strategies, and interventions, and can be
used to sketch a portrait of people with a similar worldview.
