  Machine behavior that is based on learning algorithms can be significantly
influenced by the exposure to data of different qualities. Up to now, those
qualities are solely measured in technical terms, but not in ethical ones,
despite the significant role of training and annotation data in supervised
machine learning. This is the first study to fill this gap by describing new
dimensions of data quality for supervised machine learning applications. Based
on the rationale that different social and psychological backgrounds of
individuals correlate in practice with different modes of
human-computer-interaction, the paper describes from an ethical perspective how
varying qualities of behavioral data that individuals leave behind while using
digital technologies have socially relevant ramification for the development of
machine learning applications. The specific objective of this study is to
describe how training data can be selected according to ethical assessments of
the behavior it originates from, establishing an innovative filter regime to
transition from the big data rationale n = all to a more selective way of
processing data for training sets in machine learning. The overarching aim of
this research is to promote methods for achieving beneficial machine learning
applications that could be widely useful for industry as well as academia.
