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Machine behavior that is based on learning algorithms can be significantlyinfluenced by the exposure to data of different qualities. Up to now, thosequalities are solely measured in technical terms, but not in ethical ones,despite the significant role of training and annotation data in supervisedmachine learning. This is the first study to fill this gap by describing newdimensions of data quality for supervised machine learning applications. Basedon the rationale that different social and psychological backgrounds ofindividuals correlate in practice with different modes ofhuman-computer-interaction, the paper describes from an ethical perspective howvarying qualities of behavioral data that individuals leave behind while usingdigital technologies have socially relevant ramification for the development ofmachine learning applications. The specific objective of this study is todescribe how training data can be selected according to ethical assessments ofthe behavior it originates from, establishing an innovative filter regime totransition from the big data rationale n = all to a more selective way ofprocessing data for training sets in machine learning. The overarching aim ofthis research is to promote methods for achieving beneficial machine learningapplications that could be widely useful for industry as well as academia.

Ethical behavior in humans and machines -- Evaluating training data quality for beneficial machine learning