Purpose This conceptual contribution is based on the observation that digital inequalities literature has not sufficiently considered digital footprints as an important social differentiator. The purpose of the paper is to inspire current digital inequality frameworks to include this new dimension. Design/methodology/approach Literature on digital inequalities is combined with research on privacy, big data and algorithms. The focus on current findings from an interdisciplinary point of view allows for a synthesis of different perspectives and conceptual development of digital footprints as (...) a new dimension of digital inequality. Findings Digital footprints originate from active content creation, passive participation and platform-generated data. The literature review shows how different social groups may experience systematic advantages or disadvantages based on their digital footprints. A special emphasis should be on those at the margins, for example, users of low socioeconomic background. Originality/value By combining largely independent research fields, the contribution opens new avenues for studying digital inequalities, including innovative methodologies to do so. (shrink)

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. (shrink)