Based on the National Educational Panel Study (NEPS), the study provides an overview of the distribution of digital literacy in Germany up to the beginning of the Covid pandemic. Already in childhood and adolescence, there are systematic differences in digital literacy depending on socio-economic background. Children with a migration background and those with unemployed parents show particularly low digital literacy. Gender-specific differences in digital literacy are small in childhood and adolescence, but clearly pronounced among adults. In addition, people with little formal education and people with a migration background have systematically lower digital competences in adulthood. The education sector should therefore promote the digital competences of children and young people at an early stage in order to compensate for the apparently low level of competence development outside the formal education sector. Educational opportunities for digitally less competent adults should also be strengthened to enable older generations to continue to participate in the changing spheres of life, education, and work.
A crucial building block of responsible artificial intelligence is responsible data governance, including data collection. Its importance is also underlined in the latest EU regulations. The data should be of high quality, foremost correct and representative, and individuals providing the data should have autonomy over what data is collected. In this paper, we consider the setting of collecting personally measured fitness data (physical activity measurements), in which some individuals may not have an incentive to measure and report accurate data. This can significantly degrade the quality of the collected data. On the other hand, high-quality collective data of this nature could be used for reliable scientific insights or to build trustworthy artificial intelligence applications. We conduct a framed field experiment (N = 691) to examine the effect of offering fixed and quality-dependent monetary incentives, on the quality of the collected data. We use a peer-based incentive-compatible mechanism for the quality-dependent incentives without spot-checking or surveilling individuals. We find that the incentive-compatible mechanism can elicit good quality data while providing a good user experience and compensating fairly, although, in the specific study context, the data quality does not necessarily differ under the two incentive schemes. We contribute new design insights from the experiment and discuss directions that future field experiments and applications on explainable and transparent data collection may focus on.
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