Challenges of data-driven technologies for social inequality and privacy: empirical research on context and public perceptions

Data-driven technologies are increasingly used by private and public entities for various purposes, promising efficiency gains and the delivery of new services and products. However, societal impacts of these technologies with respect to, among others, social inequality and privacy raise questions of the legitimate use of these technologies. In this dissertation, I emphasize the importance contextual norms to judge the legitimacy of using these technologies (following the notion of “contextual integrity” by Helen Nissenbaum), and highlight context-specific public opinion as a relevant factor for ethical evaluations of data-driven technologies. As an overview, first, processes of algorithmic decision-making as a major data-driven technology are described to show how exactly they may impact social inequality. Then, empirically, results from three survey experiments are presented which demonstrate that public opinion on fairness and privacy issues relating to data-driven technologies depend on social context and timing. Concluding this dissertation, I point out that we need to measure public opinion context-specifically and consider it as one among multiple elements of ethical evaluations of data-driven technologies

Dielectrophoretic trapping of multilayer DNA origami nanostructures and DNA origami-induced local destruction of silicon dioxide

Peer reviewe

Humans versus machines: Who is perceived to decide fairer? Experimental evidence on attitudes toward automated decision-making

Human perceptions of fairness in (semi-)automated decision-making (ADM) constitute a crucial building block toward developing human-centered ADM solutions. However, measuring fairness perceptions is challenging because various context and design characteristics of ADM systems need to be disentangled. Particularly, ADM applications need to use the right degree of automation and granularity of data input to achieve efficiency and public acceptance. We present results from a large-scale vignette experiment that assessed fairness perceptions and the acceptability of ADM systems. The experiment varied context and design dimensions, with an emphasis on who makes the final decision. We show that automated recommendations in combination with a final human decider are perceived as fair as decisions made by a dominant human decider and as fairer than decisions made only by an algorithm. Our results shed light on the context dependence of fairness assessments and show that semi-automation of decision-making processes is often desirable