A System Framework for Personalized and Transparent Data-Driven Decisions

Sarah Oppold, Melanie Herschel

Abstract: Decision support systems that rely on data analytics are used in numerous applications. Their advantages are indisputable, however, they also present risks, possibly having severe impact on people’s lives. Consequently, the need to support ethical or responsible behavior of such systems has recently emerged, putting an emphasis on ensuring fairness, transparency, accountability, etc. This paper presents a novel system framework that offers transparent and personalized services tailored to user profiles to serve their best interest. Our framework personalizes the choice of model for individuals or groups of users based on metadata about data sets and machine learning models. Querying and processing these metadata ensures transparency by supporting various kinds of queries by different stakeholders. We discuss our framework in detail, show why existing solutions are inadequate, and highlight research questions that need to be tackled in the future. Based on a prototypical implementation, we showcase that even a baseline implementation of our framework supports the desired transparency and personalization.