The ability to accurately assess and represent the truthfulness of data is considered one of the key challenges in managing and processing big data. With the requested basic funding, we aim to further develop our fundamental research on logical frameworks for reasoning in a context where not all data are equally reliable. Specifically, we aim to study how (un)reliability of data propagates in data processing and manifests in the results of a data processing process. We want to Initially focus this research on criteria evaluation in database querying and decision support techniques. Additionally, we aim to investigate whether and how the discoverd techniques can contribute to more reliable artificial intelligence.