Data quality has been a major concern of organizations for decades. The recent advances in artificial intelligence (AI) have brought data quality (DQ) back into the spotlight: while many recent data quality and cleaning solutions are powered by machine learning (ML), DQ is a core requirement to ensure reliable AI-based systems. DQ is tackled from different perspectives by different research communities, including database, ML, and information systems. We believe it is important to bring together these communities to foster a vital discussion about the future of DQ assessment and improvement. QDB'23 revives the successful QDB workshop series to cover the needs of the AI era, addressing both industry and academia (cf. data-centric AI). The workshop aims to (1) revive vital discussions about data quality, and (2) specifically exchange novel ideas and best practices about data quality assessment and improvement in the context of AI-based systems. In 2023, we accepted five regular research papers, which have been peer-reviewed by a selected list of DQ researchers covering different communities (database, ML, information systems). The full-day workshop on August 28, 2023, will also include an interactive discussion session on the future of data quality and feature two invited keynotes by Renée Miller (Northeastern University, USA) and Theodoros Rekatsinas (Apple, USA).