Conference: Inaugural Berkeley-Stanford Workshop on Veridical Data Science

The Inaugural Berkeley-Stanford Workshop on Veridical Data Science will be held on Friday May 31, 2024, at BIDS on UC Berkeley campus. The organizing committee consists of Bin Yu (UC Berkeley, co-chair), Russ Poldrack (Stanford, co-chair), Maya Mathur (Stanford), and Tiffany Tang (University of Michigan). This conference is jointly hosted by the Berkeley Institute of Data Science (BIDS) and the Department of Statistics, parts of UC Berkeley's College of Computing, Data Science, and Society (CDSS) and Stanford Data Science Center for Open and Reproducible Science (SDS-CORES). Data science is playing a ubiquitous role in science, society, and beyond, underscoring the urgent need to ensure the trustworthiness and safety of data science including machine learning. The data science life cycle (DSLC) is often a complex and multi-step process, including domain problem formulation, data collection and cleaning, data visualization, model/algorithm development, post-hoc visualization, interpretation, vetting and communication. In every step of a DSLC, human judgment calls are made and often unaccounted for in the traditional uncertainty quantification. Veridical data science (VDS) is the practice of rigorously conducting data analysis while making human judgment calls and using domain knowledge to extract and communicate useful, trustworthy information from data to solve a real-world domain problem. The Predictability-Computability-Stability (PCS) framework and documentation have been developed towards veridical data science. This conference is focused on veridical (truthful) data science (VDS) for responsible data analysis and decision-making. There will be 4 keynote speakers and 9 invited speakers. We expect around 100–150 attendees from both academia and industry. 8-10 lightning talk speakers will be selected based on submitted abstracts. This workshop intends to build a community of veridical data science researchers. https://na.eventscloud.com/website/69057 This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.