Host: Digital Research Alliance of Canada

Online workshop description:  A thought-provoking exploration of the implications of data scraping in a world changed by AI. What is "public data" anyway? This session delves into the concept of "Public Data" as seen under both the academic research and general lens. We'll discuss the impacts of accessing and collecting data as well as the knock-on effects of using machine learning models trained with it. Factors such as privacy, cybersecurity, copyright, legislation, acceptable use, and applicable organizational policy all factor into this complex topic. In a time when many are looking for simple answers, the situation is anything but simple. While some desire to collect data, others are seeking ways to prevent their data from being scraped in the first place, or at least get their lawyers involved after the fact.

This presentation is facilitated by Scott Baker. Scott manages the Research Cybersecurity and Compliance team at UBC Advanced Research Computing: a group focused on supporting researchers to find secure solutions for their project architectures and research data as well as navigating compliance requirements. He is a member of the program leadership committee for UBC's Privacy & Information Security Management program. He is also a member of the Digital Research Alliance of Canada national security council where he chairs the governance and policy working group.