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.
Can we find a way to protect our data, maximize the potential benefits of data-intensive research and machine learning training, all while not ending up in serious trouble? Join the discussion and find out!
Manager, Sensitive Research, University of British Columbia
Scott Baker is the Manager, Sensitive Research at UBC Advanced Research Computing. He leads a group of privacy and security professionals focused on helping researchers find balanced solutions for their research projects. He is a member of the Digital Research Alliance of Canada national security council, and has over 25 years of experience managing people, projects, and IT systems.