Author: Rachel Thomas

Things can go disastrously wrong in data science and machine learning projects when we undervalue data work, use data in contexts that it wasn’t gathered for, or ignore the crucial role that humans play in the data science pipeline. A new multi-university centre focused on Information Resilience, funded by the Australian government’s top scientific funding body (ARC), has recently launched. Information Resilience is the capacity to detect and respond to failures and risks across the information chain in which data is sourced, shared, transformed, analysed, and consumed. I’m honored to be a member of the strategy board, and I have been thinking about what information resilience means with respect to data practices. Through a series of case studies and relevant research papers, I will highlight these risks and point towards more resilient practices.