The 2021 Conference on Computer Vision and Pattern Recognition, the premier conference of its kind, will feature two papers co-authored by an LLNL researcher targeted at improving the understanding of robust machine learning models. Both papers include contributions from LLNL computer scientist Bhavya Kailkhura and examine the importance of data in building models, part of a Lab effort to develop foolproof artificial intelligence and machine learning systems. The first paper focuses on “poisoning” attacks to data that malicious hackers or adversaries might use to trick artificial intelligence into making mistakes, such as manipulating facial recognition systems to commit fraud or causing autonomous drones to crash. In the second paper, Kailkhura and co-authors examined approaches to evaluating data importance. Read more at LLNL News.