Accountability, Evaluation, and Obscurity of AI Algorithms

Schedule
2021
Monday, January 4th
12:00 AM	An Adversarial Training Based Machine Learning Approach to Malware Classification under Adversarial Conditions Sean Devine, United States Army Nathaniel Bastian, Army Cyber Institute, U.S. Military Academy Online 12:00 AM - 12:00 AM Visit Site
12:00 AM	Bias in Geographic Information Systems: The Case of Google Maps Ben Wagner, TBA Soheil Human, Vienna University of Economics and Business Till Winkler, Vienna University of Economics and Business Online 12:00 AM - 12:00 AM Visit Site
12:00 AM	How Useful are Hand-crafted Data? Making Cases for Anomaly Detection Methods Len Du, United Australian Artificial Intelligence Marcus Hutter, Australian National University Online 12:00 AM - 12:00 AM Visit Site
12:00 AM	Introduction to the Minitrack on Accountability, Evaluation, and Obscurity of AI Algorithms Radmila Juric, University of South East Norway Robert Steele, Capitol Technology University Online 12:00 AM - 12:00 AM Visit Site
12:00 AM	The Effect of Training Set Timeframe on the Future Performance of Machine Learning-based Malware Detection Models Colin Galen, Capitol Technology University Robert Steele, Capitol Technology University Online 12:00 AM - 12:00 AM Visit Site

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