Untrusted Data

Untrusted data poses a significant challenge across numerous machine learning applications, from semi-supervised learning to meta-analysis and collaborative machine learning. Current research focuses on developing methods to mitigate the risks associated with poisoned, manipulated, or otherwise unreliable data, employing techniques like data purification, robust aggregation algorithms, and conformal prediction to improve model robustness and accuracy. These efforts are crucial for ensuring the reliability and trustworthiness of AI systems, particularly in sensitive domains like healthcare and security, where the consequences of faulty models based on untrusted data can be severe.