Everything Sandbox Gazette has reported about machine learning: 5 stories, newest first. Part of our AI coverage.
Algorithmic bias emerges when historical patterns in training data reinforce existing inequalities, causing automated systems to make unfair predictions that human operators often miss.
Machine learning models fail when training data lacks the specific attack patterns they face in production, creating blind spots that rule-based systems miss entirely.
Models optimize for statistical patterns, not truth, creating blind spots that attackers exploit through data poisoning and prompt injection.
Your fraud detection model will fail if you train it on clean data that lacks the subtle anomalies attackers actually use to bypass your rules.
Map your machine learning lifecycle to known attack patterns to stop data poisoning and model theft before deployment.