Understanding Unobservable Failures in AI Safety

This chapter delves into the challenges of recognizing non-obvious failures in AI systems, contrasting easily identifiable problems with those that go unnoticed. The discussion highlights the necessity for alternative research methods, such as anomaly detection, to improve AI alignment and safety amidst subtle biases and complex issues.