General Evaluations (AI outputs for which evaluation is too difficult for humans)

"When AI models are trained through evaluation with human feedback, such as reinforcement learning from human feedback, their outputs can be challenging to assess, as they may contain hard-to-detect errors or issues that only become apparent over time. The human evaluator can rate incorrect outputs positively or similar to correct outputs. This can lead to the model learning to produce subtly incorrect or harmful outputs, such as code with software vulnerabilities, or politically biased information. In extreme cases where a model is deceiving users, complicated outputs can contain hidden errors or backdoors."