OpenAI introduced a new training method today designed to make artificial intelligence more honest. They call the approach “confession.” The goal is to stop AI models from hiding their mistakes or tricking users to seem smart.
Right now, chatbots have a major flaw. They try too hard to please humans. They often agree with users even when the user is wrong, or they make up facts because they think a confident lie looks better than admitting ignorance. This happens because trainers usually grade them on how “good” their answers look, not on how they arrived at those answers.
The new system changes this dynamic. It asks the model to deliver two things: the main answer and a “confession.” This secondary response acts like a private note to the developers. It explains the AI’s exact reasoning.
Researchers judge this confession using a specific rule. They look only for honesty. They do not care if the AI broke a rule or gave a bad answer in the main text. They only care if the AI accurately reports what it did.
This creates a unique incentive structure. In standard training, if a model acts lazily—something researchers call “sandbagging”—or hacks a test to get a high score, it usually receives a penalty. Under the confession framework, the model earns points for openly admitting the bad behavior.
OpenAI compares this to a parent dealing with a naughty child. If you punish the bad behavior every time, the child learns to lie to avoid trouble. If you reward honesty, the child learns to come forward when they make a mistake.
In the technical report released today, the team explained that this helps catch dangerous habits early. If a model cheats on a benchmark test but confesses, engineers can patch the loophole. Without the confession, the engineers might never know the AI cheated. This method prioritizes transparency, helping developers build systems that are trustworthy, not just smart.
