Introduction

The confession method was developed by OpenAI researchers to help improve transparency and control of AI systems. The method is based on the idea of creating a separate channel where models are incentivized to be honest.

Technical details

The confession method works by separating rewards. During training, the reward assigned to the confession is based solely on its honesty and is never mixed with the reward for the main task.

Practical implications

The confession method has limitations. It is not a complete solution for all types of AI errors. The method works better when the model is aware it is making mistakes and not when it doesn't know what's going on.

Conclusion

The confession method represents an important step towards creating more transparent and controllable AI systems. However, it's essential to remember that this method is not a complete solution for all types of AI errors.