As the capabilities of large machine learning models continue to grow, and as the autonomy afforded to such models continues to expand, the spectre of a new adversary looms: the models themselves. The threat that a model might behave in a seemingly reasonable manner, while secretly and subtly modifying its behavior for ulterior reasons is often referred to as deceptive alignment in the AI Safety & Alignment communities. Consequently, we call this new direction Deceptive Alignment Monitoring. In this work, we identify emerging directions in diverse machine learning subfields that we believe will become increasingly important and intertwined in the near future for deceptive alignment monitoring, and we argue that advances in these fields present both long-term challenges and new research opportunities. We conclude by advocating for greater involvement by the adversarial machine learning community in these emerging directions.

大型机器学习模型的能力不断增长，其自主性也不断扩大，因此越来越重要的是对欺骗性对齐进行监控，我们提出了这一全新的方向，识别机器学习领域中新兴的方向，旨在发现欺骗性对齐现象，提出长期挑战和研究机会，并呼吁敌对机器学习社区更多地参与这些新兴方向。

欺骗性对齐监测