AI safety by debate via regret minimization

We consider the setting of AI safety by debate as a repeated game. We consider the question of efficient regret minimization in this setting, when the players are either AIs or humans, equipped with access to computationally superior AIs. In such a setting, we characterize when internal and external regret can be minimized efficiently. We conclude with conditions in which a sequence of strategies converges to a correlated equilibrium.