Deception is a particularly worrying alignment failure mode because it makes it difficult for us to realize that we have made a mistake: at training time, a deceptive misaligned model and an aligned model make the same behavior.
There are two ways for deception to appear:
An action chosen instrumentally due to non-myopic future goals that are better achieved by deceiving humans now so that it has more power to achieve its goals in the future.
Because deception was directly selected for as an action.
Another way of describing the difference is that 1 follows from an inner alignment failure: a mesaoptimizer learned an unintended mesaobjective that performs well on training, while 2 follows from an outer alignment failure — an imperfect reward signal.
Classic discussion of deception focuses on 1 (example 1, example 2), but I think that 2 is very important as well, particularly because the most common currently used alignment strategy is RLHF, which actively selects for deception.
Once the AI has the ability to create strategies that involve deceiving the human, even without explicitly modeling the human, those strategies will win out and end up eliciting a lot of reward. This is related to the informed oversight problem: it is really hard to give feedback to a model that is smarter than you. I view this as a key problem with RLHF. To my knowledge very little work has been done exploring this and finding more empirical examples of RLHF models learning to deceive the humans giving it feedback, which is surprising to me because it seems like it should be possible.
I've been exploring evolutionary metaphors to ML, so here's a toy metaphor for RLHF: recessive persistence. (Still just trying to learn both fields, however.)
"Since loss-of-function mutations tend to be recessive (given that dominant mutations of this type generally prevent the organism from reproducing and thereby passing the gene on to the next generation), the result of any cross between the two populations will be fitter than the parent." (k)
An interpretable system trained for the primary task of being deceptive should honestly explain its devious plots in a separate output. An RLHF-tuned agent loses access to the original SSL-trained map of the world.
So the most obvious problem is the wrong type signature of model behaviors, there should be more inbuilt side channels to its implied cognition used to express and train capabilities/measurements relevant to what's going on semantically inside the model, not just externally observed output for its primary task, out of a black box.
2Thomas Larsen
I'm excited for ideas for concrete training set ups that would induce deception2 in an RLHF model, especially in the context of an LLM -- I'm excited about people posting any ideas here. :)