This post is a distillation of Evan Hubinger's post "how do we become confident in the safety of a machine learning system?", made as part of the summer 2022 SERI MATS program. While I have attempted to understand and extrapolate Evan's opinions, this post has not been vetted. Likewise, I use training stories (and contribution stories) to describe the methodology of proposals for safe advanced AI without the endorsement of those proposals' authors and based on a relatively shallow understanding of those proposals (due to my inexperience and time constraints). The opinions presented in this post are my own unless otherwise noted.

Epistemic status: Exploratory

Some day, all too soon from now, people will deploy the AI that seals humanity's fate. There are many scenarios for how this comes about or what happens afterward. Some have multiple AI negotiating or fighting for dominance, others one. Some think the handoff of power from humans to AI will go slowly, others fast. But whatever the case, there is one question that these people need to get right: "Is the AI we're about to deploy safe?"

For people to get this answer right when it matters, two things need to happen: we need tools to accurately determine whether an advanced AI is safe, and we need an advanced AI those tools approve of that we can deploy in time for it to matter.

Training stories are a tool to evaluate proposals for making this happen. Specifically, they're meant to analyze complete proposals for training prosaic advanced AI: advanced AI which has similar enough architecture to current systems that most research is cross-applicable. The reason to focus on complete proposals is that it gets us to pay attention to what will be important when push comes to shove. From there, we can backpropagate and get an estimate of what research paths are most beneficial. Evan thinks theoretical prosaic alignment is the most beneficial in expectation for him and many others to pursue, though he supports a broad spectrum of research.

Because I think the original post describes the central concept of training stories very well, I have avoided retreading the same ground, and instead focused on expanding its usability. I will start with some more case studies of using training stories to analyze proposals, and then expand on the original post with contribution stories: a method for analyzing research agendas by h...

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