Note: This post presents a framework for assessing and thinking about AI risk. It is not referencing the YouTube channel or any other previous content.

Most conversations about AI risks seem like people are talking past each other. There’s a lot of strawmanning. This is largely because the issue is complex, and in order to make it manageable, the concepts get oversimplified. The most common example of this is p(doom), collapsing extreme outcomes into a single variable and focusing on that. Critics happily jump on the fact that there are many other possible bad outcomes, or that 100% extinction rates are an extremely high bar. There’s some legitimacy to this.

So here I want to try to grapple with the the complexity of the larger web of issues surrounding bad AI outcomes by presenting The AI Risk Network. If you’re interested in this topic, bear with me. It might be a bit of a slog.

First I want to contrast this approach with others. Liron Shapira has what he calls The Doom Train, a linear progression through various dependencies or thresholds that eventually lead to human extinction, with various ‘stops’ along the way where the skeptic can get off.

Shapira uses this as a discussion guide to focus on particular points where the skeptic gets off the train and exits belief in the extreme outcome of human extinction or permanent disempowerment. This can be useful, if you want to focus on that one outcome (which is admittedly the most important in terms of scale, but maybe not in likelihood). But like I said, this framework is hyperfocused, linear, and binary, which makes it subject to some reasonably fair criticisms. You progress through the conditions, and if they’re not met, you get completely off the train, and then we either hash that condition out, or the discussion is over. And we’re only talking about the one bad outcome.

Even a framework like this comes with some complexity, so I’m not making anything easier here. Buckle up.

Here is my current thinking on AI risk:

It’s organized with three columns: drivers, scenarios, and severity bins.

The drivers are the causal mechanisms for various scenarios, and they don’t just include achieving superintelligence arising, leading to extinction scenarios. They include the more near-term, immediate, somewhat more mundane causal factors we’re already contending with, things like AI dependence and misplaced trust, which even mos...

Evander H.

Evander H.