Jan 14, 2018
We (Alexey Turchin and David Denkenberger) have a new paper out where we suggest a scale to communicate the size of global catastrophic and existential risks.
For impact risks, we have the Torino scale of asteroid danger which has five color-coded levels. For hurricanes we have the Saffir-Simpson scale of five categories. Here we present similar scale for communicating the size of the global catastrophic and existential risks.
Typically, some vague claims about probability are used as a communication tool for existential risks, for example, some may say, “There is a 10 per cent chance that humanity will be exterminated by nuclear war”. But the probability of the most serious global risks is difficult to measure, and the probability estimate doesn’t take into account other aspects of risks, for example, preventability, uncertainty, timing, relation to other risks etc. As a result, claims about probability could be misleading or produce reasonable skepticism.
To escape these difficulties, we suggested creating a scale to communicate existential risks, similar to the Torino scale of asteroid danger.
In our scale, there are six color codes, from white to purple. If hard probabilities are known, the color corresponds to probability intervals for a fixed timeframe of 100 years, which helps to solve uncertainty and timing.
However, for most serious risks, like AI, their probabilities are not known, but the required levels of prevention action are known. For these cases, the scale communicates the risk’s size through the required level of prevention action. In some sense, it is similar to Updateless Decision Theory, where an event’s significance is measured, not by observable probabilities, but by the utility of corresponding actions. The system would work, because in many cases of x-risks, the required prevention actions are not very sensitive to the probability.
How should the scale be implemented in practice? If probabilities are not known, a group of experts should aggregate available information and communicate it to the public and policymakers, saying something like: "We think that AI is a red risk, a pandemic is a yellow risk and asteroid danger is a green risk.” It would help to bring some order to the public perception of each risk—where, currently, asteroid danger is clearly overestimated compared to the risks of AI risk—without making unsustainable claims about unmeasurable probabilities.
In the article we have already given some estimates for the most well-known existential risks, but clearly they are open to debate.
Here's the abstract:
Existential risks threaten the future of humanity, but they are difficult to measure. However, to communicate, prioritize and mitigate such risks it is important to estimate their relative significance. Risk probabilities are typically used, but for existential risks they are problematic due to ambiguity, and because quantitative probabilities do not represent some aspects of these risks. Thus, a standardized and easily comprehensible instrument is called for, to communicate dangers from various global catastrophic and existential risks. In this article, inspired by the Torino scale of asteroid danger, we suggest a color-coded scale to communicate the magnitude of global catastrophic and existential risks. The scale is based on the probability intervals of risks in the next century if they are available. The risks’ estimations could be adjusted based on their severities and other factors. The scale covers not only existential risks, but smaller size global catastrophic risks. It consists of six color levels, which correspond to previously suggested levels of prevention activity. We estimate artificial intelligence risks as “red”, while “orange” risks include nanotechnology, synthetic biology, full-scale nuclear war and a large global agricultural shortfall (caused by regional nuclear war, coincident extreme weather, etc.) The risks of natural pandemic, supervolcanic eruption and global warming are marked as “yellow” and the danger from asteroids is “green”.
The paper is published in Futures https://www.sciencedirect.com/science/article/pii/S001632871730112X
If you want to read the full paper, here's a link on preprint: https://philpapers.org/rec/TURGCA
Two main pictures from the paper: