Interpret the causal meaning of the ‘prior thought’–outcome probability association

Ascertain the causal interpretation of the observed association between AI researchers’ self‑reported amount of prior thought about the social impacts of smarter‑than‑human machines and their assigned probabilities to extremely good and extremely bad outcomes of High‑Level Machine Intelligence; specifically, determine whether greater prior thought improves prediction quality or whether selection effects (e.g., preexisting concern leading to more thought) drive the association.

Background

The survey compared outcome probability assignments across respondents who reported having thought “very little/a little” versus “a lot/a great deal” about the social impacts of smarter‑than‑human machines. Those who reported more prior thought assigned higher probabilities to extremely bad outcomes and lower probabilities to extremely good outcomes.

The authors explicitly note that the causal direction is unclear: increased thinking might improve predictive judgment, or concern might motivate greater thinking, implying a selection effect. Disentangling these interpretations remains unresolved.