Input to UN Interim Report on Governing AI for Humanity
This document was written by Bradford Saad, with assistance from Andreas Mogensen and Jeff Sebo. Jakob Lohmar provided valuable research assistance. The document benefited from discussion with or feedback from Frankie Andersen-Wood, Adam Bales, Ondrej Bajgar, Thomas Houlden, Jojo Lee, Toby Ord, Teruji Thomas, Elliott Thornley and Eva Vivalt.
Other papers
Staking our future: deontic long-termism and the non-identity problem – Andreas Mogensen (Global Priorities Institute, Oxford University)
Greaves and MacAskill argue for axiological longtermism, according to which, in a wide class of decision contexts, the option that is ex ante best is the option that corresponds to the best lottery over histories from t onwards, where t is some date far in the future. They suggest that a stakes-sensitivity argument…
In search of a biological crux for AI consciousness – Bradford Saad (Global Priorities Institute, University of Oxford)
Whether AI systems could be conscious is often thought to turn on whether consciousness is closely linked to biology. The rough thought is that if consciousness is closely linked to biology, then AI consciousness is impossible, and if consciousness is not closely linked to biology, then AI consciousness is possible—or, at any rate, it’s more likely to be possible. A clearer specification of the kind of link between consciousness and biology that is crucial for the possibility of AI consciousness would help organize inquiry into…
Dispelling the Anthropic Shadow – Teruji Thomas (Global Priorities Institute, University of Oxford)
There are some possible events that we could not possibly discover in our past. We could not discover an omnicidal catastrophe, an event so destructive that it permanently wiped out life on Earth. Had such a catastrophe occurred, we wouldn’t be here to find out. This space of unobservable histories has been called the anthropic shadow. Several authors claim that the anthropic shadow leads to an ‘observation selection bias’, analogous to survivorship bias, when we use the historical record to estimate catastrophic risks. …