Evolutionary debunking and value alignment
Michael T. Dale (Hampden-Sydney College) and Bradford Saad (Global Priorities Institute, University of Oxford)
GPI Working Paper No. 11-2024
This paper examines the bearing of evolutionary debunking arguments—which use the evolutionary origins of values to challenge their epistemic credentials—on the alignment problem, i.e. the problem of ensuring that highly capable AI systems are properly aligned with values. Since evolutionary debunking arguments are among the best empirically-motivated arguments that recommend changes in values, it is unsurprising that they are relevant to the alignment problem. However, how evolutionary debunking arguments bear on alignment is a neglected issue. This paper sheds light on that issue by showing how evolutionary debunking arguments: (1) raise foundational challenges to posing the alignment problem, (2) yield normative constraints on solving it, and (3) generate stumbling blocks for implementing solutions. After mapping some general features of this philosophical terrain, we illustrate how evolutionary debunking arguments interact with some of the main technical approaches to alignment. To conclude, we motivate a parliamentary approach to alignment and suggest some ways of developing and testing it.
Other working papers
Consequentialism, Cluelessness, Clumsiness, and Counterfactuals – Alan Hájek (Australian National University)
According to a standard statement of objective consequentialism, a morally right action is one that has the best consequences. More generally, given a choice between two actions, one is morally better than the other just in case the consequences of the former action are better than those of the latter. (These are not just the immediate consequences of the actions, but the long-term consequences, perhaps until the end of history.) This account glides easily off the tongue—so easily that…
Economic growth under transformative AI – Philip Trammell (Global Priorities Institute, Oxford University) and Anton Korinek (University of Virginia)
Industrialized countries have long seen relatively stable growth in output per capita and a stable labor share. AI may be transformative, in the sense that it may break one or both of these stylized facts. This review outlines the ways this may happen by placing several strands of the literature on AI and growth within a common framework. We first evaluate models in which AI increases output production, for example via increases in capital’s substitutability for labor…
On two arguments for Fanaticism – Jeffrey Sanford Russell (University of Southern California)
Should we make significant sacrifices to ever-so-slightly lower the chance of extremely bad outcomes, or to ever-so-slightly raise the chance of extremely good outcomes? Fanaticism says yes: for every bad outcome, there is a tiny chance of of extreme disaster that is even worse, and for every good outcome, there is a tiny chance of an enormous good that is even better.