A Fission Problem for Person-Affecting Views

Elliott Thornley (Global Priorities Institute, University of Oxford)

GPI Working Paper No. 26-2024, forthcoming in Ergo

On person-affecting views in population ethics, the moral import of a person’s welfare depends on that person’s temporal or modal status. These views typically imply that – all else equal – we’re never required to create extra people, or to act
in ways that increase the probability of extra people coming into existence.

In this paper, I use Parfit-style fission cases to construct a dilemma for person-affecting views: either they forfeit their
seeming-advantages and face fission analogues of the problems faced by their rival impersonal views, or else they turn out to be not so person-affecting after all. In light of this dilemma, the attractions of person-affecting views largely evaporate. What
remains are the problems unique to them.

Other working papers

Will AI Avoid Exploitation? – Adam Bales (Global Priorities Institute, University of Oxford)

A simple argument suggests that we can fruitfully model advanced AI systems using expected utility theory. According to this argument, an agent will need to act as if maximising expected utility if they’re to avoid exploitation. Insofar as we should expect advanced AI to avoid exploitation, it follows that we should expected advanced AI to act as if maximising expected utility. I spell out this argument more carefully and demonstrate that it fails, but show that the manner of its failure is instructive…

Towards shutdownable agents via stochastic choice – Elliott Thornley (Global Priorities Institute, University of Oxford), Alexander Roman (New College of Florida), Christos Ziakas (Independent), Leyton Ho (Brown University), and Louis Thomson (University of Oxford)

Some worry that advanced artificial agents may resist being shut down. The Incomplete Preferences Proposal (IPP) is an idea for ensuring that doesn’t happen. A key part of the IPP is using a novel ‘Discounted REward for Same-Length Trajectories (DREST)’ reward function to train agents to (1) pursue goals effectively conditional on each trajectory-length (be ‘USEFUL’), and (2) choose stochastically between different trajectory-lengths (be ‘NEUTRAL’ about trajectory-lengths). In this paper, we propose evaluation metrics…

Evolutionary debunking and value alignment – Michael T. Dale (Hampden-Sydney College) and Bradford Saad (Global Priorities Institute, University of Oxford)

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…