Longtermism, aggregation, and catastrophic risk
Emma J. Curran (University of Cambridge)
GPI Working Paper No. 18-2022
Advocates of longtermism point out that interventions which focus on improving the prospects of people in the very far future will, in expectation, bring about a significant amount of good. Indeed, in expectation, such long-term interventions bring about far more good than their short-term counterparts. As such, longtermists claim we have compelling moral reason to prefer long-term interventions. In this paper, I show that longtermism is in conflict with plausible deontic scepticism about aggregation. I do so by demonstrating that, from both an ex-ante and ex-post perspective, longtermist interventions – and, in particular, those which aim to mitigate catastrophic risk – typically generate extremely weak claims of assistance from future people.
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…
Time discounting, consistency and special obligations: a defence of Robust Temporalism – Harry R. Lloyd (Yale University)
This paper defends the claim that mere temporal proximity always and without exception strengthens certain moral duties, including the duty to save – call this view Robust Temporalism. Although almost all other moral philosophers dismiss Robust Temporalism out of hand, I argue that it is prima facie intuitively plausible, and that it is analogous to a view about special obligations that many philosophers already accept…
Measuring AI-Driven Risk with Stock Prices – Susana Campos-Martins (Global Priorities Institute, University of Oxford)
We propose an empirical approach to identify and measure AI-driven shocks based on the co-movements of relevant financial asset prices. For that purpose, we first calculate the common volatility of the share prices of major US AI-relevant companies. Then we isolate the events that shake this industry only from those that shake all sectors of economic activity at the same time. For the sample analysed, AI shocks are identified when there are announcements about (mergers and) acquisitions in the AI industry, launching of…