Strong longtermism and the challenge from anti-aggregative moral views
Karri Heikkinen (University College London)
GPI Working Paper No. 5 - 2022
Greaves and MacAskill (2019) argue for strong longtermism, according to which, in a wide class of decision situations, the option that is ex ante best, and the one we ex ante ought to choose, is the option that makes the very long-run future go best. One important aspect of their argument is the claim that strong longtermism is compatible with a wide range of ethical assumptions, including plausible non-consequentialist views. In this essay, I challenge this claim. I argue that strong longtermism is incompatible with a range of non-aggregative and partially aggregative moral views. Furthermore, I argue that the conflict between these views and strong longtermism is so deep that those in favour of strong longtermism are better off arguing against them, rather than trying to modify their own view. The upshot of this discussion is that strong longtermism is not as robust to plausible variations in underlying ethical assumptions as Greaves and MacAskill claim. In particular, the stand we take on interpersonal aggregation has important implications on whether making the future go as well as possible should be a global priority.
Other working papers
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Some believe that the current era is uniquely important with respect to how well the rest of human history goes. Following Parfit, call this the Hinge of History Hypothesis. Recently, MacAskill has argued that our era is actually very unlikely to be especially influential in the way asserted by the Hinge of History Hypothesis. I respond to MacAskill, pointing to important unresolved ambiguities in his proposed definition of what it means for a time to be influential and criticizing the two arguments…
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Imperfect Recall and AI Delegation – Eric Olav Chen (Global Priorities Institute, University of Oxford), Alexis Ghersengorin (Global Priorities Institute, University of Oxford) and Sami Petersen (Department of Economics, University of Oxford)
A principal wants to deploy an artificial intelligence (AI) system to perform some task. But the AI may be misaligned and aim to pursue a conflicting objective. The principal cannot restrict its options or deliver punishments. Instead, the principal is endowed with the ability to impose imperfect recall on the agent. The principal can then simulate the task and obscure whether it is real or part of a test. This allows the principal to screen misaligned AIs during testing and discipline their behaviour in deployment. By increasing the…