The Hinge of History Hypothesis: Reply to MacAskill
Andreas Mogensen (Global Priorities Institute, University of Oxford)
GPI Working Paper No. 9 - 2022, published in Analysis
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 used to cast doubt on the claim that the current era is a uniquely important moment in human history.
Other working papers
How should risk and ambiguity affect our charitable giving? – Lara Buchak (Princeton University)
Suppose we want to do the most good we can with a particular sum of money, but we cannot be certain of the consequences of different ways of making use of it. This paper explores how our attitudes towards risk and ambiguity bear on what we should do. It shows that risk-avoidance and ambiguity-aversion can each provide good reason to divide our money between various charitable organizations rather than to give it all to the most promising one…
Against Willing Servitude: Autonomy in the Ethics of Advanced Artificial Intelligence – Adam Bales (Global Priorities Institute, University of Oxford)
Some people believe that advanced artificial intelligence systems (AIs) might, in the future, come to have moral status. Further, humans might be tempted to design such AIs that they serve us, carrying out tasks that make our lives better. This raises the question of whether designing AIs with moral status to be willing servants would problematically violate their autonomy. In this paper, I argue that it would in fact do so.
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…