Against Willing Servitude: Autonomy in the Ethics of Advanced Artificial Intelligence
Adam Bales (Global Priorities Institute, University of Oxford)
GPI Working Paper No. 23-2024
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.
Other working papers
Three mistakes in the moral mathematics of existential risk – David Thorstad (Global Priorities Institute, University of Oxford)
Longtermists have recently argued that it is overwhelmingly important to do what we can to mitigate existential risks to humanity. I consider three mistakes that are often made in calculating the value of existential risk mitigation: focusing on cumulative risk rather than period risk; ignoring background risk; and neglecting population dynamics. I show how correcting these mistakes pushes the value of existential risk mitigation substantially below leading estimates, potentially low enough to…
In Defence of Moderation – Jacob Barrett (Vanderbilt University)
A decision theory is fanatical if it says that, for any sure thing of getting some finite amount of value, it would always be better to almost certainly get nothing while having some tiny probability (no matter how small) of getting sufficiently more finite value. Fanaticism is extremely counterintuitive; common sense requires a more moderate view. However, a recent slew of arguments purport to vindicate it, claiming that moderate alternatives to fanaticism are sometimes similarly counterintuitive, face a powerful continuum argument…
Estimating long-term treatment effects without long-term outcome data – David Rhys Bernard (Rethink Priorities), Jojo Lee and Victor Yaneng Wang (Global Priorities Institute, University of Oxford)
The surrogate index method allows policymakers to estimate long-run treatment effects before long-run outcomes are observable. We meta-analyse this approach over nine long-run RCTs in development economics, comparing surrogate estimates to estimates from actual long-run RCT outcomes. We introduce the M-lasso algorithm for constructing the surrogate approach’s first-stage predictive model and compare its performance with other surrogate estimation methods. …