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)
GPI Working Paper No. 30-2024
A principal wants to deploy an artificial intelligence (AI) system to perform some task. But the AI may be misaligned and pursue a conflicting objective. The principal cannot restrict its options or deliver punishments. Instead, the principal can (i) simulate the task in a testing environment and (ii) impose imperfect recall on the AI, obscuring whether the task being performed is real or part of a test. By committing to a testing mechanism, the principal can screen the misaligned AI during testing and discipline its behaviour in deployment. Increasing the number of tests allows the principal to screen or discipline arbitrarily well. The screening effect is preserved even if the principal cannot commit or if the agent observes information partially revealing the nature of the task. Without commitment, imperfect recall is necessary for testing to be helpful.
Other working papers
AI takeover and human disempowerment – Adam Bales (Global Priorities Institute, University of Oxford)
Some take seriously the possibility of AI takeover, where AI systems seize power in a way that leads to human disempowerment. Assessing the likelihood of takeover requires answering empirical questions about the future of AI technologies and the context in which AI will operate. In many cases, philosophers are poorly placed to answer these questions. However, some prior questions are more amenable to philosophical techniques. What does it mean to speak of AI empowerment and human disempowerment? …
Aggregating Small Risks of Serious Harms – Tomi Francis (Global Priorities Institute, University of Oxford)
According to Partial Aggregation, a serious harm can be outweighed by a large number of somewhat less serious harms, but can outweigh any number of trivial harms. In this paper, I address the question of how we should extend Partial Aggregation to cases of risk, and especially to cases involving small risks of serious harms. I argue that, contrary to the most popular versions of the ex ante and ex post views, we should sometimes prevent a small risk that a large number of people will suffer serious harms rather than prevent…
Dynamic public good provision under time preference heterogeneity – Philip Trammell (Global Priorities Institute and Department of Economics, University of Oxford)
I explore the implications of time preference heterogeneity for the private funding of public goods. The assumption that players use a common discount rate is knife-edge: relaxing it yields substantially different equilibria, for two reasons. First, time preference heterogeneity motivates intertemporal polarization, analogous to the polarization seen in a static public good game. In the simplest settings, more patient players spend nothing early in time and less patient players spending nothing later. Second…