Will AI Avoid Exploitation?
Adam Bales (Global Priorities Institute, University of Oxford)
GPI Working Paper No. 16-2023, published in Philosophical Studies
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: in exploring the argument, we gain insight into how to model advanced AI systems.
Other working papers
On the desire to make a difference – Hilary Greaves, William MacAskill, Andreas Mogensen and Teruji Thomas (Global Priorities Institute, University of Oxford)
True benevolence is, most fundamentally, a desire that the world be better. It is natural and common, however, to frame thinking about benevolence indirectly, in terms of a desire to make a difference to how good the world is. This would be an innocuous shift if desires to make a difference were extensionally equivalent to desires that the world be better. This paper shows that at least on some common ways of making a “desire to make a difference” precise, this extensional equivalence fails.
Misjudgment Exacerbates Collective Action Problems – Joshua Lewis (New York University) et al.
In collective action problems, suboptimal collective outcomes arise from each individual optimizing their own wellbeing. Past work assumes individuals do this because they care more about themselves than others. Yet, other factors could also contribute. We examine the role of empirical beliefs. Our results suggest people underestimate individual impact on collective problems. When collective action seems worthwhile, individual action often does not, even if the expected ratio of costs to benefits is the same. …
Altruism in governance: Insights from randomized training – Sultan Mehmood, (New Economic School), Shaheen Naseer (Lahore School of Economics) and Daniel L. Chen (Toulouse School of Economics)
Randomizing different schools of thought in training altruism finds that training junior deputy ministers in the utility of empathy renders at least a 0.4 standard deviation increase in altruism. Treated ministers increased their perspective-taking: blood donations doubled, but only when blood banks requested their exact blood type. Perspective-taking in strategic dilemmas improved. Field measures such as orphanage visits and volunteering in impoverished schools also increased, as did their test scores in teamwork assessments…