The rise of artificial intelligence (A.I.) based systems has the potential to benefit adopters and society as a whole. However, these systems may also enclose potential conflicts and unintended consequences. Notably, people will only adopt an A.I. system if it confers them an advantage, at which point non-adopters might push for a strong regulation if that advantage for adopters is at a cost for them. Here we propose a stochastic game theoretical model for these conflicts. We frame our results under the current discussion on ethical A.I. and the conflict between individual and societ al gains, the societ al value alignment problem. We test the arising equilibria in the adoption of A.I. technology under different norms followed by artificial agents, their ensuing benefits, and the emergent levels of wealth inequality. We show that without any regulation, purely selfish A.I. systems will have the strongest advantage, even when a utilitarian A.I. provides a more significant benefit for the individual and the society. Nevertheless, we show that it is possible to develop human conscious A.I. systems that reach an equilibrium where the gains for the adopters are not at a cost for non-adopters while increasing the overall fitness and lowering inequality. However, as shown, a self-organized adoption of such policies would require external regulation.
One major problem for the introduction of safe A.I. systems is the so called value alignment problem @cite_37 : How can A.I. systems ensure that their behaviour aligns to the values of their owners? Even though it is not yet a solved problem, for this paper, we will assume that an A.I. system can accurately estimate the goals of each individual with whom it interacts. With this assumption, we are able to study the problems that emerge at the societ al level even after having the individual value alignment solved.
@cite_37
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The principal-agent problem concerns delegation in the absence of trust. Given a principal and an agent with different value structures, the principal wants to motivate the agent to address the principal’s aims by providing appropriate incentives. We address this problem in the context of a real-world complication, where the principal and agent lack a common problem frame. This context is especially relevant when the principal is a user, and the agent is a technological artifact with a limited repertoire of percepts and actions. We identify necessary conditions for establishing trust between such disparate actors, and we show, via a constructive proof, that it is always possible to create these necessary conditions. We conclude with several distinctions that let the principal rank the expected quality of agent behavior.