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.
However, some argue that having an ethical framework or even A.I. systems that pass the comparative moral Turing test is not enough @cite_6 . Roman Yampolskiy defends that it is insufficient to have a human-like morality on A.I. systems with super-human intelligence. On such agents, small moral mistakes, common in humans, could lead to the extinction of humanity. Furthermore, a moral A.I. system with super-human intelligence will be able to recursively self-improve, with no provided guarantees that the resulting improvements remain moral. Instead of an ethical approach, Yampolskiy proposes a safety engineering approach, able to provide proofs that developed A.I. systems will remain safe, even under recursive self-improvement @cite_23 . Yampolskiy also proposes A.I. confinement as a possible approach while no safety guarantees are in place @cite_12 @cite_8 . This approach would consist in ensuring that an A.I. system could help humanity while having no ability to negatively influence the world around it. This idea of A.I. confinement had been first presented in @cite_32 , and discussed by Bostrom @cite_31 and Chalmers @cite_7 . This is, however, more of a preventive measure than a perfect solution, as limiting the negative A.I. influence will also limit the possible positive influence.
@cite_7 @cite_8 @cite_32 @cite_6 @cite_23 @cite_31 @cite_12
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What happens when machines become more intelligent than humans? One view is that this event will be followed by an explosion to ever-greater levels of intelligence, as each generation of machines creates more intelligent machines in turn. This intelligence explosion is now often known as the “singularity”. The basic argument here was set out by the statistician I. J. Good in his 1965 article “Speculations Concerning the First Ultraintelligent Machine”: Part 1 The foundations of foresight: engines of construction the principles of change predicting and projecting. Part 2 Profiles of the possible: engines of abundance thinking machines the world beyond Earth engines of healing long life in an open world a door to the future the limits to growth. Part 3 Dangers and hopes: engines of destruction strategies and survival finding the facts the network of knowledge world enough and time. Machine ethics and robot rights are quickly becoming hot topics in artificial intelligence robotics communities. We will argue that the attempts to allow machines to make ethical decisions or to have rights are misguided. Instead we propose a new science of safety engineering for intelligent artificial agents. In particular we issue a challenge to the scientific community to develop intelligent systems capable of proving that they are in fact safe even under recursive self-improvement. Machine ethics and robot rights are quickly becoming hot topics in artificial intelligence and robotics communities. We will argue that attempts to attribute moral agency and assign rights to all intelligent machines are misguided, whether applied to infrahuman or superhuman AIs, as are proposals to limit the negative effects of AIs by constraining their behavior. As an alternative, we propose a new science of safety engineering for intelligent artificial agents based on maximizing for what humans value. In particular, we challenge the scientific community to develop intelligent systems that have human-friendly values that they provably retain, even under recursive self-improvement. With almost daily improvements in capabilities of artificial intelligence it is more important than ever to develop safety software for use by the AI research community. Building on our previous work on AI Containment Problem we propose a number of guidelines which should help AI safety researchers to develop reliable sandboxing software for intelligent programs of all levels. Such safety container software will make it possible to study and analyze intelligent artificial agent while maintaining certain level of safety against information leakage, social engineering attacks and cyberattacks from within the container.