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.
A comparison of a number of such ethical frameworks can be found on the paper ''An Ethical Framework for a Good AI Society: Opportunities, Risks, Principles, and Recommendations'' @cite_43 . That paper analyses principles proposed by 6 different entities, including the previously mentioned Asilomar AI principles @cite_41 , wielding 47 principles on total, and compares them to the existing 4 principles of bio-ethics (Non-maleficence; Justice; Beneficence; Autonomy) @cite_42 , finding a considerable overlap. They argue that for the bio-ethics principles to be applied to the field of A.I., a fifth principle is needed: Explicability. This principle incorporates both intelligibility and accountability. They go on to propose 20 action points, that is, recommendations for enabling a beneficial A.I. society.
@cite_41 @cite_43 @cite_42
2902634493 2022546212 73339090 2051031454
This article reports the findings of AI4People, an Atomium—EISMD initiative designed to lay the foundations for a “Good AI Society”. We introduce the core opportunities and risks of AI for society; present a synthesis of five ethical principles that should undergird its development and adoption; and offer 20 concrete recommendations—to assess, to develop, to incentivise, and to support good AI—which in some cases may be undertaken directly by national or supranational policy makers, while in others may be led by other stakeholders. If adopted, these recommendations would serve as a firm foundation for the establishment of a Good AI Society. 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. In this paper, we consider the influence of Gibson's affordance theory on the design of robotic agents. Affordance theory (and the ecological approach to agent design in gen- eral) has in many cases contributed to the development of successful robotic systems; we provide a brief survey of AI research in this area. However, there remain signifi- cant issues that complicate discussions on this topic, particularly in the exchange of ideas between researchers in artificial intelligence and ecological psychology. We identify some of these issues, specifically the lack of a generally accepted definition of "affordance" and fundamental differences in the current approaches taken in AI and ecological psychology. While we consider reconciliation between these fields to be possible and mutually beneficial, it will require some flexibility on the issue of direct perception. As artificial intelligence moves ever closer to the goal of producing fully autonomous agents, the question of how to design and implement an artificial moral agent (AMA) becomes increasingly pressing. Robots possessing autonomous capacities to do things that are useful to humans will also have the capacity to do things that are harmful to humans and other sentient beings. Theoretical challenges to developing artificial moral agents result both from controversies among ethicists about moral theory itself, and from computational limits to the implementation of such theories. In this paper the ethical disputes are surveyed, the possibility of a ‘moral Turing Test’ is considered and the computational difficulties accompanying the different types of approach are assessed. Human-like performance, which is prone to include immoral actions, may not be acceptable in machines, but moral perfection may be computationally unattainable. The risks posed by autonomous machines ignorantly or deliberately harming people ...