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.
The focus of most previous works was on considering high-level ethical principles for A.I. systems acting in a society. In most cases there was no claim or prediction about the potential adoption of A.I. systems or their acceptance by non-adopters and by society in general. Just a few works considered the development of computational models on the impact of A.I.. For instance, one study analyzed the amount of safety precautions companies would take considering that they are competing with others for the dominating A.I. @cite_46 .
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A large number of studies have been conducted during the last decade and a half attempting to identify those factors that contribute to information systems success. However, the dependent variable in these studies-I S success-has been an elusive one to define. Different researchers have addressed different aspects of success, making comparisons difficult and the prospect of building a cumulative tradition for I S research similarly elusive. To organize this diverse research, as well as to present a more integrated view of the concept of I S success, a comprehensive taxonomy is introduced. This taxonomy posits six major dimensions or categories of I S success-SYSTEM QUALITY, INFORMATION QUALITY, USE, USER SATISFACTION, INDIVIDUAL IMPACT, and ORGANIZATIONAL IMPACT. Using these dimensions, both conceptual and empirical studies are then reviewed a total of 180 articles are cited and organized according to the dimensions of the taxonomy. Finally, the many aspects of I S success are drawn together into a descriptive model and its implications for future I S research are discussed. Our modern era is characterized by a large-scale web of interconnected and interdependent economic and infrastructure systems, coupled with threats of terrorism. This paper demonstrates the value of introducing interdependency analysis into various phases of risk assessment and management through application of the Inoperability Input–Output Model (IIM). The IIM estimates the cascading inoperability and economic losses that result from interdependencies within large-scale economic and infrastructure systems. Based on real data and the Nobel Prize-winning W. Leontief economic model, the IIM is a computationally efficient, inexpensive, holistic method for estimating economic impacts. Three illustrative case studies are presented. The first and second illustrate how the supply- and demand-side IIM is used to calculate higher-order effects from attacks to vulnerabilities and implementation of risk management policies in large-scale economic systems. The final case study illustrates a more general use for interdependency analysis: to evaluate risk management options against multiple objectives. This study calculates a Pareto-optimal or efficient frontier of solutions by integrating a simplified model of the costs of recovery to the Power sector derived from open-source data with the IIM. Through these case studies, which use a database from the Bureau of Economic Analysis, we illustrate the value of interdependency analysis in the risk assessment and management process as an integral part of systems engineering. © 2005 Wiley Periodicals, Inc. Syst Eng 8: 323–341, 2005 In the field of infrastructures’ surveillance and protection, it is important to make decisions based on activities occurring in the environment and its local context and conditions. In this paper we use an active rule based event processing architecture in order to make sense of situations from the combination of different signals received by the rule engine. However obtaining some high level information automatically is not without risks, especially in sensitive environments, and detection mistakes can happen for various reasons: the signal’s source can be defective, whether it is human—miss-interpretation of the signal—or computed—material malfunction; the aggregation rules can be wrong syntaxically, for example when a rule will never be triggered or a situation never detected; the interpretation given to the combination of signals does not correspond to the reality on the field—because the knowledge of the rule designer is subjective or because the environment evolves over-time—the rules are therefore incorrect semantically. In this paper, a new approach is proposed to avoid the third kind of error sources. We present a hybrid machine learning technique adapted to the complexity of the rules’ representation, in order to create a system more conform to reality. The proposed approach uses a combination of an Association Rule Mining algorithm and Inductive Logic Programming for rule induction. Empirical studies on simulated datasets demonstrate how our method can contribute to sensible systems such as the security of a public or semi-public place. In this work we aim at extending the theoretical foundations of lifelong learning. Previous work analyzing this scenario is based on the assumption that learning tasks are sampled i.i.d. from a task environment or limited to strongly constrained data distributions. Instead, we study two scenarios when lifelong learning is possible, even though the observed tasks do not form an i.i.d. sample: first, when they are sampled from the same environment, but possibly with dependencies, and second, when the task environment is allowed to change over time in a consistent way. In the first case we prove a PAC-Bayesian theorem that can be seen as a direct generalization of the analogous previous result for the i.i.d. case. For the second scenario we propose to learn an inductive bias in form of a transfer procedure. We present a generalization bound and show on a toy example how it can be used to identify a beneficial transfer algorithm.