In recent years, autonomous systems have become an important research area and application domain, with a significant impact on modern society. Such systems are characterized by different levels of autonomy and complex communication infrastructures that allow for collective decision-making strategies. There exist several publications that tackle ethical aspects in such systems, but mostly from the perspective of a single agent. In this paper we go one step further and discuss these ethical challenges from the perspective of an aggregate of autonomous systems capable of collective decision-making. In particular, in this paper, we propose the Caesar approach through which we model the collective ethical decision-making process of a group of actors—agents and humans, as well as define the building blocks for the agents participating in such a process, namely Caesar agents. Factors such as trust, security, safety, and privacy, which affect the degree to which a collective decision is ethical, are explicitly captured in Caesar . Finally, we argue that modeling the collective decision-making in Caesar provides support for accountability.

Industrial control systems control and supervise our most important and critical infrastructures, such as power utilities, clean water plants and nuclear plants, as well as the manufacturing industries at the base of our economy. These systems are currently undergoing a transformation driven by the Industry 4.0 evolution, characterized by increased connectivity and flexibility. Consequently, the cybersecurity threat landscape for industrial control systems is evolving as well. Current strategies used for access control within industrial control systems are relatively rudimentary. It is evident that some of the emerging cybersecurity threats related to Industry 4.0 could be better mitigated using more fine-grained access control policies. In this article we discuss and describe a number of access control strategies that could be used within manufacturing systems. We evaluate the strategies in a simulation experiment, using a number of attack-scenarios. Moreover, a method is outlined for automatic policy-generation based on engineering-data, which is aligned with one of the best performing strategies.