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This article examines some of the most relevant issues concerning P2P systems so as to take sides in today's strongly polarized debate. The idea is to integrate a context-based perspective with an ontological representation of informational norms; thanks to a procedural outlook which is presented in terms of burden of proof. More particularly, we examine three 'roads.' First, the topological approach to complex social networks allows us to comprehend the laws according to which information is distributed through P2P systems and how a 'short route' has joined, and sometimes replaced, the traditional 'long route' between creator, business, and the public. The second road is the context-based perspective elaborated by Helen Nissenbaum, and developed by Francis Grodzinsky and Herman Tavani: The goal is to determine the norms that govern such an informational flow as norms of appropriateness and distribution. The final road is the informational viewpoint on ethics proposed by Luciano Floridi with the idea that standard ethical theories cannot easily be adapted to deal with the new informational issues emerging with digital technology. While empirical evidence on the impact of P2P systems is still quite controversial, it is crucial to determine on whom the burden of proof falls in a given context, on censors or advocates, by singling out both the default norms and exceptions in the use and development of P2P software. [ABSTRACT FROM AUTHOR]

Background: This paper aims to move the debate forward regarding the potential for artificial intelligence (AI) and autonomous robotic surgery with a particular focus on ethics, regulation and legal aspects (such as civil law, international law, tort law, liability, medical malpractice, privacy and product/device legislation, among other aspects)., Methods: We conducted an intensive literature search on current or emerging AI and autonomous technologies (eg, vehicles), military and medical technologies (eg, surgical robots), relevant frameworks and standards, cyber security/safety- and legal-systems worldwide. We provide a discussion on unique challenges for robotic surgery faced by proposals made for AI more generally (eg, Explainable AI) and machine learning more specifically (eg, black box), as well as recommendations for developing and improving relevant frameworks or standards., Conclusion: We classify responsibility into the following: (1) Accountability; (2) Liability; and (3) Culpability. All three aspects were addressed when discussing responsibility for AI and autonomous surgical robots, be these civil or military patients (however, these aspects may require revision in cases where robots become citizens). The component which produces the least clarity is Culpability, since it is unthinkable in the current state of technology. We envision that in the near future a surgical robot can learn and perform routine operative tasks that can then be supervised by a human surgeon. This represents a surgical parallel to autonomously driven vehicles. Here a human remains in the 'driving seat' as a 'doctor-in-the-loop' thereby safeguarding patients undergoing operations that are supported by surgical machines with autonomous capabilities., (© 2018 John Wiley & Sons, Ltd.)