Autonomous Systems as Legal Agents: Directly by the Recognition of Personhood or Indirectly by the Alchemy of Algorithmic Entities

The clinical manifestations of platelet dense (δ) granule defects are easy bruising, as well as epistaxis and bleeding after delivery, tooth extractions and surgical procedures. The observed symptoms may be explained either by a decreased number of granules or by a defect in the uptake/release of granule contents. We have developed a method to study platelet dense granule storage and release. The uptake of the fluorescent marker, mepacrine, into the platelet dense granule was measured using flow cytometry. The platelet population was identified by the size and binding of a phycoerythrin-conjugated antibody against GPIb. Cells within the discrimination frame were analysed for green (mepacrine) fluorescence. Both resting platelets and platelets previously stimulated with collagen and the thrombin receptor agonist peptide SFLLRN was analysed for mepacrine uptake. By subtracting the value for mepacrine uptake after stimulation from the value for uptake without stimulation for each individual, the platelet dense granule release capacity could be estimated. Whole blood samples from 22 healthy individuals were analysed. Mepacrine incubation without previous stimulation gave mean fluorescence intensity (MFI) values of 83±6 (mean ± 1 SD, range 69–91). The difference in MFI between resting and stimulated platelets was 28±7 (range 17–40). Six members of a family, of whom one had a known δ-storage pool disease, were analysed. The two members (mother and son) who had prolonged bleeding times also had MFI values disparate from the normal population in this analysis. The values of one daughter with mild bleeding problems but a normal bleeding time were in the lower part of the reference interval

Philosophical Signposts for Artificial Moral Agent Frameworks

This article focuses on a particular issue under machine ethics—that is, the nature of Artificial Moral Agents. Machine ethics is a branch of artificial intelligence that looks into the moral status of artificial agents. Artificial moral agents, on the other hand, are artificial autonomous agents that possess moral value, as well as certain rights and responsibilities. This paper demonstrates that attempts to fully develop a theory that could possibly account for the nature of Artificial Moral Agents may consider certain philosophical ideas, like the standard characterizations of agency, rational agency, moral agency, and artificial agency. At the very least, the said philosophical concepts may be treated as signposts for further research on how to truly account for the nature of Artificial Moral Agents

Can AI weapons make ethical decisions?

The ability of machines to make truly independent and autonomous decisions is a goal of many, not least of military leaders who wish to take the human out of the loop as much as possible, claiming that autonomous military weaponry—most notably drones—can make decisions more quickly and with greater accuracy. However, there is no clear understanding of how autonomous weapons should be conceptualized and of the implications that their “autonomous” nature has on them as ethical agents. It will be argued that autonomous weapons are not full ethical agents due to the restrictions of their coding. However, the highly complex machine-learning nature gives the impression that they are making their own decisions and creates the illusion that their human operators are protected from the responsibility of the harm they cause. Therefore, it is important to distinguish between autonomous AI weapons and an AI with autonomy, a distinction that creates two different ethical problems for their use. For autonomous weapons, their limited agency combined with machine-learning means their human counterparts are still responsible for their actions while having no ability to control or intercede in the actual decisions made. If, on the other hand, an AI could reach the point of autonomy, the level of critical reflection would make its decisions unpredictable and dangerous in a weapon

Ethics of Artificial Intelligence

Artificial intelligence (AI) is a digital technology that will be of major importance for the development of humanity in the near future. AI has raised fundamental questions about what we should do with such systems, what the systems themselves should do, what risks they involve and how we can control these. - After the background to the field (1), this article introduces the main debates (2), first on ethical issues that arise with AI systems as objects, i.e. tools made and used by humans; here, the main sections are privacy (2.1), manipulation (2.2), opacity (2.3), bias (2.4), autonomy & responsibility (2.6) and the singularity (2.7). Then we look at AI systems as subjects, i.e. when ethics is for the AI systems themselves in machine ethics (2.8.) and artificial moral agency (2.9). Finally we look at future developments and the concept of AI (3). For each section within these themes, we provide a general explanation of the ethical issues, we outline existing positions and arguments, then we analyse how this plays out with current technologies and finally what policy conse-quences may be drawn

Ethics of Artificial Intelligence Demarcations

In this paper we present a set of key demarcations, particularly important when discussing ethical and societal issues of current AI research and applications. Properly distinguishing issues and concerns related to Artificial General Intelligence and weak AI, between symbolic and connectionist AI, AI methods, data and applications are prerequisites for an informed debate. Such demarcations would not only facilitate much-needed discussions on ethics on current AI technologies and research. In addition sufficiently establishing such demarcations would also enhance knowledge-sharing and support rigor in interdisciplinary research between technical and social sciences.Comment: Proceedings of the Norwegian AI Symposium 2019 (NAIS 2019), Trondheim, Norwa