This paper provides a systematic literature review, analysis and discussion of methods that are proposed to practise ethics in research and innovation (R&I). Ethical considerations concerning the impacts of R&I are increasingly important, due to the quickening pace of technological innovation and the ubiquitous use of the outcomes of R&I processes in society. For this reason, several methods for practising ethics have been developed in different fields of R&I. The paper first of all presents a systematic search of academic sources that present and discuss such methods. Secondly, it provides a categorisation of these methods according to three main kinds: (1) ex ante methods, dealing with emerging technologies, (2) intra methods, dealing with technology design, and (3) ex post methods, dealing with ethical analysis of existing technologies. Thirdly, it discusses the methods by considering problems in the way they deal with the uncertainty of technological change, ethical technology design, the identification, analysis and resolving of ethical impacts of technologies and stakeholder participation. The results and discussion of our literature review are valuable for gaining an overview of the state of the art and serve as an outline of a future research agenda of methods for practising ethics in R&I.
The General Data Protection Regulation (GDPR) is the new European data protection law whose compliance affects organisations in several aspects related to the use of consent and personal data. With emerging research and innovation in data management solutions claiming assistance with various provisions of the GDPR, the task of comparing the degree and scope of such solutions is a challenge without a way to consolidate them. With GDPR as a linked data resource, it is possible to link together information and approaches addressing specific articles and thereby compare them. Organisations can take advantage of this by linking queries and results directly to the relevant text, thereby making it possible to record and measure their solutions for compliance towards specific obligations. GDPR text extensions (GDPRtEXT) uses the European Legislation Identifier (ELI) ontology published by the European Publications Office for exposing the GDPR as linked data. The dataset is published using DCAT and includes an online webpage with HTML id attributes for each article and its subpoints. A SKOS vocabulary is provided that links concepts with the relevant text in GDPR. To demonstrate how related legislations can be linked to highlight changes between them for reusing existing approaches, we provide a mapping from Data Protection Directive (DPD), which was the previous data protection law, to GDPR showing the nature of changes between the two legislations. We also discuss in brief the existing corpora of research that can benefit from the adoption of this resource.
Trust models in internet environments today are single-faceted. A single-faceted approach to modelling trust can suit some, or many, individuals but we believe that such a single-faceted approach cannot capture the wide and varied range of subjective views of trust found across a large and broad population. In response, we have designed, developed and evaluated a rich, semantic, human-centric model of trust that can handle the myriad of terms and intertwined meanings of trust. This multi-faceted model of trust can be Personalised on a per user basis and specialized on per domain basis. In this paper we briefly present an overview of this model and explain how it can be Personalised and specialized. However, the primary focus of this paper is on the experimental evaluation that has been carried out to evaluate the accuracy of recommendations based on this multi-faceted, Personalised model of trust for internet environments.
This paper proposes an open, extensible control plane for a global event service, based on semantically rich messages. This is based on the novel application of control plane separation and semantic-based matching to Content-Based Networks. Here we evaluate the performance issues involved in attempting to perform ontology-based reasoning for content-based routing. This provides us with the motivation to explore peer-clustering techniques to achieve efficient aggregation of semantic queries. The clustering of super-peers using decentralized policy engineering will deliver the incremental deployment of new peer-clustering strategies.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.