Jose Moral

Actualmente, la discusión sobre los problemas éticos de las nuevas tecnologías suele enfocarse en los dilemas que, de manera concreta y generalmente aislada, supone el uso de unas y otras tecnologías; frente a ello, lo que se propone en este artículo es considerar la problematicidad de nuestro entramado tecnológico, dentro de lo que podemos denominar “nuestra tecnosfera”, para, de ese modo, discutir nuestro presente como un horizonte problemático, en que se redefine el sentido ético de la realidad antropológica. De este modo, y contando como referente teórico la reflexión sobre la técnica de Zubiri y de Ellacuría, proponemos la idea de “hacerse verdaderamente cargo del mundo” como medio para repensar éticamente las nuevas tecnologías (nuestras).

Explainable artificial intelligence has become a vitally important research field aiming, among other tasks, to justify predictions made by intelligent classifiers automatically learned from data. Importantly, efficiency of automated explanations may be undermined if the end user does not have sufficient domain knowledge or lacks information about the data used for training. To address the issue of effective explanation communication, we propose a novel information-seeking explanatory dialogue game following the most recent requirements to automatically generated explanations. Further, we generalise our dialogue model in form of an explanatory dialogue grammar which makes it applicable to interpretable rule-based classifiers that are enhanced with the capability to provide textual explanations. Finally, we carry out an exploratory user study to validate the corresponding dialogue protocol and analyse the experimental results using insights from process mining and argument analytics. A high number of requests for alternative explanations testifies the need for ensuring diversity in the context of automated explanations.

Scientific articles are one of the most important types of output of a researcher. In that sense, bibliometrics is an essential tool for assessing and analyzing academic research output contributing to the progress of science in many different ways. It provides objective criteria to assess research developed by researchers, being increasingly valued as a tool for measuring scholarly quality and productivity. Science mapping is a bibliometric tool to analyze and mine scientific output. The aim of this chapter is to present a thorough review of science mapping software tools, showing strengths and limitations. Six software tools that meet the criteria of being free, full, and allowing the whole analysis to be performed are analyzed:BibExcelCiteSpace IICitNetExplorerSciMATSci2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${}^{2}$$\end{document} ToolVOSviewer.This analysis describes aspects related to data processing, analysis options, and visualization. The particular properties of each tool that allows us to analyze the science are presented, the choice of a particular tool one depends on the type of actor to be analyzed and the output expected.