Should we welcome robot teachers?

Abstract Current uses of robots in classrooms are reviewed and used to characterise four scenarios: (s1) Robot as Classroom Teacher; (s2) Robot as Companion and Peer; (s3) Robot as Care-eliciting Companion; and (s4) Telepresence Robot Teacher. The main ethical concerns associated with robot teachers are identified as: privacy; attachment, deception, and loss of human contact; and control and accountability. These are discussed in terms of the four identified scenarios. It is argued that classroom robots are likely to impact children’s’ privacy, especially when they masquerade as their friends and companions, when sensors are used to measure children’s responses, and when records are kept. Social robots designed to appear as if they understand and care for humans necessarily involve some deception (itself a complex notion), and could increase the risk of reduced human contact. Children could form attachments to robot companions (s2 and s3), or robot teachers (s1) and this could have a deleterious effect on their social development. There are also concerns about the ability, and use of robots to control or make decisions about children’s behaviour in the classroom. It is concluded that there are good reasons not to welcome fully fledged robot teachers (s1), and that robot companions (s2 and 3) should be given a cautious welcome at best. The limited circumstances in which robots could be used in the classroom to improve the human condition by offering otherwise unavailable educational experiences are discussed

Robot tutors:Welcome or ethically questionable?

Robot tutors provide new opportunities for education. However, they also introduce moral challenges. This study reports a systematic literature re-view (N = 256) aimed at identifying the moral considerations related to ro-bots in education. While our findings suggest that robot tutors hold great potential for improving education, there are multiple values of both (special needs) children and teachers that are impacted (positively and negatively) by its introduction. Positive values related to robot tutors are: psychological welfare and happiness, efficiency, freedom from bias and usability. However, there are also concerns that robot tutors may negatively impact these same values. Other concerns relate to the values of friendship and attachment, human contact, deception and trust, privacy, security, safety and accountability. All these values relate to children and teachers. The moral values of other stakeholder groups, such as parents, are overlooked in the existing literature. The results suggest that, while there is a potential for ap-plying robot tutors in a morally justified way, there are imported stake-holder groups that need to be consulted to also take their moral values into consideration by implementing tutor robots in an educational setting. (from Narcis.nl

Årsstatistikk fra legevakt 2018

Dette er ellevte rapport om aktiviteten på legevakt i Norge. Tidligere rapporter omfatter årene fra 2006 til 2017, mens denne inneholder data fra 2018. Det er behov for presise data fra aktiviteten på legevakt, og regningskortene er et godt grunnlag for å kunne gi en tilnærmet fullstendig rapportering. Mange data fra legevakt vil være relativt uendret fra det ene året til det andre. Det gjelder for eksempel relativ fordeling av kontakttyper i forhold til døgnets timer, geografi, kjønn og alder, samt diagnosefordelingen. Andre data er mer relevante for årlige analyser, slik at man kan følge utviklingen over tid. Det kan for eksempel gjelde fordelingen av ulike typer vaktleger, kontakttyper og takstbruk. Ved presentasjon av slike data har vi også tatt med tidligere år.publishedVersio

Power influences upon technology design for age-related cognitive decline using the VSD framework

Implicit in the value sensitive design (VSD) approach is a concern for understanding, and where possible, disrupting problematic power relationships. Yet an awareness of the issues and ethics of power relations is a pre-requisite for such a concern to bear fruit. This article provides some insight into the issues, and through a case study of technology design to support care arrangements for age-related cognitive decline, illustrates how finding a satisfactory resolution can be particularly troublesome

Combining Multiple Neural Networks to Predict Bronze Alloy Elemental Composition

The problem of predicting the composition of alloys measured by means of Laser-Induced Breakdown Spectroscopy (LIBS) analysis is frequently tackled in the literature. In this paper we propose the use of an ensemble of neu-ral networks to model the functional relationship between LIBS spectra and the corresponding composition of bronze alloys, expressed in terms of concentra-tions of the constituting elements. The networks are trained independently and their inputs are determined by different feature selection processes. Their out-puts are then combined by applying an averaging function. The results achieved allow to correctly predicting the composition of unknown bronze alloy samples