scholarly journals A New Perspective on Robot Ethics through Investigating Human–Robot Interactions with Older Adults

2021 ◽  
Vol 11 (21) ◽  
pp. 10136
Author(s):  
Anouk van Maris ◽  
Nancy Zook ◽  
Sanja Dogramadzi ◽  
Matthew Studley ◽  
Alan Winfield ◽  
...  

This work explored the use of human–robot interaction research to investigate robot ethics. A longitudinal human–robot interaction study was conducted with self-reported healthy older adults to determine whether expression of artificial emotions by a social robot could result in emotional deception and emotional attachment. The findings from this study have highlighted that currently there appears to be no adequate tools, or the means, to determine the ethical impact and concerns ensuing from long-term interactions between social robots and older adults. This raises the question whether we should continue the fundamental development of social robots if we cannot determine their potential negative impact and whether we should shift our focus to the development of human–robot interaction assessment tools that provide more objective measures of ethical impact.

Author(s):  
Ruth Stock-Homburg

AbstractKnowledge production within the interdisciplinary field of human–robot interaction (HRI) with social robots has accelerated, despite the continued fragmentation of the research domain. Together, these features make it hard to remain at the forefront of research or assess the collective evidence pertaining to specific areas, such as the role of emotions in HRI. This systematic review of state-of-the-art research into humans’ recognition and responses to artificial emotions of social robots during HRI encompasses the years 2000–2020. In accordance with a stimulus–organism–response framework, the review advances robotic psychology by revealing current knowledge about (1) the generation of artificial robotic emotions (stimulus), (2) human recognition of robotic artificial emotions (organism), and (3) human responses to robotic emotions (response), as well as (4) other contingencies that affect emotions as moderators.


AI & Society ◽  
2021 ◽  
Author(s):  
Dafna Burema

AbstractThis paper argues that there is a need to critically assess bias in the representations of older adults in the field of Human–Robot Interaction. This need stems from the recognition that technology development is a socially constructed process that has the potential to reinforce problematic understandings of older adults. Based on a qualitative content analysis of 96 academic publications, this paper indicates that older adults are represented as; frail by default, independent by effort; silent and technologically illiterate; burdensome; and problematic for society. Within these documents, few counternarratives are present that do not take such essentialist representations. In these texts, the goal of social robots in elder care is to “enable” older adults to “better” themselves. The older body is seen as “fixable” with social robots, reinforcing an ageist and neoliberal narrative: older adults are reduced to potential care receivers in ways that shift care responsibilities away from the welfare state onto the individual.


Author(s):  
Vignesh Prasad ◽  
Ruth Stock-Homburg ◽  
Jan Peters

AbstractFor some years now, the use of social, anthropomorphic robots in various situations has been on the rise. These are robots developed to interact with humans and are equipped with corresponding extremities. They already support human users in various industries, such as retail, gastronomy, hotels, education and healthcare. During such Human-Robot Interaction (HRI) scenarios, physical touch plays a central role in the various applications of social robots as interactive non-verbal behaviour is a key factor in making the interaction more natural. Shaking hands is a simple, natural interaction used commonly in many social contexts and is seen as a symbol of greeting, farewell and congratulations. In this paper, we take a look at the existing state of Human-Robot Handshaking research, categorise the works based on their focus areas, draw out the major findings of these areas while analysing their pitfalls. We mainly see that some form of synchronisation exists during the different phases of the interaction. In addition to this, we also find that additional factors like gaze, voice facial expressions etc. can affect the perception of a robotic handshake and that internal factors like personality and mood can affect the way in which handshaking behaviours are executed by humans. Based on the findings and insights, we finally discuss possible ways forward for research on such physically interactive behaviours.


Author(s):  
Matthias Scheutz ◽  
Paul Schermerhorn

Effective decision-making under real-world conditions can be very difficult as purely rational methods of decision-making are often not feasible or applicable. Psychologists have long hypothesized that humans are able to cope with time and resource limitations by employing affective evaluations rather than rational ones. In this chapter, we present the distributed integrated affect cognition and reflection architecture DIARC for social robots intended for natural human-robot interaction and demonstrate the utility of its human-inspired affect mechanisms for the selection of tasks and goals. Specifically, we show that DIARC incorporates affect mechanisms throughout the architecture, which are based on “evaluation signals” generated in each architectural component to obtain quick and efficient estimates of the state of the component, and illustrate the operation and utility of these mechanisms with examples from human-robot interaction experiments.


2020 ◽  
Vol 32 (1) ◽  
pp. 7-7
Author(s):  
Masahiro Shiomi ◽  
Hidenobu Sumioka ◽  
Hiroshi Ishiguro

As social robot research is advancing, the interaction distance between people and robots is decreasing. Indeed, although we were once required to maintain a certain physical distance from traditional industrial robots for safety, we can now interact with social robots in such a close distance that we can touch them. The physical existence of social robots will be essential to realize natural and acceptable interactions with people in daily environments. Because social robots function in our daily environments, we must design scenarios where robots interact closely with humans by considering various viewpoints. Interactions that involve touching robots influence the changes in the behavior of a person strongly. Therefore, robotics researchers and developers need to design such scenarios carefully. Based on these considerations, this special issue focuses on close human-robot interactions. This special issue on “Human-Robot Interaction in Close Distance” includes a review paper and 11 other interesting papers covering various topics such as social touch interactions, non-verbal behavior design for touch interactions, child-robot interactions including physical contact, conversations with physical interactions, motion copying systems, and mobile human-robot interactions. We thank all the authors and reviewers of the papers and hope this special issue will help readers better understand human-robot interaction in close distance.


Author(s):  
Aike C. Horstmann ◽  
Nicole C. Krämer

AbstractSince social robots are rapidly advancing and thus increasingly entering people’s everyday environments, interactions with robots also progress. For these interactions to be designed and executed successfully, this study considers insights of attribution theory to explore the circumstances under which people attribute responsibility for the robot’s actions to the robot. In an experimental online study with a 2 × 2 × 2 between-subjects design (N = 394), people read a vignette describing the social robot Pepper either as an assistant or a competitor and its feedback, which was either positive or negative during a subsequently executed quiz, to be generated autonomously by the robot or to be pre-programmed by programmers. Results showed that feedback believed to be autonomous leads to more attributed agency, responsibility, and competence to the robot than feedback believed to be pre-programmed. Moreover, the more agency is ascribed to the robot, the better the evaluation of its sociability and the interaction with it. However, only the valence of the feedback affects the evaluation of the robot’s sociability and the interaction with it directly, which points to the occurrence of a fundamental attribution error.


2018 ◽  
Author(s):  
Anna Henschel ◽  
Emily S. Cross

A wealth of social psychology studies suggest that moving in synchrony with another person positively influences likeability and prosocial behavior towards that individual. Recently, human-robot interaction (HRI) researchers have started to develop real-time, adaptive synchronous movement algorithms for social robots. However, little is known how socially beneficial synchronous movements with a robot actually are. We predicted that moving in synchrony with a robot would improve its likeability and participants’ social motivation towards it, as measured by the number of questions asked during a free interaction period. Using a between-subjects design, we implemented the synchrony manipulation via a drawing task. Contrary to predictions, we found no evidence that participants who moved in synchrony with the robot rated it as more likeable or asked it more questions. By including validated behavioral and neural measures, future studies can generate a better and more objective estimation of synchrony’s effects on rapport with social robots.


2019 ◽  
Vol 374 (1771) ◽  
pp. 20180025 ◽  
Author(s):  
Tony J. Prescott ◽  
Daniel Camilleri ◽  
Uriel Martinez-Hernandez ◽  
Andreas Damianou ◽  
Neil D. Lawrence

From neuroscience, brain imaging and the psychology of memory, we are beginning to assemble an integrated theory of the brain subsystems and pathways that allow the compression, storage and reconstruction of memories for past events and their use in contextualizing the present and reasoning about the future—mental time travel (MTT). Using computational models, embedded in humanoid robots, we are seeking to test the sufficiency of this theoretical account and to evaluate the usefulness of brain-inspired memory systems for social robots. In this contribution, we describe the use of machine learning techniques—Gaussian process latent variable models—to build a multimodal memory system for the iCub humanoid robot and summarize results of the deployment of this system for human–robot interaction. We also outline the further steps required to create a more complete robotic implementation of human-like autobiographical memory and MTT. We propose that generative memory models, such as those that form the core of our robot memory system, can provide a solution to the symbol grounding problem in embodied artificial intelligence. This article is part of the theme issue ‘From social brains to social robots: applying neurocognitive insights to human–robot interaction’.


Sign in / Sign up

Export Citation Format

Share Document