The study analyses differences in the perceived age, cuteness and mental abilities of robots displayed as 3D models in a VR setting, manipulating anthropomorphic variables such as height, head dimension, and limb length. Participants (N = 122) were randomly assigned to one of four experimental conditions where they interacted with a robot with a different combination of physical dimensions. The interactions took place in a CAVE – an immersive VR room, which allowed participants to directly perceive the dimensions of the robot. In the CAVE we collected from participants impressions about the robot using Likert scales and numeric input. After the interaction, they had to fill in a post-test questionnaire measuring their tendency to anthropomorphism, literacy about Artificial Intelligence, and sensitivity to cute stimuli. Results show that changes in the physical appearance of the robots affect the perception of their age. A small robot with an increased head-to-body ratio is also perceived to be cuter, and the tendency towards anthropomorphism is related to higher perceived levels of experience in the robot. Results thus may be considered relevant when developing robots for specific functions, such as serving adults or children activities, studies, or work.

The evaluation of the use of Artificial Intelligence (AI) in legal decisions still has unsolved questions. These may refer to the perceived degree of seriousness of the possible errors committed, the distribution of responsibility among the different decision-makers (human or artificial), and the evaluation of the error concerning its possible benevolent or malevolent consequences on the person sanctioned. Above all, assessing the possible relationships between these variables appears relevant. To this aim, we conducted a study through an online questionnaire (N = 288) where participants had to consider different scenarios in which a decision-maker, human or artificial, made an error of judgement for offences punishable by a fine (Civil Law infringement) or years in prison (Criminal Law infringement). We found that humans who delegate AIs are blamed less than solo humans, although the effect of decision maker was subtle. In addition, people consider the error more serious if committed by a human being when a sentence for a crime of the penal code is mitigated, and for an AI when a penalty for an infringement of the civil code is aggravated. The mitigation of the evaluation of seriousness for joint AI-human judgement errors suggests the potential for strategic scapegoating of AIs.

Research on gender role stereotypes activated in interactions with humanoid robots has yet to produce conclusive knowledge. To analyse how much, and in which way, gender role stereotypes used in interactions with humans are also called into play in interactions with humanoid robots, a study was conducted with 240 participants. The study was an online survey in which a scale was used for determining the appropriateness for performing four stereotypically masculine and four feminine by humans and robots. Overall, eight humanoid robots – four judged feminine and four masculine – were considered. Results showed that gender role stereotypes are activated for both genders, but men most strongly activate those pertaining to male roles. These stereotypes are also adopted in reference to humanoid robots, though robots are generally considered less suitable for performing female roles. Furthermore, an increased degree of similarity of robots to humans has a positive effect in assessing the appropriateness to perform female roles only for female robots. The same does not happen with male robots. These results suggest that male and female robots are not categorised in the same way. Robots are essentially perceived as male entities, a particularly relevant hypothesis for the gender-sensitive design of humanoid robots.

Abstract During the COVID-19 pandemic, the use of social media offered a possible way to address the difficulties of social relationships for people with autism spectrum disorder (ASD), as well as a way to ease the problems of their caregivers. To gather information on the feasibility of this solution, we conducted an online questionnaire about the first lockdown period in Italy (March–May 2020) with 29 caregivers of ASD individuals. The questionnaire investigated their living conditions, the way time was spent during isolation, the availability of technological equipment, the perceived level of anxiety and the perceived utility of social media. The results showed that the difficulties of using social media had not been overcome, even at this time of greatest need. However, caregivers who take care of ASD people with high levels of anxiety perceived social media as more useful. This result invites further reflection on how to implement social media effectively for people with ASD.

The present study was aimed at determining the age and gender distribution of the humanoid robots in the ABOT dataset, and providing a systematic data-driven formalization of the process of age and gender categorization of humanoid robots. We involved 153 participants in an online study and asked them to rate the humanoid robots in the ABOT dataset in terms of perceived age, femininity, masculinity, and gender neutrality. Our analyses disclosed that most of the robots in the ABOT dataset were perceived as young adults, and the vast majority of them were attributed a neutral or masculine gender. By merging our data with the data in the ABOT dataset, we discovered that humanlikeness is crucial to elicit social categorization. Moreover, we found out that body manipulators (e.g., legs, torso) guide the attribution of masculinity, surface look features (e.g., eyelashes, apparel) the attribution of femininity, and that robots without facial features (e.g., head, eyes) are perceived as older. Finally, yet importantly, we unveiled that men tend to attribute lower age scores and higher femininity ratings to humanoid robots than women. Our work provides evidence of an existing underlying bias in the design of humanoid robots that needs to be addressed: the under-representation of feminine robots and lack of representation of androgynous ones. We make the results of this study publicly available to the HRI community by attaching the dataset we collected to the present paper and creating a dedicated website.

As collaborative robots and artificial intelligence (AI) systems are being deployed in ever-increasing contexts, we are more and more called upon to make judgements on their moral behaviour. Understanding the factors, affecting our ethical judgements involving these types of agents, would thus seem of the uttermost importance to allow for safer and well-regulated interactions between humans and machines. So far, however, this topic has been rarely investigated. We compared the perception of the seriousness of an action committed by either a person or a robot, causing harm to either some persons or some robots, and the attribution of the appropriated punishment for that action. The results showed a significant effect of the type of victim: the action was considered more a serious offence, and deemed worthy of more severe punishment, if the victims were humans than if they were robots. A significant agent-by-victim interaction was also found in the punishment judgements: for human victims, a human agent was punished more severely than a robot, while for robot victims, a robot agent was attributed a more severe punishment than a human one. The results are discussed in the light of the theories linking moral judgements to mind perception.

"BIT Special Issue ECCE2021 Editorial." Behaviour & Information Technology, 40(5), pp. 443–444

“Nomadic” patients are people who, mainly for work, move around the world very often. Such people have the right to an effective, safe, fast health-care assistance wherever they are. Some technological devices are currently being experimented, in order to let personal health-related information be portable and easily transferable. However, each of these devices follows an idiosyncratic concept. An explorative study has been carried out in order to understand which procedures, if any are currently being used in Italy to manage nomadic patients' data. The goal of this work was to outline a concept of information architecture for an efficient technological support device, essential to the transfer of medical data.

Multimedia systems are more and more used in distance learning. Since these systems are often structured as a hypertext, they pose additional problems to the user due to the complexity of navigable paths. In these cases the user has to learn both the structure of the hypertext and the provided contents. Three studies have been conducted to test the hypothesis that the level of usability of a system can affect the learning performance. The first two studies were aimed at evaluating the level of usability of a system developed as a multimedia distance learning course. An experiment was then conducted to compare the learning performance of students using this system to that of other students using different educational tools. Results lend a preliminary support to the hypothesis that a difficult to use hypermedia system can negatively affect learning performance.