Cuteness is a key factor in human-human interaction. Infant cuteness, described by Lorenz’s baby schema, is thought to promote infant survival by eliciting caregiving behaviors and positive emotions. Despite evidence that people prefer to interact with cute robots, the specific mechanisms through which cuteness influences human–robot interaction remain poorly understood. This research investigated the relationship between perceived robot cuteness, emotional responses, and caretaking tendencies. In two online surveys, participants rated all robots from the ABOT database. In survey 1, 156 participants evaluated robots’ perceived cuteness and the extent to which they evoked positive and negative emotions. In survey 2, a separate pool of 152 participants rated the caretaking tendencies elicited by the robots. Results showed that cuteness was positively correlated with positive emotions and caretaking tendencies, and negatively correlated with negative emotions. Path analysis revealed that the effect of cuteness on caretaking tendencies was partially mediated by participants’ emotional responses. Consistent with the baby-schema hypothesis, cuteness was negatively associated with perceived robot age and positively with the presence of facial features. Interestingly, participants' individual characteristics, most notably their tendency to anthropomorphize, influenced the responses. Our findings confirm the importance of robot cuteness for HRI and extend theories of baby schema to artificial agents. They also raise ethical considerations: while cuteness is a powerful design feature that facilitates affective bonding with robots, its persuasive potential should not be used lightly. Cute robots may foster care and trust, but these same mechanisms could be exploited to manipulate users in harmful ways.

The deployment of autonomous vehicles (AVs) on the roads opens moral and ethical issues related to their ‘behaviour’ in daily traffic situations. A debated question is how individuals perceive the choices taken by AVs in life-threatening scenarios, and whether the same or different moral standards are applied to humans’ and AVs’ decisions. In an online experiment, a questionnaire was submitted to an international sample (N = 353). The aim was to test whether the actions made by an AV or a human driver in realistic road-accident scenarios were judged according to a different perspective. We manipulated the decision maker, the decision, the number of pedestrians crossing the road and the number of occupants inside the vehicle, to assess the importance of utilitarian principles and the role of self-sacrifice in moral evaluations.The results highlight a preference for humans’ decisions over AVs’ ones and suggest a difference in the importance of utilitarian principles in the assessment of humans and AVs. The human self-sacrifice attitude is appreciated to a different degree, according to the type of individuals saved (pedestrians or occupants). Further investigations are needed to disentangle a human-bias from the effect of self-sacrifice on the moral evaluations of humans’ and AVs’ actions.

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.

This study focused on investigating the content, structure, and predictors of robots' stereotypes. We involved 120 participants in an online study and asked them to rate 80 robots on communion, agency, suitability for female and suitability for male tasks. In line with the stereotype content model, we discovered that robots' stereotypes are described by two dimensions, communion and agency, which combine to form univalent (e.g., low communion/low agency), as well as ambivalent clusters (e.g., low communion/high agency). Moreover, we found out that a robot's stereotypical appearance has a role in activating stereotypes. Indeed, in our study, female robots featuring appearance cues socio-culturally associated with femininity (e.g., eyelashes or apparel) were perceived as more communal, and juvenile robots featuring appearance cues tapping into the baby schema (e.g., cartoony eyes) were perceived as more communal, less agentic, and less suited to perform tasks. Given the renowned relationship between stereotyping, prejudice and discrimination, the causal link between appearance and stereotyping we establish in this paper can help HRI researchers disentangle the relation between robots' design and people's behavioral tendencies towards them, including proneness to harm.

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.