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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. [ABSTRACT FROM AUTHOR]

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 FROM AUTHOR]

This paper presents an experiment in which participants had to discriminate three mid-air haptic shapes (circle, square, point) by reporting whether the haptic stimulus (e.g. circle on the palm of the hand) was compatible with an image (e.g. a circle) or a word (e.g. 'circle') displayed on a screen. Results indicate that only the 'point' stimulus was appreciably recognised and discriminated in terms of accuracy and time needed for the identification. Accuracy increased with repetition, and response time decreased, suggesting a learning effect. The comparison between visual and textual labels shows that for the haptic point stimulus there is no significant difference but a tendency to have greater accuracy with images than with texts, while the opposite result is found for the circle stimulus. This outcome suggests the need for new experiments focussed of the effect of visual/textual labels to make the recognition/discrimination tasks of haptic stimuli easier. Practitioner Summary: Three haptic shapes were presented with images or texts, matching or not the stimuli. The point was easy to recognise, while the circle and the square were difficult to discriminate against each other. Visual/textual labels bring contradictory results for different shapes. Abbreviations: 1D: one-dimensional; 2D: two-dimensional; 3D: three-dimensional; API: application programming interface; cm: centimeter; GLMM: generalised linear mixed-effect model; HCI: human-computer interaction; Hz: hertz; LMM: linear mixed-effect model; MCC: Matthews' correlation coefficient; mm: millimeter; ms: millisecond; QQ-plot: Quantile-Quantile plot; SD: standard deviation [ABSTRACT FROM AUTHOR]