This work is a case study in applying recent, high-level ethical guidelines, specifically concerning transparency and anthropomorphisation, to Human-Robot Interaction (HRI) design practice for a real-world Socially Assistive Robot (SAR) application. We utilize an online study to investigate how the perception and efficacy of SARs might be influenced by this design practice, examining how robot utterances and display manipulations influence perceptions of the robot and the medical recommendations it gives. Our results suggest that applying transparency policies can improve the SAR's effectiveness without harming its perceived anthropomorphism. However, our objective measures suggest participant understanding of the robot's decision-making process remained low across conditions. Furthermore, verbal anthropomorphisation does not seem to affect the perception or efficacy of the robot.
Infant engagement during play is an active area of research, related to the development of cognition. Automatic detection of engagement could benefit the research process, but existing techniques used for automatic affect detection are unsuitable for this scenario, since they rely on the automatic extraction of facial and postural features trained on clear video capture of adults. This study shows that end-to-end Deep Learning methods can successfully detect engagement of infants, without the need of clear facial video, when trained for a specific interaction task. It further shows that attention mapping techniques can provide explainability, thereby enabling trust and insight into a model's reasoning process.
CCS CONCEPTS• Applied computing → Psychology; • Computing methodologies → Interest point and salient region detections.
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