Anthropomorphize me!

In this paper, we propose a methodology to maximize the benefits of interdisciplinary cooperation in AI research groups. Firstly, we build the case for the importance of interdisciplinarity in research groups as the best means to tackle the social implications brought about by AI systems, against the backdrop of the EU Commission proposal for an Artificial Intelligence Act. As we are an interdisciplinary group, we address the multi-faceted implications of the mass-scale diffusion of AI-driven technologies. The result of our exercise lead us to postulate the necessity of a behavioural theory that standardizes the interaction process of interdisciplinary groups. In light of this, we conduct a review of the existing approaches to interdisciplinary research on AI appliances, leading to the development of methodologies like ethics-by-design and value-sensitive design, evaluating their strengths and weaknesses. We then put forth an iterative process theory hinging on a narrative approach consisting of four phases: (i) definition of the hypothesis space, (ii) building-up of a common lexicon, (iii) scenario-building, (iv) interdisciplinary self-assessment. Finally, we identify the most relevant fields of application for such a methodology and discuss possible case studies.

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Towards the development of an automated robotic storyteller: comparing approaches for emotional story annotation for non-verbal expression via body language

Steinhaeusser,

Zehe,

Schnetter

et al. 2024

J Multimodal User Interfaces

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Storytelling is a long-established tradition and listening to stories is still a popular leisure activity. Caused by technization, storytelling media expands, e.g., to social robots acting as multi-modal storytellers, using different multimodal behaviours such as facial expressions or body postures. With the overarching goal to automate robotic storytelling, we have been annotating stories with emotion labels which the robot can use to automatically adapt its behavior. With it, three different approaches are compared in two studies in this paper: 1) manual labels by human annotators (MA), 2) software-based word-sensitive annotation using the Linguistic Inquiry and Word Count program (LIWC), and 3) a machine learning based approach (ML). In an online study showing videos of a storytelling robot, the annotations were validated, with LIWC and MA achieving the best, and ML the worst results. In a laboratory user study, the three versions of the story were compared regarding transportation and cognitive absorption, revealing no significant differences but a positive trend towards MA. On this empirical basis, the Automated Robotic Storyteller was implemented using manual annotations. Future iterations should include other robots and modalities, fewer emotion labels and their probabilities.

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Anthropomorphize me!

Cited by 9 publications

References 23 publications

Personalized Storytelling with Social Robot Haru

Personalized Storytelling with Social Robot Haru

Maximizing team synergy in AI-related interdisciplinary groups: an interdisciplinary-by-design iterative methodology

Towards the development of an automated robotic storyteller: comparing approaches for emotional story annotation for non-verbal expression via body language

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