Part D

Technology acceptance research and outreach

WP9 NEXUS Experiments

The WP9 centers on NEXUS Experiments, a creative and participatory platform based at the University of Freiburg that fosters a bidirectional exchange of knowledge between scientific and non-scientific stakeholders. This subproject synthesizes preliminary and ongoing research outcomes from all work packages and disseminates them through innovative public outreach and knowledge transfer formats.

Participatory

Connecting society through artful science dialogue.

The objective is to cultivate an open and constructive dialogue between researchers and the public on the societal and ethical dimensions of assistive AI systems, while also enabling valuable feedback and diverse perspectives from society to inform academic research.

Through dynamic and interactive events, NEXUS Experiments redefines the dialogue between science, art, and society, creating vibrant spaces where scientists, artists, developers, and the public engage in lively exchange.

Engaging

By combining participatory formats with artistic interventions, NEXUS generates new perspectives and ideas that inspire reflection and active involvement in shaping the future of science and technology.

Ultimately, Work Package 9 aims to advance a novel model of technology acceptance in human–robot interaction, positioning technology acceptance research as an inclusive, socially embedded, and collaborative practice.

NEXUS Experiments invites everyone to join this exciting journey of curiosity, creativity, and co-creation—pushing boundaries where science meets society.

Co-creative

Recent Highlights from WP9

WP9 (NEXUS experiments) has completed reviews of various robot acceptance models and is now utilizing Cognitive-Affective Maps to investigate public perceptions of human-robot collaboration. The early findings are promising. The study is an international, bilingual effort involving healthcare professionals in Germany, Colombia, and the USA.

Figure 1: *“Fairness and Bias in Robot Learning” by Laura Londoño et al., featured on the cover of the Proceedings of the IEEE, Vol. 112, No. 4, April 2024.*

Figure 2: The typical robot learning paradigm consists of different stages. Each of these stages can incorporate bias and result in discriminatory behavior. Accounting for fairness in robot learning is essential for robots to operate in human environments.

Figure 3: Learning a fair model for robots can include strategies categorized as in-processing and post-processing. First, in-processing strategies consist of adding fairness notions during the training stage. After obtaining a robot model, post-processing debiasing strategies aim to find and correct bias.

To foster public engagement, recent activities have included the GenAI & Me workshop, youth robotics outreach initiatives, and the development of a mobile/web serious game titled “Adventure on Servo Bay.”

The serious game is part of the science communication activities accompanying the project ReScaLe. It aims to inspire young people for robotics, artificial intelligence, and STEM subjects, while also conveying important societal, ethical, and legal questions related to human-robot interaction. With “Adventure on Servo Bay,” an interactive learning space is being created that playfully imparts knowledge and appeals to a broad target audience. The release is planned for the coming years, and we look forward to the positive impact this project will have on education and science communication.