Ninth International Conference on Communication & Media Studies

Abstract

The interaction between humans and machines is complex since multiple parameters changing at the same time shaping a dynamic socio-technical system opening a space of uncertainty in action. Uncertainty, by the means of educational science, refers to the lack of complete knowledge or predictability when making decisions. However, computational systems suggest certain outcomes and the elimination of ambiguity (Bridle 2018, Verständig 2019, Williamson et al. 2023). This often causes images of machine systems such as black boxes (Pasquale 2015) or anthropomorphisms (Alabed et al. 2022). Our qualitative research, based on 16 interviews and ethnographic observations over the past two years, examines how people engage with machines for development, artistic expression, exploration, and educational purposes (Ahlborn et al. 2022). We use ethnographic research and narrative interviews to explore and reconstruct images and moments of uncertainty in these interactions (Christin 2020, Verständig 2020). Our goal is to enhance the understanding of the subtleties and complexities of this dynamic in-between space. We recognize the persistent nature of uncertainty in these interactions, viewing it as a basis to explore AI-related images and imaginaries, from educators to artists. The findings inform teaching materials, such as data stories and metaphors for civic data infrastructures and higher education, aiming to debunk AI myths and foster diverse understandings. Combining creative practices and education, our research offers a unique perspective, laying the foundation for future interdisciplinary research on the explainability of AI.