Abstract
Complex data-driven algorithmic systems such as AI are deeply embedded in contemporary culture (Seaver 2017; Seyfert & Roberge 2017; Stalder 2017), for example in the curation of individual social media feeds or personal playlists based on the analysis of vast quantities of unstructured data. Conversely, they exert a direct influence on cultural production (Seaver 2022). This study is dedicated to examining the connection between AI and art. This field is facing a number of challenges, both in terms of concepts such as authorship, intentionality or creativity, as well as in terms of changing conventions of representation and perception in terms of aesthetics (Manovich & Arielli 2024). This submission provides insights into a PhD project that focuses on subjectivation in the context of AI and art (Ahlborn 2020, 2023, 2024). The research design is based on an ethnographic approach (Christin 2020) and draws inspiration from the field of workplace studies (Schmidt 2008), which focuses on the prevailing technological and material conditions. Thus the question arises as to which algorithmic models are used and what role data plays in the development and design of AI art. Furthermore, to what extent can a materiality be ascribed to such systems? To answer these questions, the submission draws on empirical material in the form of detailed workplace descriptions, observation protocols and interview material with a total of 12 artists and creative technologists. The aim is to provide an answer to the question of changing material and medial conditions of complex data-driven algorithmic systems (Bajohr 2022).
Presenters
Juliane AhlbornResearch Assistant, Faculty Educational Sciences, Bielefeld University, Germany
Details
Presentation Type
Paper Presentation in a Themed Session
Theme
2025 Special Focus—From Democratic Aesthetics to Digital Culture
KEYWORDS
AI Art, Materiality, Algorithmicity, Mediality
