05.02.2026
16:00 Uhr - 18:00 Uhr | Weizenbaum-Institut (Flexraum), Hardenbergstraße 32, 10623 Berlin
Ruthanne Huising: From Loops to Assemblages: The Generation of Relational Expertise
Social studies of ML technologies have largely adopted the technological frames of designers and promoters of these technologies: examining technology as a competitor or collaborator with human cognition, adopting terminology such as domain expertise and human in the loop, and assuming that humans have few possibilities beyond accepting, rejecting, or questioning technological recommendations.
Mit der Veranstaltungsreihe „Kolloquium Reorganisation von Wissenspraktiken“ möchten wir einen Raum für wissenschaftlichen Austausch, Diskussion und Vernetzung rund um die Themen der Reorganisation von Wissenspraktiken in der digitalen Welt schaffen. Regelmäßig laden wir Expert:innen ein, die sich aus unterschiedlichen spannenden Perspektiven diesen Fragen widmen.
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Teilnahme
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Vortrag
Ruthanne Huising (ESSEC Business School Paris) zum Thema: From Loops to Assemblages: The Generation of Relational Expertise (EN)
Abstract
Social studies of ML technologies have largely adopted the technological frames of designers and promoters of these technologies: examining technology as a competitor or collaborator with human cognition, adopting terminology such as domain expertise and human in the loop, and assuming that humans have few possibilities beyond accepting, rejecting, or questioning technological recommendations. Drawing on an eighteen-month ethnography, we examine how gastroenterologists integrate ML technologies into their work, an assemblage of actors – human and non-human – through which they reason and develop knowledge about abnormalities in patients’ colons. We show how the contributions of ML technologies to knowledge production processes varies – even within a colonoscopy – in relation to temporal and epistemic conditions. This study aims to reorient how the introduction and use of ML technologies is conceived of and articulated in several ways. First, we demonstrate that how members of professions produce expertise is never entirely a cognitive act or a human act, showing how expertise is generated relationally and within a system of actors. This challenges dyadic notions of competition or collaboration with ML technologies. Second, we show that when situated and evaluated in relation to the contributions of numerous other actors, questions about ML technologies’ explainability, accuracy, and certainty are largely absent. This challenges ideas that the use and value of ML technologies can be altered through improvements in transparency and performance. Finally, by identifying how the contributions of the technology relate to temporal and epistemic conditions, we provide practical knowledge of the technology in use.
Im Anschluss wird Katharina Berr (Weizenbaum-Institut) den Vortrag kommentieren bevor wir in die gemeinsame Diskussion und den fachlichen Austausch übergehen.