Projekt

The aim of this project is to carve out the politics of classification as a prerequisite for objectifying health-related decision-making. Symptom checkers, mostly based on AI, promise substantiated diagnoses and information for patients and are often assumed to be better ?more objective ? doctors. Yet, AI-based decision-making has proven not to be as neutral and objective as intended, which has elicited discussions on bias and reliability. This project takes an STS perspective and focuses on the practices of formalization and classification, which aim at enhancing objectivity and reliability but at the same time exclude certain users, knowledges, and practices. Based on an ethnographic approach, the project owners conduct participant observation and semi-structured interviews with the IT and medical experts of companies offering a symptom checker as well as with different user groups. On the side of the companies, they unfold categories and classification systems; on the side of users, they explore how they understand the app's results and negotiate questions of objectivity, authority, and personal meaning. The project's interrelated objectives are (1) to inquire on the translation of medical expertise into technical and data infrastructures, (2) to unearth the mostly-invisible but powerful sociotechnical systems shaping digital health applications and their impact on society and (3) to observe the emergence of new practices of diagnosis and health literacy. The expected scientific findings will add to the discussion of bias and objectivity regarding AI and will substantiate the relevance of a broad academic, as well as public, discourse on automated decision-making in healthcare and its social impact.

• Prof. Dr. Silja Samerski

  Hochschule Emden/Leer
  FB Soziale Arbeit und Gesundheit
  Emden
  

• Prof. Dr.-Ing. Corinna Bath

  Technische Universität Braunschweig
  FB Maschinenbau
  Institut für Flugführung
  Braunschweig