Enacting Citizens’ and Professionals’ Concerns on COVID-19 Vaccination with AI
Lea Loesch, Vrije Universiteit Amsterdam (VU); Elena Syurina, Vrije Universiteit Amsterdam; Florian Kunneman, Vrije Universiteit Amsterdam; Aura Timen, National Institute for Public Health and the Environment (RIVM); Teun Zuiderent-Jerak, Vrije Universiteit Amsterdam
Toronto 2021: Inventive AI
Incorporating patients’ and professionals’ experiential knowledge and value considerations remains one of the greatest challenges for guideline development (Wieringa et al. 2018). This is particularly true for vaccination guidelines, where the direct involvement of patients in the guideline development is of limited use, as vaccination guidelines are aimed at a large number and variety of citizens rather than specific patient groups.
This project explores the role AI-based methods, particularly from the field of natural language processing (NLP), can play in innovating the inclusion of values and experiential knowledges in vaccination guidelines. Text mining methods are deployed to analyse existing but unexplored sets of textual records where societal and professional knowledge and concerns are expressed, such as databases form the National Institute for Public Health and the Environment (RIVM) as well as various social media platforms.
Thereby, we seek to depart from the image of algorithms as infallible, autonomous techno-scientific artifacts, but rather regard them as figures mobilised by practitioners and analysts (Ziewitz 2017), including ourselves.
