Local transplantation, adaptation, and creation of AI models for public health policy - Fournier-Tombs, Eleonore (Frontiers in Artificial Intelligence, 2023)
This paper presents the TAC framework, a method for assessing the localisation of different elements of an AI system. This framework is applied in the public health context, notably to different types of models that were used during the COVID-19 pandemic, The framework, which stands for Transplantation, Adaptation and Creation, aims to guide AI for public health developers and public health officials in conceptualising model localisation. The paper provides guidance justifying the importance of model localisation, within a broader context of policy models, geopolitics and decolonisation. It also suggests procedures for moving between the different elements in the framework, for example going from transplantation to adapation, and from adaptation to creation. This paper is submitted as part of a special research topic entitled: A digitally-enabled, science-based global pandemic preparedness and response scheme: how ready are we for the next pandemic?