9. Artificial Intelligence, Health and Knowledge: Data Infrastructures for the Life Itself
Kaya Akyüz, University of Vienna; Mónica Cano Abadía, BBMRI-ERIC; Melanie Goisauf, University of Vienna; Michaela Th. Mayrhofer, BBMRI-ERIC
Posted: February 28, 2022 Accepted Languages: English/Inglés/Inglês, Spanish/Español/Espanhol
Big data and machine learning applications are expected to improve predictive analytics, accuracy, and diagnostic performance in technologies, like imagining and genomics. These technologies are shifting the ways in which knowledge about the body and the life itself is produced. We are facing an increasing biomedicalization and molecularization of life and hype around these new technologies. The socio-technological conditions under which they are transforming practices, and the implications for existing/future knowledge and data infrastructures need further research.
We invite contributions that provide insights into how artificial intelligence is shaping knowledge production in the field of biomedicine, with a special interest in data practices and infrastructures, by questioning:
How are certain aspects of human biology made the object of study through AI technologies and how does this reshape the understanding of the human body, health, and disease?
In a continuously datafied world how are certain topics prioritized, ignored, revitalized as researchable topics and what are the exclusion/inclusion dynamics that such practices entail?
How does medical AI intersect with and reproduce existing inequalities and power relations? How are biases reproduced?
What imaginaries are inscribed in approaches such as “explainable” and “trustworthy” AI?
How does the implementation of these technologies reshape human and non-human intra-actions?
How are existing infrastructures, such as biobanks and platforms, transformed through these data practices?
With this panel, we would like to foster exchanges among scholars, who are approaching these topics both empirically and conceptually, to enrich the STS perspective on the relationship between artificial intelligence, health and knowledge.
