SOURCE-LINKED INTELLIGENCE

AIrbrush: a transdiciplinary value-sensitive study of biases and stereotypes in AI-generated Global Health images, and their significance for science and society

CORDIS · observation · Publication date unknown

d Global Health images, and their significance for science and society This project engages with the proliferation of abusive and biased stereotypes from colonial and humanitarian photography through generative AI technology, and investigates their consequences for science and society. This is a pressing issue: AI simultaneously absorbs and learns from real images, which, in the case of global health, have been marked by racism, coloniality and sexism, meaning that given images become a cluster for generative AI to learn from biased depictions and perpetuate negative stereotypes. Such cycles have to be studied and eliminated in order to move toward more equal postcolonial societies and promote a culture of value-sensitive depictions of vulnerable people. The project builds and greatly expands on the emerging methodology of purposeful generation and value-sensitive evaluation of AI-generated Global Health visuals, recently pioneered by Prof. Koen Peeters (the supervisor) and Dr. Alenichev, and encapsulated in a Lancet Global Health Article in August 2023. Offering a first-ever systema

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recordType

award

status

SIGNED

region

EU

value

191760

unit

EUR