The project carried out its empirical and analytical work while adapting to rapid technological change in generative AI. The research combined visual analysis, stakeholder interviews, live platform auditing, media monitoring and interdisciplinary exchange.
First, the project identified and analysed more than 100 images published or circulated by non-governmental, humanitarian and global health organisations on their social media. This analysis examined recurring tropes, including poverty, suffering, ethnicity, childhood, expertise, care, empowerment and technological intervention. It showed how familiar patterns continue to structure global health communication, including where AI tools are beginning to be used.
Second, the project conducted 29 semi-structured interviews with visual and technical stakeholders, including AI artists, technology specialists and communication professionals working at NGOs. These interviews explored image decision-making, ethical risks and responses to generative AI. A key finding was that many dilemmas now associated with AI were already present before AI: images were already shaped by staging, editing, donor expectations, organisational branding and pressure to produce emotionally effective stories. AI can accelerate and automate these existing tensions.
Third, the project audited commercial AI tools, stock-image environments and media platforms, documenting how AI-generated global health and humanitarian-style images can be produced, sold and circulated at scale. This showed that commercial image infrastructures play an important role in making synthetic stereotypes visible, searchable and reusable.
Fourth, the project co-organised an international workshop on artificial images in global health before and in the era of AI. The workshop brought together 27 experts from five continents and strengthened the project’s historical perspective by situating AI-generated imagery within a longer history of image manipulation.
The project produced scientific and public-facing outputs on the ethics of global health communication in the AI era, racialised visual stereotypes, and the need to understand AI bias as a social, historical and institutional issue as well as a computational one.
Methodologically, the project adapted from an initially planned emphasis on systematic generation of synthetic images towards a broader value-sensitive audit of real-world AI imagery and live commercial platforms. This increased the relevance of the research while reducing the need for resource-intensive image generation.
Overall, AIrbrush achieved its core aims by generating new empirical evidence, developing an interdisciplinary conceptual framework, and producing outputs relevant to debates on AI, ethics and global health communication.
