The Rise of Synthetic Suffering in Humanitarian Campaigns
In an alarming trend sweeping the humanitarian sector, organizations are increasingly turning to AI-generated images depicting extreme poverty, vulnerable children, and survivors of sexual violence for their social media campaigns. This shift toward synthetic imagery represents what experts are calling “poverty porn 2.0” – a digital evolution of the long-criticized practice of using sensationalized poverty imagery to solicit donations.
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“The images replicate the visual grammar of poverty – children with empty plates, cracked earth, stereotypical visuals,” explains Arsenii Alenichev, a researcher at the Institute of Tropical Medicine in Antwerp who has studied this emerging phenomenon extensively. His research has identified more than 100 AI-generated images of extreme poverty currently being used in social media campaigns against hunger and sexual violence.
Drivers Behind the Digital Shift
Multiple factors are fueling this transition to artificial imagery. According to Noah Arnold of Fairpicture, budget constraints resulting from US funding cuts to NGO budgets have created financial pressure that makes AI alternatives increasingly attractive. “It’s quite clear that various organizations are starting to consider synthetic images instead of real photography because it’s cheap and you don’t need to bother with consent and everything,” Alenichev confirms.
The convenience factor cannot be overstated. As organizations navigate complex consent processes and ethical considerations around depicting real vulnerable individuals, AI-generated content offers a seemingly straightforward alternative. However, this convenience comes at a significant ethical cost that extends beyond immediate ethical concerns about representation in humanitarian work.
Amplification of Harmful Stereotypes
The AI-generated images appearing on major stock photo platforms like Adobe Stock Photos and Freepik often perpetuate the most damaging racial and regional stereotypes. Captions such as “Asian children swim in a river full of waste” and “Caucasian white volunteer provides medical consultation to young black children in African village” accompany images that researchers describe as heavily racialized.
“They are so racialized. They should never even let those be published because it’s like the worst stereotypes about Africa, or India, or you name it,” Alenichev states. These problematic representations echo similar challenges seen in other sectors where corporate responsibility intersects with representation and ethical considerations.
Platform Responsibility and Limitations
Stock photo platforms find themselves in a difficult position regarding this content. Joaquín Abela, CEO of Freepik, acknowledges the issue but places responsibility primarily on media consumers rather than platforms. “It’s like trying to dry the ocean. We make an effort, but in reality, if customers worldwide want images a certain way, there is absolutely nothing that anyone can do,” he explains.
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Freepik has attempted to address biases in other areas of its photo library by “injecting diversity” and ensuring gender balance in depictions of professionals like lawyers and CEOs. However, the platform’s business model, which relies on user-generated content and pays contributors licensing fees, creates structural challenges in policing harmful content.
Case Studies: Major Organizations Using AI Imagery
Several prominent organizations have already incorporated AI-generated content into their campaigns. In 2023, the Dutch branch of Plan International released a video campaign against child marriage featuring AI-generated images of a girl with a black eye, an older man, and a pregnant teenager. Similarly, the UN posted a video with AI-generated “re-enactments” of sexual violence in conflict, including synthetic testimony from a Burundian woman describing being raped.
The UN later removed the video, with a Peacekeeping spokesperson stating: “The video in question has been taken down, as we believed it shows improper use of AI, and may pose risks regarding information integrity.” These incidents highlight how even well-intentioned organizations can stumble when navigating new technological developments in their communication strategies.
The Feedback Loop of Bias
Perhaps most concerning is the potential for these AI-generated images to create a self-perpetuating cycle of bias. As Alenichev warns, biased images in global health communications may filter out into the wider internet and be used to train the next generation of AI models – a process known to amplify existing prejudices.
Generative AI tools have consistently demonstrated a tendency to replicate and sometimes exaggerate broader societal biases. When these tools are trained on stereotypical imagery, they produce even more extreme versions of those stereotypes, which then feed back into the training data for future AI systems. This technological challenge reflects broader industry developments in AI governance and ethical implementation.
Moving Toward Ethical Solutions
Some organizations are beginning to establish guardrails. Plan International has adopted guidance “advising against using AI to depict individual children” as of this year. The organization stated that its 2023 campaign used AI-generated imagery specifically to safeguard “the privacy and dignity of real girls.”
Kate Kardol, an NGO communications consultant, expresses concern about these developments: “It saddens me that the fight for more ethical representation of people experiencing poverty now extends to the unreal.” Her sentiment reflects a broader unease within the humanitarian community about balancing effective fundraising with dignified representation.
As the technology continues to evolve, the sector faces critical questions about how to harness AI’s potential without perpetuating harm. The solution will likely require a combination of platform policies, organizational guidelines, and continued critical examination of how technology intersects with humanitarian ethics.
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