Seminars and Events
Evaluating Text-to-Image Platforms’ Content Moderation During the 2024 US Presidential Election
Event Details
Speaker: Kevin Greene, Princeton University
Location: Virtual Only via Zoom
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How do generative AI platforms’ content moderation policies handle the creation of political deepfakes? There are considerable concerns about the risks posed by AI generated images of political leaders, but no systematic evaluation detailing how AI platforms address this outcome. We leverage an automated pipeline to extract and transform references to individuals on the US Presidential tickets from prominent media into prompts for generative AI systems, enabling politically diverse, externally valid evaluations. These prompts are sent to three prominent T2I platforms each week for the final three months of the 2024 US Presidential election. First, we show that the platforms take different approaches to content moderation. These differences contribute to there being low agreement in blocking behavior between platforms. Second, there is little consistency in the blocking behavior within platforms over time. Stability AI allowed almost all prompts featuring political figures until a sudden change two weeks before the 2024 election. Further, almost no prompts were blocked in every week of our collection. Our findings highlight the importance of developing scalable context specific approaches to monitoring text-to-image platforms.
Speaker Bio
Host: Zhuoyu Shi, POC: Pete Zamar
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