As generative AI increasingly summarizes, reproduces, and even rewrites the web, what happens to shared knowledge and cultural memory? Drawing on an interactive artwork that shows how AI can rewrite the same website according to different biases, this session explores how algorithmic personalization could fragment public discourse while inviting participants to discuss possible responses and alternatives.
A third of recent websites now contain AI-generated text; Google is increasingly making AI summaries the default mode of knowledge retrieval; and third parties are creating “sloppelgangers” of artists’ web pages with AI. What might this trend mean for cultural heritage, and what should we do about it?
In this session, we get hands-on with an interactive artwork to compare how an LLM can rewrite the same passage for different inferred biases, and talk plainly about what that means for classrooms, creativity, and shared cultural memory. After a quick demo, facilitators will lead participants in an exercise to see how AI could rewrite the web to match their own biases.
We’ll then workshop practical responses—like requiring source-checking across multiple outlets or via aggregators like AllSides, building class activities that compare AI summaries to original texts, and even a return to Web 1.0 protocols like RSS and web rings—to help protect public knowledge in a more personalized web.
