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Researchers at Particular Audience have developed a technique that reveals exactly which training data shaped specific AI search results, piercing through the opacity that has long shrouded how these systems make decisions. The breakthrough could fundamentally reshape how search giants defend themselves against bias claims and copyright lawsuits by making their algorithmic choices auditable for the first time. If the method proves scalable, it threatens the competitive secrecy that has allowed companies like Google and OpenAI to operate largely without external oversight.