""Even a small proportion of AI-generated citations in high-stakes areas raises trust and reliability concerns," Originality.ai Director of Marketing and Sales Madeleine Lambert told The Register via email."
""And while AI Summaries aren't directly used in training data, over-sampling AI-written content makes it more likely those outputs are recycled into future models. This can then become a recursive loop.""
""Model collapse is a degenerative process affecting generations of learned generative models, in which the data they generate end up polluting the training set of the next generation. Being trained on polluted data, they then mis-perceive reality.""
Originality.ai analyzed 29,000 YMYL Google queries, the top-page AI Overviews, their cited links, and the first 100 organic results for each query. The AI Detection Lite 1.0.1 model flagged 10.4 percent of AI Overview citations as likely generated by large language models. AI Overviews therefore sometimes draw on output produced by other AIs, which can amplify recycled ideas and biases and create echo chambers. Over-sampling AI-written content raises the probability that those outputs reappear in future training sets, increasing recursive-loop risks and potential model collapse. Google challenged the study and the detector's accuracy.
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