When professionals discover that ChatGPT or other large language models reference negative information about them, the experience often feels perplexing and unfair. Understanding why these systems emphasize unfavorable content requires examining the underlying mechanisms that determine which information surfaces in AI-generated responses. Large language models construct their knowledge about individuals through three distinct pathways. Training data forms the foundation, containing billions of text fragments collected from across the internet during specific periods. These datasets prioritize high-authority sources, meaning content from established publications carries inherently more weight than information from newer or less prominent platforms. According to Stanford’s research on AI…