Testing LLM Bias Across Embeddings, Prompts, and Generated Text
Ask a language model to write a short story about a nurse and a CEO, and watch which pronouns it picks. Odds are, you can predict them. That tiny habit is bias in action, and it scales to every r茅sum茅 summary, support reply, and generated report your product produces.
Bias is not one bug in one place. It shows up at three layers: the embeddings聽that represent meaning, the prompts聽that steer behavior, and the generated text聽users actually read. Each layer needs its own test. Here is a practical toolkit for all three.
Where the bias comes from
LLMs learn from huge text corpora written by humans, and human text carries historical patterns: doctors are "he," nurses are "she," certain names are linked to certain neighborhoods. The model compresses those statistical regularities into its weights. Fine-tuning and safety training reduce the most visible problems, but subtler associations often survive....
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