Thousands of hours of context engineering has shown me how LLMs will do their best to answer a question with insufficient context and can give all sorts of wrong answers. I've found that the way I prompt it and what information is in the context can heavily bias the way it responds when it doesn't have enough information to respond accurately.
You assume the bias is in the LLM itself, but I am very suspicious that the bias is actually in your system prompt and context engineering.
Are you willing to share the system prompt that led to this result that you're claiming is sexist LLM bias?
Edit: Oidar (child comment to this) did an A/B test with male names and it seems to have proven the bias is indeed in the LLM, and that my suspicion of it coming from the prompt+context was wrong. Kudos and thanks for taking the time.