Vector search isn't full blown AI and should be inherently less prone to bias. It just converts words/phrases into vectors where the distance between the vectors represents semantic similarity.

It doesn't encode value judgements like whether a policy is good or bad, it just enables a sort of full text search ++ where you don't need to precisely match terms. Like a search for "changes to rent" might match a law that mentions changes to "temporary accommodations".

Bias is certainly possible based on which words are considered correlated to others, but it should be much less prone to containing higher-level associations like something being bad policy.

Which bias?