Text Embedding Models Contain Bias
In Which We Define 'Biased' As Meaning 'Not Conforming To Our Ideas Of How The World Should Be'. Because it's unthinkable that movies with male main characters could actually just be better than movies with female main characters.(I'm not saying they are, mind you - but when we analyze sentiment in a large dataset and reach a result like that, the first question to ask should be "is that result accurate?" not "how do we tune out this problematic result?")