an AI resume screener had been trained on CVs of employees already at the firm, giving people extra marks if they listed “baseball” or “basketball” – hobbies that were linked to more successful staff, often men. Those who mentioned “softball” – typically women – were downgraded.
Marginalised groups often “fall through the cracks, because they have different hobbies, they went to different schools”
you are right, i don’t know how LLMs are trained, but ironically, this is a perfect example of a minority being privelaged by a system, and racism is still very much involved.
an important assumption you have to consider: in your example, why did the AI know what race people are in the first place? it seems a small consideration but it’s so wildly significant.
the modern understanding of race was not present throughout all of history, and only arose in the 17th century. without getting into the weeds, the fact that your fictional AI can distinguish between whiteness and non-whiteness already means it was designed by someone who understands those structures, and let them slip into the AI itself.
a perfectly well-meaning and anti-racist designer would prevent the AI from even recognizing race at all costs, both directly by sanitizing training data to remove race from the inputs, and indirectly by noting correlations with other data (such as sports, in this article) and controlling for that.