we are building increasingly powerful artificial intelligence systems. models with billions of parameters can now diagnose diseases, drive cars, and make financial predictions with superhuman accuracy. but there's a catch: in many cases, we have no idea how they're doing it.
The "Black Box" Problem
this is the interpretability problem. as neural networks get deeper, their internal decision-making processes become virtually impossible for a human to understand. this is a massive barrier to trust and adoption, especially in high-stakes domains like medicine, finance, and law.