Interpretability

Interpretability remains one of the greatest challenges in AI — modern neural networks with billions of parameters operate as effective black boxes. Anthropic's research on mechanistic interpretability has made progress in identifying specific circuits within neural networks. Techniques include attention visualization, feature attribution, and probing classifiers. The EU AI Act requires explainability for high-risk AI systems, creating regulatory pressure for interpretability advances. The tension between model capability and interpretability is significant: the most powerful models are typically the least interpretable, while highly interpretable models (like decision trees) are often less capable.