Safety & AlignmentInterpretability
Explainability
The degree to which humans can understand why a model made a particular prediction or decision.
Explainability (or interpretability) is the field focused on making AI model decisions understandable to humans. It matters for trust, debugging, regulatory compliance, and catching problematic behavior.
Techniques range from simple (feature importance, attention visualization) to advanced (SHAP values, mechanistic interpretability research that traces how transformer layers process information).
The challenge: deep neural networks with billions of parameters are extremely opaque. Even their creators often can't fully explain specific outputs.
Anthropic, OpenAI, and academic researchers are actively working on interpretability research, hoping to understand LLMs at a mechanistic level. This is considered essential for safely deploying powerful AI systems.