Explainability

The ability to describe how an AI system arrived at a particular output or decision in terms that humans can understand. It is legally relevant in some contexts: GDPR Articles 13 to 15 require meaningful information about the logic involved in automated decisions covered by Article 22, and Article 86 of the EU AI Act gives affected persons a right to an explanation of certain decisions based on high-risk AI. It is also operationally important for debugging, auditing, and building user trust. There is a spectrum from full transparency (the model's reasoning is completely legible) to post-hoc explanation (a separate method generates an explanation after the fact). Most large language models are not inherently explainable.