Safety, Alignment & Ethics

Interpretability

The ability to examine what is happening inside a model to understand its reasoning.

Definition

Interpretability research tries to open the 'black box' of neural networks — understanding what representations models form internally, which parts of the input they're paying attention to, and how information flows through the network to produce outputs. This is a fundamental research challenge because modern neural networks are enormously complex. Progress in interpretability is important for catching systematic errors, verifying alignment, and building justified trust in AI systems.

Related Terms

Explainability

Making AI decisions understandable to non-technical users.

Transparency

The ability to understand and explain how an AI reaches its outputs.

Attention Mechanism

The part of a transformer that lets the model focus on the most relevant words regardless of where they appear in a sentence.

Heard enough terminology — ready to talk outcomes?

We translate AI concepts into measurable business results. No upfront fees — you pay only when independently verified results are delivered.

← Back to glossary

Disclaimer

This definition is provided for educational and informational purposes only. It represents a general explanation of a technical concept and does not constitute professional, technical, or investment advice. Artificial intelligence is a rapidly evolving field; terminology, techniques, and capabilities change frequently. Coaley Peak Ltd makes no warranty as to the accuracy, completeness, or currency of the information provided. Nothing on this page should be relied upon as the sole basis for commercial, technical, legal, or investment decisions without independent professional advice.

Document reference: ISO_webpage_knowledge-base_glossary_v1

Last modified: 29 March 2026

Knowledge Base·Safety, Alignment & Ethics·Interpretability