Explainability

What is Explainability?

Explainability enables users to understand why an agent made a particular decision, building trust and enabling error diagnosis. Techniques include attention visualization, feature importance scoring, natural language reasoning traces, and counterfactual explanations showing how different inputs would change outputs. For LLM agents, chain-of-thought prompting generates step-by-step reasoning that both improves accuracy and provides explanations.

Explainability is particularly critical in high-stakes domains like healthcare, finance, and legal decisions where understanding rationale is required for compliance, debugging, or building user confidence. However, complex models may resist full explanation, and generating explanations can impact performance or introduce new attack vectors. The field balances explanation fidelity against simplicity and computational cost.

Example

How Signet addresses this

Signet's Quality dimension values explainability as a marker of agent sophistication and trustworthiness. Agents providing clear reasoning for decisions score higher in quality. The Security dimension also considers explainability important for auditing and incident investigation.