Model Explainability
The ability to understand and communicate why an AI model produced a specific output—which input features influenced the decision, how confident the model is, and what would change the outcome. Explainability is a regulatory requirement in many contexts (the EU AI Act mandates it for high-risk AI systems) and a practical requirement for trust. AI agents in healthcare, finance, legal, and HR must provide explainable decisions so that human reviewers can verify the reasoning and catch errors.
Written by Max Zeshut
Founder at Agentmelt · Last updated Sep 9, 2026
Example
An AI lending agent declines a loan application and provides the explanation: 'Declined due to debt-to-income ratio of 48% (threshold: 43%) and 2 late payments in the past 12 months. Increasing monthly income by $800 or resolving the late payment records would change this decision.' The applicant and the compliance team can both understand and verify the reasoning.
Frequently asked questions
- Is explainability the same as interpretability?
- They are related but distinct. Interpretability means the model's internal mechanics are understandable (e.g., a decision tree is inherently interpretable). Explainability means the model's outputs can be explained to stakeholders, even if the internals are complex. Modern LLM-based agents achieve explainability through chain-of-thought reasoning and citation—showing their work even though the underlying model is not inherently interpretable.