What is Explainability?

Explained

Explainability is the capability to understand and explain how an AI model arrives at its outputs. It is critical for trust, debugging, and regulatory compliance. See XAI (Explainable AI).

Further reading

• Explainable AI (XAI): Building Trust and Transparency in AI-Driven Enterprise

Related terms

• XAI AI that explains its decisions in a understandable way.
• Bias Distortion in data or model that produces unfair or inaccurate results.
• Constitutional AI Training approach that aligns models with explicit rules and self-critique.
• Ethical AI AI designed and deployed with ethical principles (transparency, fairness, privacy).
• EU AI Act European regulation that classifies AI systems by risk and mandates compliance.
• Guardrails Runtime constraints and policies that keep LLM outputs safe and on-brand.