AI Explainability

A practical example

Example: when an AI scoring model deprioritizes a lead, an explainable setup lets an ops manager see the input attributes, the model version, and the contributing factors, and explain the decision to sales without guessing.

What to evaluate before investing

• Ask whether outputs include citations or source references, and whether those references are stored and auditable after the fact.
• Check if you can retrieve the exact inputs, prompt version, and model version for any historical decision.
• Clarify the vendor's own visibility: can they explain internal model behavior, or only the inputs they control?

Limitations and tradeoffs

Explainability has limits: for many large models, even vendors can only approximate why an output occurred. Treat explanations as evidence for review and accountability, not as proof of correctness.