How to build explainability into an AI system ?

Explainable AI (XAI) fits along the entire knowledge ladder, but its role becomes more important the higher you go.

1. From Data to Information

XAI helps reveal what the AI is looking at.
At this stage, explainability techniques show which parts of the input mattered. This is low-level interpretability, explaining how raw data becomes structured information.

2. From Information to Knowledge

Here XAI explains what the model has learned internally. This step makes the model’s knowledge representation more transparent.

3. From Knowledge to Reasoning

This is where XAI is most critical. Explainability is used to show: > The logical chain the model followed > Why it combined certain pieces of knowledge > Where reasoning failed (hallucination detection, consistency checks) > How intermediate steps (chain-of-thought or rationales) are structured

This is reasoning-level explainability.

4. From Reasoning to Action (or Wisdom)

At the highest level, XAI explains why the AI acted in a particular way. Examples: - A self-driving car explaining why it braked - A recommender system explaining why it suggested a product - A medical AI providing justification for a diagnosis - AI agents giving policy-level rationales

This is decision-level explainability is often needed for safety, regulation, user trust & accountability.

Explainable AI works as a transparent layer across the entire ladder: > Bottom: Explain what inputs matter > Middle: Explain what the model has learned > Top: Explain why the system made a decision

Using the following link you can access this sandbox SIPOC model in the ProcessHorizon web app and adapt it to your needs (easy customizing) and export or print the automagically created visual AllinOne SIPOC map as a PDF document or share it with your peers: https://app.processhorizon.com/enterprises/SgFwDWtHg7EBBVVS2bYy7wjY/frontend