06/18/2026
Would you trust an AI decision if no one could explain how it was made?
That's exactly why Explainable AI matters.
Here are 3 practical tips to make your AI systems more explainable:
🔹 Tip #1: Make AI decisions easy to understand
If stakeholders can't understand why an AI system made a decision, they're less likely to trust it. Prioritize clear, plain-language explanations.
🔹 Tip #2: Create accountability from day one
Define who reviews AI outputs, how decisions can be challenged, and where human oversight is required.
🔹 Tip #3: Document inputs and assumptions
Keep track of data sources, inputs, and key assumptions so decisions can be reviewed and validated when needed.
Remember: Explainability isn't about explaining every line of code. It's about helping people understand how AI works, where its limitations are, and when human judgment should take over.
Save this checklist for your next AI project. DM "AI" to learn more.