Most "explainable AI" explains the model, not the decision — which is why users still don't trust it
I build cash-flow and forecasting tools, and the hardest part was never the model. It was making the output something a non-technical person would actually trust enough to act on. That's where I think a lot of the XAI conversation goes sideways.
Here's the thing nobody says out loud: SHAP, LIME, attention maps — those explain the model to you, the builder. They do almost nothing for the person staring at a decision. A bar chart of feature importances is not an explanation to a founder asking "can I cover payroll next month." It's an explanation of the math, dressed up as an explanation of the situation.
A few things I had to unlearn:
Faithful and useful are usually in tension. The most faithful explanation (here are 200 weighted features) is useless. The most useful one (here are the 2 drivers and what changed) is a simplification a reviewer could call "lossy." The field mostly picks faithfulness, because it's defensible in a paper — not because it helps a human.
Descriptive explanations are half an explanation. "Revenue is down because of X" is where most tools stop. The user actually needs "…and if you do Y, here's the projected change." Explanation without a next action is just a nicer-looking number.
Uncertainty is part of the explanation, not a disclaimer. A point forecast with no distribution behind it is a confident lie. Showing the range — or the regime the model thinks you're in — is itself explanatory, and it builds trust faster than any feature-importance chart.
LLMs are a trap here. You can now translate model internals into plain language, which is great — until you let the model narrate freely and it quietly smooths over what the math actually did. Now your "explanation" is a hallucination. The only thing that's worked for me is hard-grounding the narrative in the computed values and letting the LLM phrase, never invent.
Curious how others handle the faithfulness-vs-usefulness tradeoff — do you expose raw attributions, the simplified story, or both behind a toggle? And has anyone found a clean way to keep an LLM narration layer honest?
Posted Jun 8, 2026, 4:40 PM UTC