Explainable AI Is Necessary, but It's Not Enough
CIO, Thursday, August 20th, 2026
CIO shows how a model can produce a perfect SHAP explanation and still be wrong because the data and rules are off.
The article opens with a fraud model scoring an insurance claim at 0.23, or low risk. A SHAP explanation lays out exactly why: no prior claims, a modest claim value, an unremarkable claimant profile.
A human adjuster reads the explanation, agrees and signs off, and every box that explainable AI asks us to check has been checked.
The claim is settled and closed. The article then shows what was actually wrong, arguing that a model can explain its answer perfectly and still be wrong when the underlying data and rules are flawed.