AI fails are usually because the 'summaries' we see on dashboards are too simplified, not because of the AI itself.
Before a human sees AI data, the software 'compresses' complex probabilities into simple scores or narratives. This creates a false sense of confidence and 'organizational momentum' that leads to disasters, even when the underlying math was technically correct.
The Unowned Layer: Interpretation in AI-Assisted Decision Making
SSRN · 6332340
<div> Artificial intelligence systems are now embedded in high-stakes institutional decision making. Governance frameworks have responded by strengthening model validation, bias mitigation, drift monitoring, documentation, and formal human oversight. These efforts address real technical and procedural risks. Yet one structural stage remains largely unexamined. </div> <div> <br> </div> <div> Between probabilistic computation and formal human approval lies a translation process. Statistical output