The AI Decisions Gap: Why Model Explainability is Only Half the Battle

About this session

When a regulator, a court, or an inspector general reviews an AI-driven decision, the first question is never "how does your model work" - it's "who decided? what did they know? and can you prove it?" Model explainability is necessary, but not sufficient: accountability lands on the human decision made on top of the model, and most organizations have no durable record of that decision at all — a gap that compounds, as AI compresses the time between insight and action, into what I call decision debt.In this session I'll make the case that the decision, not the model, is the real unit of accountability, and walk through what it takes to make decisions reproducible, attributable, and auditable the moment they are made — human attestation with separation of duties, a frozen evidence chain, and a system disciplined enough to refuse when the evidence is too thin to defend. I'll ground it in real use cases, like a live AI-enhanced flood-warning deployment, and in why governing decisions well makes teams faster, not slower. If you build, audit, or answer for AI that drives consequential decisions, you'll leave with a sharper way to close the gap between a model you can explain and a decision you can defend.