The "Human Brake": Architecting Review Loops for High-Stakes AI
In low-stakes scenarios—like a chatbot recommending a restaurant—an AI mistake is merely annoying. In high-stakes scenarios—like generating a legal contract, summarizing a medical record, or approving a loan—that same mistake becomes a business risk. This is the Trust Gap . Stakeholders expect zero-error behavior , yet probabilistic systems like LLMs cannot offer absolute guarantees. Waiting for a “perfect” model is not a strategy. It is a dead end. The real solution is architectural. To deploy AI safely, we must stop trying to replace humans and start designing systems that amplify them. We need a Human Brake: a workflow where AI does the heavy lifting, but a human expert acts as the final commit gate before anything irreversible happens. Here’s how to design that review loop on Databricks using the MLflow Review App—and turn AI from a liability into a force multiplier.