Trusting AI Extraction - Confidence Scores, Governance and Human in the Loop Design

The number one barrier to AI adoption is not technology -- it is trust. When extracted data feeds into compliance workflows, financial systems, or regulated processes, "mostly accurate" is not good enough. This roundtable tackles the trust problem head-on, exploring Box Extract's new confidence scoring system, governance controls, and design patterns for human-in-the-loop validation. We will examine how enterprises are building extraction workflows where AI handles the high-confidence cases automatically while routing uncertain results to human reviewers. This session provides a practical framework for deploying AI extraction with high trust.

Topics we'll cover:

• How Box Extract confidence scores work (aggregated LLM responses, decimal percentages, Low/Medium/High labels) and how to set thresholds for your use case
• Design patterns for human-in-the-loop workflows and the Accuracy / Automation curve
• How Box's native governance model (permissions, classification, audit trails) provides trust infrastructure
• Practical governance policies for AI extraction: who can configure agents, which folders are in scope, how to audit extraction results
• Regulatory considerations: HIPAA, GDPR, and industry-specific requirements for AI-processed content