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+# Artificial Intelligence, FOIA, and the Architecture of Democratic Transparency
+
+## Abstract
+
+This article examines Federal FOIA Intelligence Search as a case study
+in the responsible deployment of artificial intelligence within public
+records research.
+
+It argues that **architectural restraint**, not model sophistication,
+is the key determinant of legitimacy in civic AI systems.
+
+---
+
+## I. Introduction
+
+FOIA was designed for an analog era. As records proliferate, the challenge
+has shifted from access to navigation.
+
+AI promises assistance—but introduces risk.
+
+---
+
+## II. Design Philosophy
+
+The system adopts four governing principles:
+
+1. No data custody
+2. No automation of judgment
+3. No opacity
+4. No default AI
+
+This sharply contrasts with commercial AI search tools.
+
+---
+
+## III. Evidentiary Boundaries
+
+By separating:
+- Source material
+- Organizational metadata
+- Analytical commentary
+
+the system preserves traditional evidentiary doctrine.
+
+---
+
+## IV. Institutional Trust
+
+Courts, journalists, and agencies rely on predictability.
+Explicit AI disclosures and hashes restore that predictability.
+
+---
+
+## V. Implications for FOIA Reform
+
+Rather than expanding AI authority, policymakers should:
+- Standardize reading rooms
+- Improve metadata
+- Preserve human accountability
+
+---
+
+## VI. Conclusion
+
+> The future of transparency does not require smarter machines—
+only better boundaries.
+
+Federal FOIA Intelligence Search demonstrates that such boundaries are
+both feasible and effective.