# 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.

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## 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.

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## 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.

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## III. Evidentiary Boundaries

By separating:
- Source material
- Organizational metadata
- Analytical commentary

the system preserves traditional evidentiary doctrine.

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## IV. Institutional Trust

Courts, journalists, and agencies rely on predictability.
Explicit AI disclosures and hashes restore that predictability.

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## V. Implications for FOIA Reform

Rather than expanding AI authority, policymakers should:
- Standardize reading rooms
- Improve metadata
- Preserve human accountability

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## 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.