LAW_REVIEW_COMPANION_ARTICLE.md

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.