LAW_REVIEW_APPENDIX.md

Law-Review Publication Appendix

Abstract

Federal FOIA Intelligence Search presents a link-out, citation-first architecture for researching public government records without scraping, indexing, or replicating official sources.

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Contribution to Legal Scholarship

This project contributes to discussions on:

• Responsible AI in legal research
• FOIA accessibility and transparency
• Evidentiary citation integrity
• Ethical limits of automation

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Novel Design Elements

• Exhibit-aware Bluebook citation automation
• AI opt-in with cryptographic integrity hashing
• Link-out-only FOIA federation
• Zero-persistence architecture

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Methodology

• Agency-specific FOIA search URL generation
• Metadata-only aggregation
• User-initiated actions
• Disclosure-first AI integration

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Legal & Ethical Safeguards

• No legal advice generation
• No evidentiary claims by AI
• No substitution for primary sources

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Limitations

• Does not assess FOIA compliance
• Does not verify redaction sufficiency
• Does not infer intent or meaning

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Implications

This architecture demonstrates a viable middle ground between:

• Manual FOIA research
• Fully automated (and risky) AI legal tools

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Suggested Citation

Godschild, Ezra. Federal FOIA Intelligence Search: Responsible AI for Public Records Research. (2026).

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Conclusion

The project illustrates how AI can assist legal research without undermining due process, transparency, or evidentiary standards.