# 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**.