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.

---

## 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

---

## Novel Design Elements

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

---

## Methodology

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

---

## Legal & Ethical Safeguards

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

---

## Limitations

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

---

## Implications

This architecture demonstrates a viable middle ground between:
- Manual FOIA research
- Fully automated (and risky) AI legal tools

---

## Suggested Citation

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

---

## Conclusion

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