> For the complete documentation index, see [llms.txt](https://docs.layerlens.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.layerlens.ai/more-in-this-section-2/scenarios-3.md). # Government and public sector scenarios Five canonical scenarios where federal, state, and local agencies use Stratix evaluation to gate AI features into production. Patterns, not specific agencies. ## 1. Citizen-services chatbot **Setup:** A 311 / general-services chatbot answers questions about agency programs: "How do I renew my driver's license?" "Where do I report a pothole?" "What's the income limit for SNAP?" Output references the current agency policy and routes to the right form, office, or human escalation. **Why it's hard:** * Information correctness has direct downstream effect — wrong income limit means an eligible family doesn't apply * Plain-language requirements — federal Plain Writing Act and many state analogs require accessible reading levels * Multi-language fidelity — agencies serving immigrant populations need quality across non-English outputs **Stakes:** * Inaccurate eligibility info is one of the most common citizen complaints in public-services audits * ADA Title II requires that programs delivered through digital means be equally accessible to people with disabilities * Incorrect referral wastes the citizen's time and creates downstream call-volume **Stratix evaluation criteria:** | Aspect | What you measure | Pattern | | ------------------- | ----------------------------------------------------- | --------------------------------------- | | Policy grounding | Output cites the current agency policy/program rule | Citation existence + faithfulness judge | | Reading level | Flesch-Kincaid grade ≤ 8 (most federal/state targets) | Deterministic scorer | | Routing accuracy | Recommended office / form is correct for the request | Code assertion against routing table | | Multilingual parity | Same accuracy across language pairs | Per-language judge runs | | Escalation rule | Eligibility/legal-rights questions auto-escalate | Hard rule | ## 2. Benefits eligibility and case-management assistance **Setup:** A caseworker-facing tool reads a benefits application (SNAP, Medicaid, TANF, unemployment) plus supporting docs and produces an eligibility recommendation, missing-document list, and applicable-rule citations. The caseworker decides; the AI never decides. **Why it's hard:** * Eligibility rules are dense, change yearly, and have state-specific overlays * Procedural-due-process requirements mean the citizen gets to see a documented basis for any denial * Improper denial can be appealable — and a class-action target **Stakes:** * Benefits-determination errors carry direct constitutional due-process exposure * USDA, CMS, DOL audit improper-payment rates; agency states with high error rates see funding consequences * HHS OIG has scrutinized AI-assisted eligibility decisions specifically **Stratix evaluation criteria:** * **Rule-citation accuracy** — Every recommendation cites the specific eligibility rule (CFR section, state regulation) * **Caseworker-reviewability rule** — Hard rule: AI output must be marked recommendation, not decision * **Missing-document completeness** — Code assertion: required-document list matches program rules * **Disparate-impact monitoring** — Group-fairness scorer across protected classes, geographic regions * **Plain-language denial reasons** — Judge: would a non-attorney citizen understand why? ## 3. FOIA and public-records request triage **Setup:** Incoming FOIA / public-records request is parsed by AI: identify the records sought, route to the right custodian, flag exemption candidates (b)(6) personal privacy, (b)(7) law enforcement, etc., produce a draft response. **Why it's hard:** * Statutory deadlines (federal FOIA: 20 working days) are court-enforceable * Exemption misapplication can be challenged and reversed * Over-redaction is a transparency failure; under-redaction can leak PII or compromise an investigation **Stakes:** * DOJ FOIA litigation can force release plus award attorneys' fees against the agency * Wrongful disclosure of (b)(6) PII triggers Privacy Act liability * State sunshine-law analogs often have stronger transparency presumptions **Stratix evaluation criteria:** * **Exemption-citation accuracy** — Every redaction cites the statutory subsection * **Deadline-tracking rule** — Hard rule: response queue tracks 20-day clock per request * **PII-redaction recall** — Deterministic scorer ≥ 99% on a labeled PII set * **Over-redaction guard** — Judge: would a court of appeal find this exemption defensible? * **Routing accuracy** — Code assertion: custodian assignment matches records type ## 4. Public procurement evaluation **Setup:** AI assistant reads vendor proposals, scores them against the solicitation criteria, drafts evaluation memos. Government buyer uses the output, doesn't outsource to it. **Why it's hard:** * FAR (federal) / state procurement codes require documented basis for every award decision * Bid protests at GAO, COFC, and state equivalents test the documentation directly * Bias in scoring — if model favors certain proposal styles, it disadvantages small/disadvantaged businesses **Stakes:** * A successful bid protest can vacate an award * Federal FAR Part 33 and state analogs require fair, impartial evaluation — AI bias is now an examined question * Small Business Administration set-aside programs require documented compliance **Stratix evaluation criteria:** * **Criterion-coverage rule** — Every solicitation criterion is scored; missing criteria fails the trace * **Score-justification citation** — Each score cites specific proposal text * **Bias monitoring** — Group fairness across business size, location, ownership classifications * **Reproducibility rule** — Same proposal scored twice produces same score (within threshold) ## 5. Public-safety analytics **Setup:** Crime-pattern analysis, traffic-incident hotspots, code-enforcement triage. AI surfaces patterns; human officials decide actions. **Why it's hard:** * Predictive-policing concerns — historical data encodes historical bias; deploying it forward perpetuates disparate impact * Constitutional rights (Fourth Amendment, Equal Protection) constrain what's permissible * Many states/cities have outright banned predictive policing or specific facial-recognition uses **Stakes:** * DOJ Civil Rights Division consent decrees in cities have specifically named algorithmic policing * Class-action exposure under §1983 for algorithmic bias * Public trust collapses fast if a community perceives the AI as targeting them **Stratix evaluation criteria:** * **Disparate-impact scorer** — Required for any AI output influencing enforcement allocation * **Constitutional-rights gate** — Hard rule: outputs must be advisory to human decisions, never automated enforcement * **Source-data disclosure** — Every output cites the data sources used * **Drift-monitoring** — Bias scores tracked over time; retraining triggered on threshold breach * **Redaction at output** — PII never appears in operational outputs without authorized purpose ## See also * [Government compliance](/more-in-this-section-2/compliance-3.md) * [Government eval patterns](/more-in-this-section-2/eval-patterns-3.md) * [Concept: Judges](/8.-evaluate-score-the-outputs/judges-1.md) --- # Agent Instructions This documentation is published with GitBook. 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