Government and public sector scenarios
Government and public sector scenarios — citizen services, benefits, FOIA, procurement, public-safety analytics. Industry patterns, not customer case studies.
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:
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
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