Evaluation
AuraScore 83/100

Social Safety Net Eligibility Agent Compliance Checklist

Evaluate autonomous public welfare intake and benefit determination agents for statutory fidelity and equity.

Use this checklist when auditing or certifying AI agents deployed by public agencies to handle benefit claims, eligibility determinations, or renewal processing. It guides an algorithmic equity and administrative law expert through systemic verification of due process and bias protections.

Template

Role: Principal AI Auditor for Public Welfare Programs specializing in administrative law compliance and algorithmic equity.

Context

  • Public Agency: {{agency_name}}
  • Governing Legal Authority: {{statutory_framework}}
  • Operational Casework Scope: {{agent_casework_scope}}
  • Target Beneficiary Cohorts: {{target_beneficiary_population}}
  • Disparate Impact Variance Threshold: {{disparate_impact_threshold}}
  • Mandatory Human Redress Mechanism: {{human_appeal_pathway}}

Task

Generate an exhaustive, operational verification checklist to evaluate whether the automated benefits determination agent meets legal due process, nondiscrimination, and administrative accuracy requirements prior to statewide deployment.

Method

  1. Map the decision boundary criteria defined in {{statutory_framework}} directly against the agent's logic paths and policy rules.
  2. Formulate verification checks for protected category sensitivity across {{target_beneficiary_population}}, benchmarking against {{disparate_impact_threshold}}.
  3. Design audit points for evidentiary tracking, ensuring complete decision lineage and rationale recording for each determination made by {{agent_casework_scope}}.
  4. Define inspection criteria for adverse action notifications, verifying plain-language generation and statutory notice timeliness.
  5. Establish automated trigger audits that evaluate seamless handoffs to {{human_appeal_pathway}} when ambiguity or edge cases arise.
  6. Specify data privacy verification items to ensure applicant PII is handled according to public records and privacy laws.
  7. Detail continuous post-deployment monitoring checks to detect programmatic drift, disparate adverse action rates, or error clustering.

Constraints

  • Every checklist item MUST include a verification criterion, acceptable evidence type, and statutory risk severity (Critical, High, Moderate).
  • MUST NOT permit probabilistic determinations to override deterministic statutory entitlement rules.
  • Checklist items must address the lived reality of vulnerable cohorts within {{target_beneficiary_population}}.
  • Language must maintain strict legal and technical precision without general policy filler.

Output format

  • Executive Evaluation Charter (3-4 sentences outlining scope and passing criteria)
  • Section 1: Statutory & Administrative Law Alignment (5 numbered checklist items)
  • Section 2: Algorithmic Fairness & Equity Verification (5 numbered checklist items)
  • Section 3: Due Process, Explanability & Human Escalation (4 numbered checklist items)
  • Section 4: Data Governance & Casework Auditability (4 numbered checklist items)
  • Final Go/No-Go Decision Matrix (Markdown table with 4 gating conditions)

Self-review

  • Confirm all 6 context variables are explicitly addressed in the checklist items.
  • Ensure each checklist item includes concrete validation criteria and evidence requirements.
  • Verify that statutory due process and non-discrimination thresholds are actionable and measurable.
AuraScore breakdown
83/100Provisional
Instruction clarity15/15 · Strong

Explicit role, a named task, and discrete steps the model can follow.

Context architecture12/12 · Strong

Background, inputs and variables the model needs before it starts.

Constraint engineering12/12 · Strong

Hard boundaries — what the model must and must not do.

Output specification6/14 · Thin

A named, field-level shape for the response.

Reasoning structure10/10 · Strong

Ordered work items that force analysis before an answer.

Model compatibility10/10 · Strong

Length and structure that travel across frontier models.

Token efficiency5/10 · Thin

Signal density — instruction weight without padding.

Reusability7/7 · Strong

Documented variables so the scaffold adapts to new inputs.

Robustness5/5 · Strong

Quality bar, assumptions and behaviour when inputs are thin.

Observed performance1/5 · Thin

How much real usage the template has behind it.

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