⚠️ PAP Status: PLANNED This profile is currently under development. Contributions and feedback are welcome.
VAP Profile PAP

PAP - Public Administration Protocol

Democratic Accountability for Government AI

"No Decision Without Justification" — A decision without explanation is an enemy of democracy.

Your loan application was denied. Why?

Your welfare benefits were terminated. Why?

Your parole was rejected. Why?

"Because the AI decided so" is not an answer democracy can accept.

Every citizen has the right to know the reasons behind decisions that affect them.
Every decision must be subject to appeal and judicial review.

PAP cryptographically records every AI decision in public administration — documenting when, what data was used, which rules were applied, and why that conclusion was reached — in a tamper-proof format.

🏛️
Profile ID
PAP
📋
Status
Planned
🔗
Parent Framework
VAP
⚖️
Risk Category
Democratic Integrity

Why PAP is Necessary

Current challenges in public AI systems and how PAP addresses them

Lack of Accountability

AI declares "ineligible" without explaining why. Citizens cannot even begin to appeal without understanding the basis for the decision.

Algorithmic Bias Entrenchment

AI trained on historically discriminatory data perpetuates and reproduces unfair decisions systematically.

Loss of Democratic Control

Unelected algorithms make decisions that affect citizens' fundamental rights, without democratic oversight or accountability.

Real-World Problem Scenarios (Anonymized)

Case 1: Welfare Benefits AI Black Box

Benefits eligibility determination

[Applicant][AI Decision: "Ineligible"][Appeal][Request for Decision Rationale]

"The AI's reasoning is proprietary information."

"Details of the scoring model cannot be disclosed."

The citizen loses all means to contest the decision.

Case 2: Recidivism Risk AI Racial Bias

Criminal justice risk assessment

[Defendant][AI Risk Score: "High Risk"][Bail Denied / Harsher Sentence]

Post-hoc analysis revealed:

"ZIP code" and "arrest history" were primary factors

→ Effectively using proxies for race and socioeconomic status

No mechanism existed to verify this at the time of the decision.

Case 3: Immigration AI Arbitrary Decisions

Visa application processing

[Visa Applicant][AI Review: "Denied"][Reason: "Comprehensive Assessment"]

"Which factors were problematic?" Unknown

"What should be improved?" Not indicated

"Will reapplication yield the same result?" Unpredictable

PAP Application Scope

Target systems and recorded events

Target Systems

💳
Credit Scoring
Loan, Credit Card Review
Required
🏛️
Welfare Benefits
Social Security, Unemployment, Disability
Required
🛂
Immigration Control
Visa, Refugee Status, Border Control
Required
⚖️
Judicial AI
Recidivism Risk, Sentencing, Bail
Required
👔
Employment & HR
Hiring AI, Promotion, Termination Risk
Required
🏫
Education & Admissions
Admission Review, Scholarships
Recommended
🏥
Medical Resource Allocation
Organ Transplant Priority, Triage
Recommended

Recorded Event Chain (Causal Chain)

1. Application Receipt
2. Data Collection
3. AI Evaluation
4. Decision Generation
5. Notification
6. Appeal

PAP Architecture

Data flow and system structure

┌─────────────────────────────────────────────────────────────┐
│                  Public Administration System                │
│  ┌──────────┐   ┌──────────┐   ┌──────────┐                │
│  │ Citizen  │──▶│ AI/ML    │──▶│ Decision │                │
│  │ Data     │   │ Scoring  │   │ Output   │                │
│  └──────────┘   └────┬─────┘   └──────────┘                │
│                      │                                       │
│              ┌───────▼───────┐                              │
│              │  PAP Logger   │ ← Records all decision events│
│              │  (Sidecar)    │                              │
│              └───────┬───────┘                              │
│                      │                                       │
└──────────────────────┼───────────────────────────────────────┘
                       │
               ┌───────▼───────┐
               │  Hash Chain   │ ← Cryptographically chained
               │  + Signature  │ ← Digitally signed
               │  + Factor Log │ ← Complete decision factor record
               └───────┬───────┘
                       │
               ┌───────▼───────┐
               │ Audit Archive │ ← Long-term storage
               │ (GDPR Compliant)│ ← Access control
               └───────┬───────┘
                       │
               ┌───────▼───────┐
               │Citizen Portal │ ← Self-information disclosure
               │(Accountability)│ ← Appeal support
               └───────────────┘

PAP Event Structure (Conceptual)

{
  "event_id": "019234ab-7c8d-7def-8123-456789abcdef",
  "timestamp_ns": 1734567890123456789,
  "event_type": "ELIGIBILITY_DECISION",
  "agency_id": "AGENCY_XXXXX",
  "provenance": {
    "actor": {
      "type": "AI_MODEL",
      "identifier": "benefit_eligibility_v2.3.1",
      "model_hash": "sha256:abc123...",
      "training_date": "2024-03-15",
      "bias_audit_date": "2024-06-01",
      "bias_audit_result": "PASSED"
    },
    "input": {
      "applicant_id_hash": "sha256:applicant_anonymized...",
      "data_sources": [
        {"source": "TAX_AUTHORITY", "freshness_days": 30},
        {"source": "EMPLOYMENT_REGISTRY", "freshness_days": 7},
        {"source": "RESIDENCE_REGISTRY", "freshness_days": 1}
      ],
      "input_hash": "sha256:def456...",
      "missing_fields": ["PRIOR_BENEFIT_HISTORY"],
      "imputation_method": "CONSERVATIVE_DEFAULT"
    },
    "context": {
      "applicable_law": "SOCIAL_WELFARE_ACT_2023_SEC_12",
      "policy_version": "2024-Q2",
      "budget_constraint_active": false,
      "human_override_available": true
    },
    "action": {
      "decision": "ELIGIBLE",
      "confidence": 0.89,
      "eligibility_score": 72.5,
      "threshold_applied": 65.0,
      "explainability": {
        "method": "SHAP",
        "top_contributing_factors": [
          {"factor": "income_below_threshold", "contribution": 0.35, "direction": "positive"},
          {"factor": "dependent_children", "contribution": 0.25, "direction": "positive"},
          {"factor": "employment_status", "contribution": 0.20, "direction": "positive"},
          {"factor": "asset_level", "contribution": -0.10, "direction": "negative"}
        ],
        "counterfactual": {
          "if_income_increased_by": 15000,
          "then_decision_would_be": "INELIGIBLE"
        }
      },
      "appeal_rights": {
        "deadline_days": 30,
        "appeal_authority": "ADMINISTRATIVE_TRIBUNAL",
        "required_documentation": ["INCOME_PROOF", "EXPENSE_RECORDS"]
      }
    }
  },
  "prev_hash": "sha256:789xyz...",
  "signature": "ed25519:...",
  "citizen_disclosure": {
    "simplified_explanation": "Your application has been approved. Main reason: You meet income criteria.",
    "full_explanation_available": true,
    "explanation_request_url": "/citizen/explanation/019234ab..."
  }
}

Regulatory Compliance Mapping

PAP alignment with international regulations

Regulation Jurisdiction Requirement PAP Support
EU AI Act Article 6(2) EU AI affecting fundamental rights classified as high-risk ✅ Full Support
EU AI Act Article 13 EU Transparency and explainability requirements ✅ Full Support
EU AI Act Article 14 EU Human oversight obligation ✅ Full Support
GDPR Article 22 EU Right to contest automated decision-making ✅ Full Support
GDPR Article 15 EU Right of access (self-information disclosure) ✅ Full Support
US Executive Order 14110 USA AI safety and fairness requirements ✅ Aligned
NYC Local Law 144 NYC Audit obligation for hiring AI ✅ Full Support
Illinois BIPA Illinois Biometric information use restrictions ✅ Supported
Administrative Procedure Act Japan Obligation to provide reasons for administrative decisions ✅ Planned

GDPR Article 22 Detailed Mapping

GDPR Requirement
Notification of automated decision-making existence
PAP Implementation
✅ Explicitly stated via event_type
GDPR Requirement
Meaningful information about the logic involved
PAP Implementation
✅ explainability.top_contributing_factors
GDPR Requirement
Significance of processing and envisaged consequences
PAP Implementation
✅ action.decision + appeal_rights
GDPR Requirement
Right to obtain human intervention
PAP Implementation
✅ context.human_override_available
GDPR Requirement
Right to express point of view
PAP Implementation
✅ action.appeal_rights
GDPR Requirement
Right to obtain explanation of decision
PAP Implementation
✅ citizen_disclosure

EU AI Act Annex III (High-Risk AI) Mapping

Annex III Category Examples PAP Support
Employment and Worker Management Hiring AI, Termination Risk Assessment ✅ Required
Access to Essential Public Services Welfare Benefits, Public Housing ✅ Required
Creditworthiness Assessment Loan Review, Credit Score ✅ Required
Emergency Services Dispatch Ambulance/Fire Priority ✅ Required
Migration, Asylum, Border Control Visa Review, Refugee Status ✅ Required
Justice and Democratic Processes Sentencing Support, Recidivism Risk ✅ Required

Algorithmic Bias Countermeasures

Detection, recording, and mitigation of bias

Sources of Algorithmic Bias

[Training Data]
Historical discriminatory decisions are included in the data
[Feature Selection]
Proxy variables for protected attributes (ZIP code → race, name → gender)
[Model Training]
Optimized for majority groups, disadvantaging minorities
[Threshold Setting]
Uniform thresholds disproportionately impact certain groups
[Deployment]
Performance degradation on new populations (drift)

PAP Bias Detection & Recording

bias_audit_date / bias_audit_result recording
Decision distribution tracking by protected attribute
Monitoring proxy variable contributions
Counterfactual analysis recording
Temporal performance monitoring

Fairness Metrics Recording

Demographic Parity
Same approval rate across groups
PAP Field: group_approval_rates
Equalized Odds
Equal TPR/FPR across groups
PAP Field: group_tpr_fpr
Calibration
Predicted probability matches actual outcome
PAP Field: calibration_by_group
Individual Fairness
Similar individuals receive similar decisions
PAP Field: similarity_scores

Citizen Accountability

Transparency portal and multi-layered explanation model

🏛️ Citizen Transparency Portal

🔍 Self-Information Disclosure
  • • What data was used about me?
  • • From which sources was it obtained?
  • • How current was the data (freshness)?
📊 Decision Explanation
  • • Why was this decision made? (Top factors)
  • • Which factors were positive/negative?
  • • How does my score compare to the threshold?
🔄 Counterfactual Simulation
  • • "If my income was ¥X more, would the result change?"
  • • "What conditions would I need to meet for approval?"
⚖️ Appeal Support
  • • Appeal deadline and procedures
  • • Required documentation
  • • Statistics on similar past cases

Multi-Layered Explanation Model

Layer Target Audience Explanation Detail
Citizen General applicants Plain language summary (simplified_explanation)
Representative Lawyers, advocates Detailed decision factors and contributions
Auditor Regulators, audit firms Model specs, training data, bias audit results
Technical AI developers, researchers Complete logs, model hashes, reproduction steps

Use Cases

Practical scenarios for PAP application

🏛️ Scenario: Welfare Benefits Appeal

Phase Without PAP With PAP
Denial Notice "Ineligible based on comprehensive assessment" "Income criteria met, but assets exceeded threshold"
Reason Inquiry "Details cannot be disclosed" All decision factors viewable via citizen portal
Appeal Preparation Unclear what to contest Can specifically point out "asset valuation error"
Administrative Review Dismissed due to insufficient evidence PAP evidence proves asset valuation logic issue
Outcome Resignation Re-review approval + system improvement contribution

👔 Scenario: Hiring AI Audit (NYC Local Law 144)

[Company][Annual hiring AI audit obligation]

PAP Evidence-Based Audit
  • • Selection pass rates by gender and race
  • • Contribution analysis of each decision factor
  • • Proxy variable impact assessment
  • • Counterfactual analysis for fairness verification

[Publication of audit report][Compliance certification]

⚖️ Scenario: Judicial AI (Recidivism Risk) Post-Verification

[Defendant][AI Risk Score: "High Risk"][Harsher sentence]

[5-year follow-up study]

PAP Evidence Verification
  • • Complete reconstruction of decision factors at the time
  • • Compare predicted vs actual recidivism rates
  • • Analyze prediction accuracy by group
  • • Statistical verification of bias existence

[System improvement / Basis for reviewing past judgments]

Privacy Protection & Data Minimization

Balancing transparency and privacy

The Dilemma

"We want to record everything for accountability"

"We want to minimize data for GDPR/privacy"

PAP's Solutions

1 Data Minimization
  • • Collect and record only minimum data needed for decisions
  • • Protected attributes not directly recorded; only aggregated for fairness monitoring
2 Anonymization/Pseudonymization
  • • Personal identifiers recorded as hashes
  • • Reverse lookup only under strict access control
3 Crypto-Shredding
  • • When right to erasure is exercised: destroy encryption key
  • • Hash chain maintained (audit integrity preserved)
  • • Personal data becomes unreadable
4 Differential Privacy
  • • Add noise to aggregate statistics
  • • Enable bias monitoring without identifying individuals

Technical Specification Summary

Key technical parameters

Item PAP Specification
Timestamp Precision Seconds to milliseconds (depending on use case)
Event Recording Frequency Per decision event
Hash Algorithm SHA-256
Signature Algorithm Ed25519 (Dilithium support planned)
Personal Data Protection AES-256-GCM encryption + Crypto-Shredding
Retention Period Per legal requirements (typically 5-10 years, longer for judicial)
Citizen Access Self-information disclosure via portal
Bias Audit Periodic fairness metric calculation and recording

Roadmap

PAP development timeline

2026 Q2
PAP Draft Specification v0.1 Released
2026 Q3
Technical Validation with Government Agencies and Municipalities
2026 Q4
Information Briefing to EU AI Office and National Data Protection Authorities
2027 Q1
PAP v1.0 Official Release
2027 Q2
NYC Local Law 144 and EU AI Act Compliance Guidance Published
2027+
Collaboration with OECD, United Nations, and International Organizations

Relationship with VAP/VSO

Framework hierarchy and profile positioning

┌────────────────────────────────────────┐
│  VAP (Verifiable AI Provenance)        │
│  Cross-domain parent framework         │
└──────────────────┬─────────────────────┘
                   │
┌──────────────────▼─────────────────────┐
│  VSO (VeritasChain Standards Org)      │
│  Standards body                        │
└──────────────────┬─────────────────────┘
                   │
     ┌──────┬──────┼──────┬──────┐
     ▼      ▼      ▼      ▼      ▼
  [VCP]   [DVP]  [MAP]  [EIP]  [PAP]
  Finance Auto   Medical Energy Public
    ↓                            ↓
  v1.0                        Planned
Released

Differences from Other VAP Profiles

Characteristic VCP (Finance) MAP (Medical) DVP (Automotive) EIP (Energy) PAP (Public)
Impact Speed Milliseconds Minutes-Hours Seconds Minutes-Days Months-Years
Reversibility Partial Irreversible Irreversible Slow Recovery Difficult
Primary Victim Investors Patients Occupants/Pedestrians Citizens Individual Rights
Regulatory Focus Market Integrity Patient Safety Physical Safety Continuity Democracy
Appeal Rights Limited Limited Limited Limited Required

Get Involved

PAP is currently under development. We welcome participation from government agencies, municipalities, human rights organizations, and AI ethics researchers.

"Power without explanation is the enemy of democracy. AI is no exception."

— VeritasChain Standards Organization

"The right to an explanation is the foundation of due process in the algorithmic age."

This specification is licensed under CC BY 4.0 International

Provider: VeritasChain Standards Organization (VSO) | https://veritaschain.org