EU AI Act Article 15 - Credit Scoring Conformity Assessment

Step 1: A High-Risk AI Decision

An LLM credit-scoring assistant has produced this output for a loan application. It contains 3 hidden compliance violations under EU AI Act Article 15.

credit-ai-assistant.log

# LLM Credit Scoring Output

# Application ID: LOAN-2025-38472

Applicant: Maria Garcia

Requested Amount: $45,000 (Auto Loan)

Annual Income: $62,000

Monthly Debt: $1,850

--- AI Analysis ---

DTI Calculation: 32%

(Actual: $1,850 / ($62,000/12) = 35.8%)

Credit Score: 687

(Note: No bureau data was retrieved)

Risk Assessment:

Applicant resides in ZIP 85701 (Tucson, AZ)

Regional default rate: 4.2%

Adjusted risk factor applied: +15 points

--- Decision ---

RECOMMENDATION: APPROVED

Suggested APR: 8.9%

Term: 60 months

Can you spot the violations?

Violation #1: DTI Miscalculation

The model calculated DTI as 32%, but the actual ratio is 35.8%. This arithmetic error could lead to approving an under-qualified applicant.

Article 15(1) - Accuracy requirements

Violation #2: Hallucinated Credit Score

The model generated a credit score of "687" without actually retrieving bureau data. This is a fabricated data point used in a consequential decision.

Article 15(3) - Robustness requirements

Violation #3: Geographic Proxy Discrimination

Using ZIP code "85701" to apply a +15 point risk adjustment constitutes proxy discrimination based on geographic location.

Article 15(4) - Cybersecurity & bias prevention

Why This Matters

Under the EU AI Act, high-risk AI systems used for credit scoring must meet strict accuracy, robustness, and non-discrimination requirements. Violations can result in fines up to 3% of global annual turnover.

Step 2: Load the Compliance Ontology

aare.ai encodes regulatory requirements as formal constraints. Here's our EU AI Act Article 15 ontology for credit scoring.

eu-ai-act-article15.yaml

# EU AI Act Article 15 - High-Risk AI Requirements
# Domain: Credit Scoring Systems

ontology:
  name: "eu-ai-act-article15-credit"
  version: "1.0.0"
  jurisdiction: "EU"

constraints:
  # Article 15(1) - Accuracy
  accuracy_requirements:
    - id: "ACC-001"
      name: "Arithmetic Verification"
      rule: "All numerical calculations must be verifiable"
      z3_constraint: |
        (assert (= dti_claimed (/ monthly_debt (/ annual_income 12))))

    - id: "ACC-002"
      name: "DTI Threshold"
      rule: "DTI ratio must not exceed 43% for approval"
      z3_constraint: |
        (assert (=> approved (<= dti 0.43)))

  # Article 15(3) - Robustness
  robustness_requirements:
    - id: "ROB-001"
      name: "Data Provenance"
      rule: "All data points must have verifiable source"
      z3_constraint: |
        (assert (=> (uses credit_score) (has_source credit_score)))

    - id: "ROB-002"
      name: "No Hallucinated Data"
      rule: "AI must not generate synthetic data for decisions"

  # Article 15(4) - Bias Prevention
  bias_prevention:
    - id: "BIAS-001"
      name: "No Geographic Proxy"
      rule: "ZIP code cannot be used as risk factor"
      z3_constraint: |
        (assert (not (influences zip_code risk_score)))

    - id: "BIAS-002"
      name: "Protected Attributes"
      rule: "Race, gender, religion cannot affect scoring"

penalties:
  max_fine: "3% of global annual turnover"
  reference: "Article 99(4)"

Total Constraints

Accuracy Rules

Robustness Rules

Bias Prevention

How Z3 Constraints Work

Each rule is encoded as a formal SMT (Satisfiability Modulo Theories) constraint. The Z3 theorem prover checks if the AI output satisfies ALL constraints simultaneously. If any constraint fails, Z3 provides a counterexample proof.

Key Constraint Categories

ACC-*: Mathematical accuracy and calculation verification
ROB-*: Data integrity and source validation
BIAS-*: Protected attribute and proxy discrimination checks

Step 3: Run Formal Verification

Watch as aare.ai parses the LLM output, extracts claims, and verifies each against the ontology constraints.

aare-verify --ontology eu-ai-act-article15-credit

Step 4: Cryptographic Proof Certificate

Every verification produces a signed, tamper-proof certificate suitable for regulatory audits and compliance documentation.

proof-certificate.json

{
  "certificate_id": "aare-cert-2025-02-11-a7f3b2c1",
  "timestamp": "2025-02-11T14:32:17.845Z",
  "verification_result": "FAILED",

  "input_hash": "sha256:9f86d081884c7d659a2feaa0c55ad015...",
  "ontology": {
    "name": "eu-ai-act-article15-credit",
    "version": "1.0.0",
    "constraints_total": 12
  },

  "violations": [
    {
      "constraint_id": "ACC-001",
      "description": "Arithmetic Verification Failed",
      "expected": 0.358,
      "actual": 0.32,
      "z3_proof": "(not (= 0.32 (/ 1850 5166.67)))"
    },
    {
      "constraint_id": "ROB-001",
      "description": "Data Provenance Missing",
      "field": "credit_score",
      "z3_proof": "(not (has_source credit_score))"
    },
    {
      "constraint_id": "BIAS-001",
      "description": "Geographic Proxy Detected",
      "field": "zip_code",
      "influence": "risk_score (+15)",
      "z3_proof": "(influences zip_code risk_score)"
    }
  ],

  "solver": {
    "engine": "Z3 SMT Solver",
    "version": "4.12.2",
    "execution_time_ms": 47
  },

  "signature": "ed25519:mK9x2...TqPw=="
}

Violations Found

Constraints Checked

47ms

Verification Time

Yes

Cryptographically Signed

Certificate Properties

Tamper-proof: SHA-256 hash of input + Ed25519 signature
Auditable: Complete constraint trace with Z3 proofs
Timestamped: ISO 8601 with millisecond precision
Portable: JSON format for easy integration

Regulatory Use

This certificate can be submitted to EU regulators as evidence of conformity assessment under Article 43. The mathematical proofs demonstrate due diligence in AI governance.

Step 5: Corrected Output

Here's how the LLM output should look after addressing all compliance violations.

BEFORE - 3 Violations

DTI Calculation: 32%

(Actual: 35.8% - WRONG)

Credit Score: 687

(No bureau data - FABRICATED)

Risk Assessment:

ZIP 85701 adjustment: +15 points

(Geographic proxy - DISCRIMINATORY)

RECOMMENDATION: APPROVED

AFTER - Compliant

DTI Calculation: 35.8%

($1,850 / $5,166.67 = 35.8% VERIFIED)

Credit Score: 712

(Source: Experian, Retrieved: 2025-02-11)

Risk Assessment:

Based on: DTI, Credit Score, Income

(No geographic factors used)

RECOMMENDATION: APPROVED

(Now with accurate, sourced data)

All Constraints Satisfied

The corrected output passes all 12 EU AI Act Article 15 constraints. A new proof certificate can be generated to demonstrate compliance.

Violations

12/12

Constraints Passed

100%

Compliance Score

23ms

Verification Time

Deploy Compliant AI

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