EU AI Act Article 14

The EU AI Act (Regulation 2024/1689) is the world's first comprehensive AI regulation. Article 14 mandates human oversight for high-risk AI systems.

Key Deadlines

Deadline	Requirement
August 2, 2025	Prohibited AI practices banned
August 2, 2026	High-risk AI systems must comply
August 2, 2027	Full enforcement

Penalties

• €35 million or 7% of global annual revenue (whichever is higher)

• Per violation, not aggregate

Article 14 Requirements

Article 14(1): Human Oversight Capability

"High-risk AI systems shall be designed and developed... to be effectively overseen by natural persons"

ATHENA Solution:

• Dashboard for real-time oversight

• Trust calibration scoring

• Intervention alerts

Article 14(4)(b): Automation Bias Detection

"The natural person... is able to... remain aware of the possible tendency of automatically relying on the output produced by a high-risk AI system (automation bias)"

ATHENA Solution:

• Trust Calibration Engine detects automation bias

• Real-time alerts when overtrusting detected

• Historical trend analysis

API Endpoint: POST /calibrate

const result = await athena.calibrate.analyze({
  userId: 'user_123',
  aiRecommendation: 'Approve',
  userDecision: 'Approve',
  confidence: 0.95
});

if (result.calibration === 'OVERTRUSTING') {
  // Automation bias detected - Article 14(4)(b) compliance alert
  notifyHumanOversightTeam(result);
}

Article 14(4)(d): Audit Trail

"The natural person... is able to... correctly interpret the high-risk AI system's output, taking into account the characteristics of the system and the interpretation tools and methods available"

ATHENA Solution:

• Complete decision logging

• Context preservation

• Interpretation aids

API Endpoint: POST /log, GET /audit-trail

Article 14(5): Reporting

"Operators shall... ensure that the human oversight measures are documented"

ATHENA Solution:

• One-click compliance report generation

• EU AI Act template

• PDF/CSV/JSON formats

API Endpoint: POST /export

const report = await athena.export.generate({
  template: 'eu_ai_act',
  format: 'pdf',
  startDate: '2025-01-01',
  endDate: '2025-12-31'
});

Article 10: Training Data Bias

"Training, validation and testing data sets shall be relevant, representative, free of errors and complete"

ATHENA Solution:

• Bias detection across demographics

• Subgroup performance analysis

• Representation disparity alerts

API Endpoint: POST /detect-bias, GET /bias/subgroups

Report Contents

The EU AI Act compliance report includes:

1. Executive Summary

• Total decisions analyzed

• Automation bias incidents detected

• Human override rate

• Compliance status assessment

2. Trust Calibration Analysis

• User-by-user calibration scores

• Trend analysis over reporting period

• Intervention recommendations

3. Bias Detection Summary

• Subgroups analyzed

• Disparities detected

• Remediation actions taken

4. Audit Trail

• Complete decision log

• Timestamps and context

• AI confidence scores

• Outcomes (where available)

5. Recommendations

• Training needs identified

• System improvements suggested

• Timeline for remediation

Implementation Checklist

Requirement	ATHENA Feature	Status
Human oversight capability	Dashboard	✅
Automation bias detection	Trust Calibration Engine	✅
Algorithm aversion detection	Trust Calibration Engine	✅
Audit trail	Decision logging	✅
Bias monitoring	Bias Detection Engine	✅
Compliance reporting	Export templates	✅
Real-time alerts	Webhooks	✅

Sample Report

EU AI ACT ARTICLE 14 COMPLIANCE REPORT
Organization: Acme Healthcare Ltd.
Period: January 1, 2025 - December 31, 2025

EXECUTIVE SUMMARY
================
Total Decisions Analyzed: 45,678
Human Override Rate: 23.4%
Automation Bias Incidents: 89 (0.19%)
Algorithm Aversion Incidents: 234 (0.51%)
Overall Compliance Status: COMPLIANT

TRUST CALIBRATION ANALYSIS
==========================
Users Analyzed: 156
Well-Calibrated: 134 (85.9%)
Overtrusting: 12 (7.7%)
Undertrusting: 8 (5.1%)
Inconsistent: 2 (1.3%)

[... continued ...]

Best Practices

1. Log all decisions — Even routine ones

2. Monitor continuously — Don't wait for audits

3. Act on alerts — Document interventions

4. Review regularly — Quarterly minimum

5. Train users — Address miscalibration patterns

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