Human-in-Loop Controls | TrustRail - Human Oversight for AI Systems

Why Human Oversight Matters

The Problem with Fully Automated AI:

• No Human Review: High-risk decisions made without human verification
• No Override Capability: Can't intervene when AI makes wrong decision
• Regulatory Violations: EU AI Act requires human oversight for high-risk systems
• NYC LL144 Non-Compliance: Candidates can't request human review of hiring decisions

HOI Playbook Solution:

• Mandatory Review Workflows: High-risk decisions routed to humans
• Override Mechanisms: Humans can intervene and change AI decisions
• Complete Audit Trail: Every human review logged with rationale
• EU & NYC Compliant: Meets Article 14 and LL144 requirements

Real-World Human Oversight Failures:

HR Tech - Automated Rejection:

"Our AI rejected a qualified candidate. NYC LL144 says candidates can request human review, but we had no process for this. Candidate sued. Settlement: $500K plus we had to rebuild our entire hiring system."

Issue: No human review capability for AI hiring decisions

Healthcare - Diagnosis AI:

"AI recommended withholding treatment. Doctor couldn't override the system - it was locked in. Patient deteriorated. Lawsuit alleged we violated duty of care by not allowing physician override."

Issue: No human intervention mechanism

Regulatory Requirements for Human Oversight:

•

EU AI Act Article 14: High-risk AI systems shall be designed to enable human oversight. Users shall be able to fully understand AI system capacities and limitations, be able to monitor operation, and be able to intervene or interrupt the system.

•

NYC LL144 Section 5-302: Automated employment decision tools used for screening or selection must allow for alternative selection process or accommodation for individuals who request it.

•

Penalties: EU AI Act up to €15M or 3% global revenue for Article 14 violations. NYC LL144 $500-1,500 per violation.

What You Get

Complete human oversight infrastructure

🔄

Review Workflows

✓ Risk-based routing (high-risk → human review)
✓ Review assignment and queues
✓ SLA tracking and escalation
✓ Approve/reject/modify options
✓ Rationale capture required

⚙️

Override Mechanisms

✓ Human can change AI decision
✓ Override authority documented
✓ Justification required
✓ Escalation workflows
✓ Emergency stop capability

📊

Audit Trails

✓ Every review logged
✓ Reviewer identity captured
✓ Decision rationale stored
✓ Time spent on review tracked
✓ Override patterns analyzed

Implementation Timeline

1

Week 1: Design

Identify high-risk decisions, design review workflows, define authority levels

2

Week 2: Integration

Build review interfaces, integrate with AI systems, configure routing rules

3

Week 3: Launch

Train reviewers, go live with oversight, monitor performance

Why TrustRail is Different

Purpose-built human oversight vs ad-hoc manual review

Capability	Ad-Hoc Manual Review	TrustRail (HOI Playbook)
Review Process	❌ Informal, inconsistent No defined workflow or criteria	✓ Structured review workflow Risk-based routing, SLA tracking
Override Capability	❌ Requires developer intervention Can't change AI decision in real-time	✓ One-click override Authorized users can modify AI decision
Audit Trail	❌ Emails and spreadsheets No systematic logging	✓ Complete audit trail Every review logged with rationale
NYC LL144 Compliance	❌ No candidate request mechanism Violates LL144 alternative process requirement	✓ Candidate request workflow built-in LL144 Section 5-302 compliant
EU AI Act Article 14	❌ Not designed for regulatory compliance Missing intervention capabilities	✓ Article 14 compliant by design Monitoring, understanding, intervention capabilities
Setup Time	❌ 4-6 months custom development Build review system from scratch	✓ 4-6 weeks with professional setup Pre-built oversight workflows

📚

HOI Playbook Foundation

Built for EU AI Act Article 14 and NYC LL144 compliance

🎯

Risk-Based Automation

High-risk decisions automatically routed for human review

⚡

2-3 Week Launch

Workflow design, integration, reviewer training

How Human-in-Loop Controls Work

HOI Playbook provides comprehensive oversight implementation

Oversight Process

Our HOI Playbook implements structured human oversight:

Risk Detection

Identify high-risk decisions requiring human review

Automatic Routing

Route decisions to qualified reviewers based on risk level

Human Review

Reviewer examines AI decision, can approve, reject, or modify

Override Capability

Authorized users can intervene and change AI recommendations

Audit Logging

All reviews logged with reviewer, decision, rationale, timestamp

Deliverables

Our HOI Playbook produces compliant oversight system:

✓ Review Workflow Configuration

Routing rules, assignment logic, SLA definitions

✓ Override Interface

User interface for reviewers to modify AI decisions

✓ Audit Trail System

Comprehensive logging of all human oversight activities

✓ Training Materials

Reviewer training guides, decision criteria, escalation procedures

💡 EU AI Act Article 14 and NYC LL144 Section 5-302 compliant

2 Compliance Checks Addressed

EU AI Act (1 check)

EU-009: Human Oversight

Article 14 - High-risk AI systems designed to enable human oversight

NYC LL144 (1 check)

NYC-004: Alternative Selection Process

Section 5-302 - Allow alternative process for individuals who request it

HOI Playbook Structure

Human Oversight Implementation methodology

Playbook Components

📖

Review Workflow Templates

Pre-built routing logic, assignment rules, SLA configurations
📋

Override Mechanisms

Technical patterns for human intervention capabilities
✓

Audit Trail Requirements

Logging standards for regulatory compliance
🎯

2 Compliance Requirements

EU-009, NYC-004
📊

Training Materials

Reviewer guides, decision criteria, escalation procedures

Why HOI Works

✓
EU AI Act Article 14 Compliant: Enables monitoring, understanding, intervention
✓
NYC LL144 Compliant: Provides alternative selection process mechanism
✓
Risk-Based Automation: Only high-risk decisions require review
✓
Complete Audit Trail: Every oversight activity logged for regulators