When AI decisions are challenged, accountability matters.
AMEVP – AI Model Explainability & Verification Procedure
Aligned with EU regulatory expectations – referencing the AI Act & GDPR
Context
Artificial Intelligence increasingly drives critical decisions in areas like hiring, pricing and investigations. It’s not enough to simply document these systems; organisations must take responsibility when their algorithms make decisions that affect people's lives. AMEVP bridges the gap between documentation and accountability.
What is AMEVP?
The AI Model Explainability & Verification Procedure (AMEVP) provides a structured approach to ensure that automated decision-making systems are explainable and accountable. It encompasses key capabilities including:
- Explainability: making models interpretable so stakeholders understand how decisions are made.
- Justification: ensuring decisions can be justified against applicable laws and ethical standards.
- Contestability: enabling affected parties to challenge decisions effectively.
- Human responsibility: maintaining human oversight and accountability throughout the AI lifecycle.
- Incident response: preparing protocols to respond to and remediate AI failures or harms.
“Accountability is not a feature you add to AI – it is a commitment you bake into its design.”
How it works
AMEVP guides organisations through five steps:
- Decision Context Identification: Recognise the decision’s impact domain and applicable regulatory frameworks (e.g., AI Act, GDPR).
- Justification & Transparency: Document the rationale behind model choices and decision criteria, ensuring transparency.
- Proportionality: Evaluate whether the use of AI is proportionate to the decision’s significance and risks.
- Contestability & Human Oversight: Establish channels for appeals and ensure human reviewers can intervene when necessary.
- Formal Verification Outcome: Produce a verification report summarising compliance with legal requirements and outlining deliverables such as audit logs, impact assessments and mitigation plans.
Each step references relevant legislation to align your AI system with EU expectations and provides tangible deliverables to demonstrate accountability.
Where it applies
HR & Employment
Typically high‑risk or regulated decision contexts.
Financial Scoring & Eligibility
Typically high‑risk or regulated decision contexts.
Pricing & Differential Treatment
Typically high‑risk or regulated decision contexts.
Customer Access & Account Decisions
Typically high‑risk or regulated decision contexts.
Fraud & Internal Investigations
Typically high‑risk or regulated decision contexts.
Autonomous AI Agents
Typically high‑risk or regulated decision contexts.
AMEVP vs AI Audit
AI Audit
- Focuses on compliance checks and documentation.
- Often retrospective analysis of AI models.
- May not ensure accountability and human oversight.
AMEVP
- Provides explainability, justification, and contestability.
- Ensures human responsibility and incident response.
- Bridges gap between documentation and accountability.
Services
AMEVP – Verification Engagement
Comprehensive assessment to verify AI decision processes.
Sector‑Specific AMEVP
Tailored procedures for industry-specific regulations.
Continuous Accountability Support
Ongoing monitoring and support to maintain compliance.
AI Accountability Snapshot
Quick evaluation of your AI system's accountability posture.