Standards for trustworthy AI in the European Union: technical rationale, structural challenges, and an implementation path

TL;DR

This paper explores the technical foundations and challenges of establishing trustworthy AI standards in the EU, proposing a layered, risk-based approach to standardization that supports legal compliance and scalable assessment.

Contribution

It introduces a comprehensive scheme for AI standardization addressing unique challenges and integrating technical, legal, and lifecycle considerations within the EU framework.

Findings

Standards facilitate conformity assessment and legal compliance.

Layered, risk-based standards address AI's unique challenges.

A structured approach supports scalable and auditable AI systems.

Abstract

This white paper examines the technical foundations of European AI standardization under the AI Act. It explains how harmonized standards enable the presumption of conformity mechanism, describes the CEN/CENELEC standardization process, and analyzes why AI poses unique standardization challenges including stochastic behavior, data dependencies, immature evaluation practices, and lifecycle dynamics. The paper argues that AI systems are typically components within larger sociotechnical systems, requiring a layered approach where horizontal standards define process obligations and evidence structures while sectoral profiles specify domain-specific thresholds and acceptance criteria. It proposes a workable scheme based on risk management, reproducible technical checks redefined as stability of measured properties, structured documentation, comprehensive logging, and assurance cases that evolve over the system lifecycle. The paper demonstrates that despite methodological difficulties, technical standards remain essential for translating legal obligations into auditable engineering practice and enabling scalable conformity assessment across providers, assessors, and enforcement authorities