IEEE 8000 Working Group - Trustworthiness of Artificial Intelligence

Standards Committee: Entity Collaborative Activities Governance Board (BOG/CAG)

Project: P8000.1 – Standard for a Method and Criteria to Assess the Trustworthiness of Artificial Intelligence (AI) Systems

Latest news: 22 April 2026: The first version of the Draft Standard has been approved by the Working Group. More news…

Scope: The standard specifies a methodology and criteria for assessing the trustworthiness of Artificial Intelligence (AI) systems. It establishes scoring mechanisms for key principles underlying AI confidence, while accounting for interdependencies between these principles. The criteria encompass the evaluation of technical attributes, including robustness and safety, and socio-ethical properties, such as transparency, accountability, human agency and oversight, privacy, and fairness. These criteria are designed for application at multiple stages of the AI supply chain, from development through deployment and operation, and are applicable to both individual AI components and integrated, complete systems.

Purpose: The purpose of this standard is to provide a methodology and a comprehensive set of criteria for assessing the trustworthiness of Artificial Intelligence (AI) systems. This framework enables contextual assessments through the selection of applicable criteria aligned with regional norms, legal systems, and cultural values. The standard establishes the technical basis for AI trustworthiness rating services and the development of conformity assessment schemes, including certification programs.

Abstract: A method to assess the trustworthiness of AI systems is defined by the standard.

Seven distinct scores – one for each of the following principles – reflecting the trustworthiness and ethical soundness of an AI system can be assigned using the method, namely:

  • Accountability
  • Human Agency & Oversight
  • Technical Robustness & Safety
  • Privacy & Data Governance
  • Transparency
  • Diversity, Non-Discrimination & Fairness
  • Societal & Environmental Well-Being

The method is applicable to both high-risk and non-high-risk AI systems. The method is designed to score AI systems at different stages of the supply chain, from development through deployment and operation.

The method is intended to serve as the foundation of an AI system trust rating service and certification program.