Standard for XAI – eXplainable Artificial Intelligence – for Achieving Clarity and Interoperability of AI Systems Design

Scope

This standard defines mandatory and optional requirements and constraints that need to be satisfied for an AI method, algorithm, application or system to be recognized as explainable. Both partially explainable and fully or strongly explainable methods, algorithms and systems are defined. XML Schema are also defined. ©IEEE 2022. All rights reserved.

Purpose

This standard enables engineers and scientists developing AI systems to design systems with improved interoperability, supporting the export and import of AI systems and solutions from one implementation to another.

The aim is to provide researchers, developers and designers of AI (including machine learning, rule-based, neural network and other) systems and industrial applications with a unified and high-level methodology for classification of their products as partially or fully explainable. This standard includes an “XML Schema” describing the requirements and constraints that have to be satisfied. ©IEEE 2022. All rights reserved.

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