IEEE portfolio of AIS technology and impact standards and standards projects
View the IEEE 7000™ Standards & Projects
IEEE P1872.2™ - Standard for Autonomous Robotics (AuR) Ontology
This standard is a logical extension to IEEE 1872-2015™ Standard for Ontologies for Robotics and Automation. The standard extends the CORA ontology by defining additional ontologies appropriate for Autonomous Robotics (AuR) relating to:
IEEE P2660.1™ - Recommended Practices on Industrial Agents: Integration of Software Agents and Low Level Automation Functions
This recommended practice describes integrating and deploying the Multi-agent Systems (MAS) technology in industrial environments for use in building the intelligent decision-making layer on top of legacy industrial control platforms. The integration of software agents with the low-level real-time control systems, mainly based on the Programmable Logic Controllers (PLCs) running the IEC 61131-3™ control programs (forming in this manner a new component known as industrial agents) are also identified. In addition, the integration of software agents with the control applications based on IEC 61499™ standard or executed on embedded controllers is described.
This recommended practice supports and helps the engineers leverage the best practices of developing industrial agents for specific automation control problems and given application fields. Therefore, corresponding rules, guidelines and design patterns are provided.
IEEE P2801™ - Recommended Practice for the Quality Management of Datasets for Medical Artificial Intelligence
This recommended practice identifies best practices for establishing a quality management system for datasets used for artificial intelligence medical devices. It covers a full cycle of dataset management, including items such as but not limited to data collection, transfer, utilization, storage, maintenance and update.
This recommended practice recommends a list of critical factors that impact the quality of datasets, such as but not limited to data sources, data quality, annotation, privacy protection, personnel qualification/training/evaluation, tools, equipment, environment, process control and documentation.
IEEE P2802™ - Standard for the Performance and Safety Evaluation of Artificial Intelligence Based Medical Device: Terminology
This standard establishes terminology used in artificial intelligence medical devices, including definitions of fundamental concepts and methodology that describe the safety, effectiveness, risks and quality management of artificial intelligence medical devices.
It provides definitions using the following forms, such as but not limited to literal description, equations, tables, figures and legends.
The standard also establishes a vocabulary for the development of future standards for artificial intelligence medical devices.
IEEE P3333.1.3™ - Standard for the Deep Learning Based Assessment of Visual Experience Based on Human Factors
This standard defines deep learning-based metrics of content analysis and quality of experience (QoE) assessment for visual contents, which is an extension of Standard for the Quality of Experience (QoE) and Visual-Comfort Assessments of Three-Dimensional (3D) Contents Based on Psychophysical Studies (IEEE 3333.1.1™) and Standard for the Perceptual Quality Assessment of Three Dimensional (3D) and Ultra High Definition (UHD) Contents (IEEE 3333.1.2™).
The scope covers the following:
IEEE P3652.1™ - Guide for Architectural Framework and Application of Federated Machine Learning
Federated learning defines a machine learning framework that allows a collective model to be constructed from data that is distributed across data owners.
This guide provides a blueprint for data usage and model building across organizations while meeting applicable privacy, security and regulatory requirements. It defines the architectural framework and application guidelines for federated machine learning, including: 1) description and definition of federated learning, 2) the types of federated learning and the application scenarios to which each type applies, 3) performance evaluation of federated learning, and 4) associated regulatory requirements.
IEEE P7001™ - Standards for Transparency of Autonomous Systems
This standard describes measurable, testable levels of transparency, so that autonomous systems can be objectively assessed and levels of compliance determined.
A key concern over autonomous systems (AS) is that their operation must be transparent to a wide range of stakeholders, for different reasons.
For designers, the standard will provide a guide for self-assessing transparency during development and suggest mechanisms for improving transparency (for instance the need for secure storage of sensor and internal state data, comparable to a flight data recorder or black box).
IEEE P7008™ - Standard for Ethically Driven Nudging for Robotic, Intelligent and Autonomous Systems
"Nudges" as exhibited by robotic, intelligent or autonomous systems are defined as overt or hidden suggestions or manipulations designed to influence the behavior or emotions of a user.
This standard establishes a delineation of typical nudges (currently in use or that could be created). It contains concepts, functions and benefits necessary to establish and ensure ethically driven methodologies for the design of the robotic, intelligent and autonomous systems that incorporate them.
IEEE P7009™ - Standard for Fail-Safe Design of Autonomous and Semi-Autonomous Systems
This standard establishes a practical, technical baseline of specific methodologies and tools for the development, implementation, and use of effective fail-safe mechanisms in autonomous and semi-autonomous systems.
The standard includes (but is not limited to): clear procedures for measuring, testing, and certifying a system's ability to fail safely on a scale from weak to strong, and instructions for improvement in the case of unsatisfactory performance.
IEEE 7010-2020™ (Standard Now Available) - IEEE Recommended Practice for Assessing the Impact of Autonomous and Intelligent Systems on Human Well-being
Recommended Practice for Assessing the Impact of Autonomous and Intelligent Systems (A/IS) on Human Well-being is a recommended practice for measuring the impact of A/IS on humans. The overall intent of IEEE P7010™ is to support the outcome of A/IS having positive impacts on human well-being.
The recommended practice is grounded in scientifically valid well-being indices currently in use and based on a stakeholder engagement process. The intent of the recommended practice is to guide product development, identify areas for improvement, manage risks, assess performance and identify intended and unintended users, uses and impacts on human well-being of A/IS.
Now available at no charge in the IEEE Standards Reading Room.
IEEE P7011™ - Standard for the Process of Identifying and Rating the Trustworthiness of News Sources
This standard provides semi-autonomous processes using standards to create and maintain news purveyor ratings for purposes of public awareness. It standardizes processes to identify and rate the factual accuracy of news stories in order to produce a rating of online news purveyors and the online portion of multimedia news purveyors. This process will be used to produce truthfulness scorecards through multi-faceted and multi-sourced approaches.
The standard defines an algorithm using open source software and a scorecard rating system as methodology for rating trustworthiness as a core tenant in an effort to establish trust and acceptance.