Overview
New technology comes with unknown obstacles and unintended risks requiring accountable design and lifecycle planning to ensure responsible innovation. As artificial intelligence, autonomous intelligent systems (AIS), machine learning, autonomous vehicles, and robotics advance at a rapid pace, careful considerations need to be made during development and implementation regarding humanity. At IEEE SA, our global community has developed resources and standards globally recognized in the area of applied ethics and systems engineering and continues to develop accessible and sustainable approaches and solutions for pragmatic application of AIS principles and frameworks. IEEE SA offers standards, training and education, certification programs, and more, to empower stakeholders designing, developing, and using AIS. Global participation is encouraged to offer the broadest regional and cultural perspectives required to best contextualize how AIS systems can avoid risk and offer greatest benefit in operation.
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Learn more about how to engage with AIS related Communities and Industry Connections Programs.
Events
Related Standards
IEEE P2247.1™
Standard for the Classification of Adaptive Instructional Systems
IEEE P2247.2™
Interoperability Standards for Adaptive Instructional Systems (AISs)
IEEE P2247.3™
Recommended Practices for Evaluation of Adaptive Instructional Systems
IEEE P2247.4™
Recommended Practice for Ethically Aligned Design of Artificial Intelligence (AI) in Adaptive Instructional Systems
IEEE 2089-2021
IEEE Standard for an Age Appropriate Digital Services Framework Based on the 5Rights Principles for Children
IEEE 7000™
IEEE Standard Model Process for Addressing Ethical Concerns during System Design
IEEE 7001™
IEEE Standard for Transparency of Autonomous Systems
IEEE P7002™
IEEE Standard for Data Privacy Process
IEEE 7003™
Algorithmic Bias Considerations
IEEE P7004™
Standard for Child and Student Data Governance
IEEE 7005™
IEEE Standard for Transparent Employer Data Governance
IEEE 7007™
IEEE Ontological Standard for Ethically Driven Robotics and Automation Systems
IEEE P7008™
Standard for Ethically Driven Nudging for Robotic, Intelligent and Autonomous Systems
IEEE P7009™
Standard for Fail-Safe Design of Autonomous and Semi-Autonomous Systems
IEEE P7009.1™
Standard for Safety Management of Autonomous and Semi-Autonomous Systems–Interventions in the Event of Anomalous Behavior
IEEE P3109™
Standard for Arithmetic Formats for Machine Learning
IEEE 3652.1™-2020
IEEE Guide for Architectural Framework and Application of Federated Machine Learning
IEEE P3127™
Guide for an Architectural Framework for Blockchain-based Federated Machine Learning
IEEE P3187™
Guide for Framework for Trustworthy Federated Machine Learning
IEEE P2986™
Recommended Practice for Privacy and Security for Federated Machine Learning
IEEE P2805.3™
Cloud-Edge Collaboration Protocols for Machine Learning
IEEE 2830™-2021
IEEE Standard for Technical Framework and Requirements of Trusted Execution Environment based Shared Machine Learning
IEEE P3123™
Standard for Artificial Intelligence and Machine Learning (AI/ML) Terminology and Data Formats
IEEE P1900.8™
Standard for Training, Testing, and Evaluating Machine-Learned Spectrum Awareness Models
IEEE P3157™
Recommended Practice for Vulnerability Test for Machine Learning Models for Computer Vision Applications
Resources
Ethically Aligned Design (EAD):
IEEE CertifAIEd™:
- AI Ethics for Solutions Developers
- IEEE CertifAIEd™ – Ontological Specification for Ethical Transparency
- IEEE CertifAIEd™ – Ontological Specification for Ethical Algorithmic Bias
- IEEE CertifAIEd™ – Ontological Specification for Ethical Accountability
- IEEE CertifAIEd™ – Ontological Specification for Ethical Privacy
- AI Ethics Support Badge
- IEEE CertifAIEd™ Awareness
Children's Data Governance:
Other:
Report: Decoupling Human Characteristics from Algorithmic Capabilities
This report opens a conversation about decoupling anthropomorphic language from discussions around artificial intelligence in efforts to give agency to the public and decision makers. This is explored through the lens of the following human characteristics which are commonly attributed to AI Systems: Learn, Analyze, Knowledge, Foresight, and Decision-Making.
Report: Addressing Ethical Dilemmas in AI: Listening to Engineers
This report is based on the proceedings of the online hackathon “Ethical dilemmas in AI – engineering the way out”, conducted in September 2020. The goal of the hackathon was to identify the main challenges for integrating existing ethical principles and guidelines into the engineering processes that power AI development.
Measuring What Matters in the Era of Global Warming and the Age of Algorithmic Promises
At a physical level, human well being is critically dependent on environmental sustainability. Our symbiotic relationship with nature, however, has been severely damaged due to actions that have resulted in a climate crisis. A combination of a courageous socioeconomic transformation and accelerated technological innovation is necessary to mitigate this threat.
Measurementality Series
Measurementality, is a series of podcasts, webinars, and reports created by IEEE SA in collaboration with The Radical AI Podcast and explores what measurements of success we’re optimizing for AIS development.
IEEE Course Program on AI Standards
The purpose of this course series is to provide instructions for a comprehensive approach to creating ethical and responsible digital ecosystems.
Webinar: Is it Technically Possible to Make the World Age Appropriate
Today, one in three people online is under 18. There is an urgent need to address the vulnerabilities and evolving capacities associated with children and their online experiences.
IEEE Course Program on Artificial Intelligence and Ethics in Design
Responsible artificial intelligence, law, compliance, and ethics in artificial intelligence and practical applied ethics for use in the responsible design of artificial intelligence systems.