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 continue 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.
Programs and Services
Learn more about how to engage with AIS related Communities and Industry Connections Programs.
IEEE CertifAIEd Training | 26-29 February 2024
This course has been developed for individuals who are interested in learning how to assess AIS for their conformity to IEEE CertifAIEd AI Ethics criteria. Completion of this course is an integral step towards becoming recognized as an IEEE CertifAIEd Authorized Assessor.
IEEE Guide for Architectural Framework and Application of Federated Machine Learning
IEEE Standard for Technical Framework and Requirements of Trusted Execution Environment based Shared Machine Learning
Standard for Artificial Intelligence and Machine Learning (AI/ML) Terminology and Data Formats
Standard for Training, Testing, and Evaluating Machine-Learned Spectrum Awareness Models
Recommended Practice for Vulnerability Test for Machine Learning Models for Computer Vision Applications
Ethically Aligned Design (EAD):
- 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:
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.
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.
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, 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.
The purpose of this course series is to provide instructions for a comprehensive approach to creating ethical and responsible digital ecosystems.
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.
Responsible artificial intelligence, law, compliance, and ethics in artificial intelligence and practical applied ethics for use in the responsible design of artificial intelligence systems.