High level principles without enforcement mechanisms.
17 resources
The High-Level Expert Group on AI's ethics guidelines establishing seven key requirements for trustworthy AI: human agency, technical robustness, privacy, transparency, diversity, societal wellbeing, and accountability.
IEEE's comprehensive vision for prioritizing human well-being in autonomous and intelligent systems. It provides conceptual frameworks and practical recommendations across multiple domains and use cases.
A civil society declaration calling for machine learning systems to respect human rights. It emphasizes the rights to equality, non-discrimination, privacy, and due process in the context of AI systems.
A set of 23 principles for beneficial AI developed at the 2017 Asilomar conference. Covers research issues, ethics and values, and longer-term issues including AI safety and beneficial development.
The Asilomar AI Principles are a set of 23 guidelines for artificial intelligence research and development, covering safety, ethics, and long-term considerations. These principles were developed at the 2017 Asilomar Conference to provide a framework for beneficial AI development and to ensure AI systems remain aligned with human values and interests.
The Asilomar Conference on Beneficial AI was a 2017 conference that brought together AI researchers and experts to discuss the future of artificial intelligence. The conference resulted in the creation of the 23 Asilomar AI Principles, which provide guidelines for AI research and development to ensure AI remains beneficial to humanity.
The Montréal Declaration is a collaborative framework that aims to guide AI development to serve the well-being of all people. It provides democratically-grounded recommendations for responsible AI development and deployment to guide positive social change.
The Montreal Declaration is a comprehensive ethical framework for responsible AI development that addresses individuals, organizations, and companies involved in artificial intelligence. It establishes principles and guidelines to ensure AI development serves human welfare and respects fundamental rights and values.
The Montreal Declaration is a framework for responsible AI development that was announced in November 2017 following a forum on socially responsible AI development. It establishes ethical principles and guidelines for the development and deployment of artificial intelligence systems.
The Beijing AI Principles are ethical guidelines for artificial intelligence development released in May 2019. They provide comprehensive principles covering the entire lifecycle of AI research, development, and application, with emphasis on long-term planning for AGI and superintelligence risks.
The Linking Artificial Intelligence Principles (LAIP) is a platform network developed by leading Chinese academic institutions and AI enterprises. It serves as a repository and linking system for artificial intelligence ethical principles and governance frameworks.
The Beijing AI Principles is a comprehensive framework released by the Beijing Academy of Artificial Intelligence (BAAI) that provides ethical guidelines for AI research, development, and deployment. The principles outline responsible practices and governance approaches for artificial intelligence systems in China.
Ethics guidelines developed by the European Commission's High-Level Expert Group on Artificial Intelligence, published in April 2019. The guidelines establish principles and recommendations for developing trustworthy AI systems within the European Union framework.
Influential ethics guidelines for trustworthy artificial intelligence published by the EU's High-Level Expert Group on AI in April 2019. This foundational document established key principles and framework for developing and deploying trustworthy AI systems within the European context.
Microsoft's framework outlining six core principles for responsible AI development and deployment. The resource provides guidance on how to design, build, and test AI systems according to ethical standards and best practices.
Microsoft's responsible AI framework outlines ethical policies and practices for AI development and deployment. The framework addresses key issues including fairness, reliability and safety, privacy and security, inclusiveness, transparency and accountability to guide responsible AI implementation.
A training module that explores six core principles for responsible AI development and deployment: fairness, reliability and safety, privacy and security, inclusiveness, transparency, and accountability. The resource provides guidance on implementing responsible AI practices in AI system development and use.