Asilomar AI Principles

Summary

The Asilomar AI Principles represent a historic moment when 90+ AI researchers, ethicists, and thought leaders gathered in Asilomar, California to establish shared guidelines for beneficial AI development. These 23 principles span three critical timeframes: immediate research priorities, near-term ethical considerations, and long-term safety challenges as AI systems become more capable. Unlike top-down regulatory frameworks, these principles emerged from the AI community itself, creating a foundation that has influenced countless AI ethics initiatives, corporate policies, and regulatory discussions worldwide.

The Three Pillars of AI Development

The principles are uniquely organized around three temporal phases of AI development:

Research Issues (Principles 1-5) focus on immediate concerns like research funding goals, science-policy dialogue, research culture, and avoiding capability races. These address how AI research should be conducted today.

Ethics and Values (Principles 6-18) tackle near-term deployment issues including safety, failure transparency, judicial transparency, responsibility, value alignment, human values, personal privacy, liberty and privacy, shared benefit, shared prosperity, human control, non-subversion, and AI arms race prevention.

Longer-term Issues (Principles 19-23) grapple with advanced AI scenarios, covering capability caution, importance, risks, recursive self-improvement, and common good considerations for artificial general intelligence.

What Makes This Different

Unlike prescriptive compliance frameworks, the Asilomar Principles function as normative guidance—they articulate aspirational goals rather than mandatory requirements. They deliberately avoid technical specifications, instead focusing on the values and objectives that should guide AI development. This approach has made them remarkably durable; principles like "AI systems should be safe and secure throughout their operational lifetime" remain relevant across rapidly evolving technical landscapes.

The principles also uniquely bridge immediate practical concerns (like avoiding AI arms races) with speculative long-term scenarios (like artificial general intelligence), providing continuity across different stages of AI advancement.

Who This Resource Is For

• AI researchers and engineers seeking ethical guidance that doesn't constrain technical innovation
• Technology executives developing corporate AI ethics policies and governance frameworks
• Policymakers looking for community-endorsed principles to inform regulatory approaches
• Ethics boards and oversight committees needing foundational principles for AI project evaluation
• Graduate students and academics studying the intersection of AI development and societal impact
• International organizations developing global AI governance standards and agreements

Real-World Impact and Adoption

The Asilomar Principles have been referenced in major AI strategy documents from the European Commission, cited in academic papers thousands of times, and adapted by companies like DeepMind, OpenAI, and Google. They influenced the development of the Partnership on AI, shaped discussions at the OECD AI Principles, and provided groundwork for initiatives like the Montreal Declaration for Responsible AI.

However, their voluntary nature means implementation varies widely. Some organizations treat them as aspirational goals, while others have developed specific policies and practices aligned with the principles.

Key Limitations to Consider

The principles reflect 2017 perspectives and don't address some current AI concerns like generative AI misuse, deepfakes, or large language model alignment challenges. They also lack enforcement mechanisms and specific implementation guidance, leaving organizations to interpret and apply them independently.

The focus on beneficial AI assumes technical solutions to value alignment problems that remain unsolved. Critics argue some principles (particularly around long-term AI scenarios) are too speculative to guide immediate policy decisions.