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The Asilomar AI Principles are a set of 23 guidelines formulated in January 2017 by a diverse group of AI researchers, ethicists, and industry leaders during the Asilomar Conference on Beneficial AI held in California. These principles aim to guide the ethical development, deployment, and regulation of artificial intelligence (AI) systems, ensuring that AI benefits humanity while minimizing potential risks.
Divided into three main categories—Research Issues, Ethics and Values, and Long-Term Issues—the principles address both immediate and future considerations in AI development. Under Research Issues, the principles emphasize the importance of safe, ethical, and transparent AI research practices. They advocate for a culture of cooperation, trust, and openness among AI researchers and developers, stressing that AI research should focus on creating beneficial intelligence rather than pursuing undirected advancement. Sharing information about AI safety and encouraging interdisciplinary collaboration are key aspects intended to prevent risks that could arise from competitive secrecy.
The Ethics and Values category focuses on aligning AI development with human values. It underscores that AI systems should be designed to be compatible with ideals of human dignity, rights, freedoms, and cultural diversity. Critical ethical considerations include avoiding unfair bias in AI technologies to prevent discrimination, ensuring that the benefits of AI are widely and fairly distributed across society, and maintaining accountability for AI actions and decisions. The principles highlight that designers and developers bear responsibility for the moral implications of their AI systems, promoting fairness, transparency, and human-centric values.
Addressing the Long-Term Issues, the principles consider the future impacts and potential existential risks of AI. They call for efforts to avoid an arms race in lethal autonomous weapons, urging nations and corporations to refrain from using AI in military applications that could lead to global instability. The principles emphasize that the development of advanced AI should be pursued only when its effects can be aligned with the long-term well-being of humanity. They advocate for designing AI systems capable of self-improvement or self-replication with safeguards to ensure their actions remain beneficial and under meaningful human control.
The Asilomar AI Principles are considered foundational for guiding ethical AI research and development. They highlight the need for transparency, fairness, accountability, and collaboration across borders and disciplines. By explicitly outlining the societal, ethical, and existential risks that AI might pose, the principles seek to foster AI technologies that enhance human capabilities without compromising ethical standards.
From a legal and governance standpoint, the principles stress the importance of proactive regulation and international cooperation. They advocate for governments, corporations, and researchers to take a collaborative approach to AI governance, creating frameworks that anticipate ethical challenges before they escalate into significant societal issues. Compliance with existing laws and human rights is emphasized, along with the necessity for global standards to prevent detrimental competition such as an AI arms race.
In conclusion, the Asilomar AI Principles offer a comprehensive, forward-looking framework for ensuring that AI technology remains beneficial to humanity while upholding ethical standards and avoiding potential risks. By emphasizing human values, ethical integrity, and global cooperation, these principles continue to inform contemporary discussions about AI safety and governance, guiding the responsible development and use of artificial intelligence in a manner that contributes positively to society.
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The Asilomar AI Principles
11 August, 2017 | Future of Life Institute
"The Asilomar AI Principles, coordinated by the Future of Life Institute and developed at the Beneficial AI 2017 conference, are one of the earliest and most influential sets of AI governance principles. These principles were developed in conjunction with the 2017 Asilomar conference (videos here), through the process described here. Click here to see this page in other languages: Chinese, German, Japanese, Korean, Russian
Artificial intelligence has already provided beneficial tools that are used every day by people around the world. Its continued development, guided by the following principles, will offer amazing opportunities to help and empower people in the decades and centuries ahead.
Artificial intelligence has already provided beneficial tools that are used every day by people around the world. Its continued development, guided by the following principles, will offer amazing opportunities to help and empower people in the decades and centuries ahead.
Research Issues
1) Research Goal: The goal of AI research should be to create not undirected intelligence, but beneficial intelligence.
2) Research Funding: Investments in AI should be accompanied by funding for research on ensuring its beneficial use, including thorny questions in computer science, economics, law, ethics, and social studies, such as:
- How can we make future AI systems highly robust, so that they do what we want without malfunctioning or getting hacked?
- How can we grow our prosperity through automation while maintaining people’s resources and purpose?
- How can we update our legal systems to be more fair and efficient, to keep pace with AI, and to manage the risks associated with AI?
- What set of values should AI be aligned with, and what legal and ethical status should it have?
3) Science-Policy Link: There should be constructive and healthy exchange between AI researchers and policy-makers.
4) Research Culture: A culture of cooperation, trust, and transparency should be fostered among researchers and developers of AI.
5) Race Avoidance: Teams developing AI systems should actively cooperate to avoid corner-cutting on safety standards.
Ethics and Values
6) Safety: AI systems should be safe and secure throughout their operational lifetime, and verifiably so where applicable and feasible.
7) Failure Transparency: If an AI system causes harm, it should be possible to ascertain why.
8) Judicial Transparency: Any involvement by an autonomous system in judicial decision-making should provide a satisfactory explanation auditable by a competent human authority.
9) Responsibility: Designers and builders of advanced AI systems are stakeholders in the moral implications of their use, misuse, and actions, with a responsibility and opportunity to shape those implications.
10) Value Alignment: Highly autonomous AI systems should be designed so that their goals and behaviors can be assured to align with human values throughout their operation.
11) Human Values: AI systems should be designed and operated so as to be compatible with ideals of human dignity, rights, freedoms, and cultural diversity.
12) Personal Privacy: People should have the right to access, manage and control the data they generate, given AI systems’ power to analyze and utilize that data.
13) Liberty and Privacy: The application of AI to personal data must not unreasonably curtail people’s real or perceived liberty.
14) Shared Benefit: AI technologies should benefit and empower as many people as possible.
15) Shared Prosperity: The economic prosperity created by AI should be shared broadly, to benefit all of humanity.
16) Human Control: Humans should choose how and whether to delegate decisions to AI systems, to accomplish human-chosen objectives.
17) Non-subversion: The power conferred by control of highly advanced AI systems should respect and improve, rather than subvert, the social and civic processes on which the health of society depends.
18) AI Arms Race: An arms race in lethal autonomous weapons should be avoided.
Longer-term Issues
19) Capability Caution: There being no consensus, we should avoid strong assumptions regarding upper limits on future AI capabilities.
20) Importance: Advanced AI could represent a profound change in the history of life on Earth, and should be planned for and managed with commensurate care and resources.
21) Risks: Risks posed by AI systems, especially catastrophic or existential risks, must be subject to planning and mitigation efforts commensurate with their expected impact.
22) Recursive Self-Improvement: AI systems designed to recursively self-improve or self-replicate in a manner that could lead to rapidly increasing quality or quantity must be subject to strict safety and control measures.
23) Common Good: Superintelligence should only be developed in the service of widely shared ethical ideals, and for the benefit of all humanity rather than one state or organization."
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Citation: Asilomar Conference. Asilomar AI Principles. Future of Life Institu te, 2017.
