AI in general 1, 2017

Asilomar AI Principles



Future of Life Institute


The principles were developed by the Future of Life Institute, a Boston-based research organisation devoted to the safeguarding human life and developing optimistic visions of the future. In advance of the 2017 Beneficial AI conference, the organisers compiled a list of principles about how to manage AI, distilled from the various reports and initiatives around the world. These principles were circulated during the BAI event and extensive feedback collected about them. A revised version was then discussed at a meeting in Asilomar, which resulted in the final 23 item list organised into 3 broad categories.
Research Issues
Research Goal: The goal of AI research should be to create not undirected intelligence, but beneficial intelligence.
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?

  • [/ul]
    Science-Policy Link: There should be constructive and healthy exchange between AI researchers and policy-makers.
    Research Culture: A culture of cooperation, trust, and transparency should be fostered among researchers and developers of AI.
    Race Avoidance: Teams developing AI systems should actively cooperate to avoid corner-cutting on safety standards.
    Ethics and Values
    Safety: AI systems should be safe and secure throughout their operational lifetime, and verifiably so where applicable and feasible.
    Failure Transparency: If an AI system causes harm, it should be possible to ascertain why.
    Judicial Transparency: Any involvement by an autonomous system in judicial decision-making should provide a satisfactory explanation auditable by a competent human authority.
    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.
    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.
    Human Values: AI systems should be designed and operated so as to be compatible with ideals of human dignity, rights, freedoms, and cultural diversity.
    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.
    Liberty and Privacy: The application of AI to personal data must not unreasonably curtail people’s real or perceived liberty.
    Shared Benefit: AI technologies should benefit and empower as many people as possible.
    Shared Prosperity: The economic prosperity created by AI should be shared broadly, to benefit all of humanity.
    Human Control: Humans should choose how and whether to delegate decisions to AI systems, to accomplish human-chosen objectives.
    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.
    AI Arms Race: An arms race in lethal autonomous weapons should be avoided.
    Longer-term Issues
    Capability Caution: There being no consensus, we should avoid strong assumptions regarding upper limits on future AI capabilities.
    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.
    Risks: Risks posed by AI systems, especially catastrophic or existential risks, must be subject to planning and mitigation efforts commensurate with their expected impact.
    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.
    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.

    AI Governance

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