Asilomar AI Principles - Future of Life Institute

Asilomar AI Principles - Future of Life Institute 
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 The Asilomar AI Principles, coordinated by FLI and developed at the Beneficial AI 2017 conference, are one of the earliest and most influential sets of AI governance principles. Signatures 5720 Add your signature Published 11 August, 2017 These principles were developed in conjunction with the 2017 Asilomar conference ( videos here ), through the process described here .

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 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, fre

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