The Need for AI Regulation

Since the 2016 defeat of the world GO champion by Google’s Alpha Go AI program, the need for strong and effective AI Governance is quickly growing in awareness. New institutions, organizations and thinkers are just beginning the start of a conversation about how we prevent  or mitigate existential risk, as well as other economic, social and political dislocations that narrow AI has the potential to effect. While we are only at the earliest days of this conversation, we invite you to add your voice and expertise to the discussion.

Asilomar Principles

These principles were developed in conjunction with the 2017 Asilomar conference and has been signed by over 1273 AI/Robotics researchers and 2541 others. You can sign it here. 

While this is an excellent start and certainly an effort to be commended, we at Our Human Future do not believe that industry regulated principles will be sufficient to contain the existential risk threat posed by AI. Therefore, national and international laws and regulations will be needed, just as we have today for biological weapons and nuclear weapons.

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.

Reports that can help inform Regulation

The Malicious Use of Artificial Intelligence

This study was undertaken by researchers at the Future of Humanity Institute, the Center for the Study of Existential Risk, OpenAI, and 12 other institutions, drawing on expertise from a wide range of areas, including AI, cybersecurity, and public policy.

UNICRI Report: Risk and Benefits of AI and Robotics

The potential risks and benefits associated with advancements being made in the fields of artificial intelligence (AI) and robotics were analysed and discussed during a two-day workshop from at University of Cambridge on February 6-7, 2017 organized by the United Nations Interregional Crime and Justice Research Institute (UNICRI) in collaboration with, and hosted by, the Cambridge Centre for Risk Studies.

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