Browse Definitions :
Definition

Asilomar AI Principles

What are Asilomar AI Principles?

Asilomar AI Principles are 23 guidelines for the research and development of artificial intelligence (AI). The Asilomar Principles outline developmental issues, ethics and guidelines for the development of AI, with the goal of guiding the development of beneficial AI. The tenets were created at the Asilomar Conference on Beneficial AI in 2017 in Pacific Grove, Calif. The conference was organized by the Future of Life Institute.

The 23 principles were developed by a group of AI researchers, robotics, technology experts and legal scholars from different universities and organizations. These experts organized the AI principles at the Asilomar Conference while discussing the future of AI and its regulation.

The Future of Life Institute is a nonprofit organization founded in 2014 by MIT cosmologist Max Tegmark, Skype co-founder Jaan Tallinn, physicist Anthony Aguirre, DeepMind research scientist Viktoriya Krakovna and Tufts University postdoctoral scholar Meia Chita-Tegmark. Thousands of AI and robotics researchers have signed onto the principles, as well as with other endorsers from a variety of AI research leaders, including Google, Apple and OpenAI. In 2018, the state of California endorsed these principles.

The Asilomar AI Principles are divided into three categories: Research, Ethics and values, and Longer-term issues. Often, the principles are a clear statement of possible undesirable outcomes, followed by recommendations to prevent such an event.

The 23 Asilomar AI principles.
The 23 Asilomar AI Principles are separated into three different categories: Research, Ethics and values, and Longer-term issues.

Research

This subsection of five principles revolves around how AI is researched and developed as well as its transparency and its beneficial use:

  1. Research. The goal of AI research should be to create not undirected intelligence but beneficial intelligence. This means AI research should always be beneficial.
  2. Research funding. Investments in AI should be accompanied by funding for research on ensuring its beneficial use.
  3. Science-policy link. There should be constructive and healthy exchange between AI researchers and policymakers.
  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

This subsection of 13 AI principles revolves around the ethics of AI and the values instilled while developing it:

  1. Safety. AI systems should be safe and secure throughout their operational lifetime and verifiably so where applicable and feasible.
  2. Failure transparency. If an AI system causes harm, it should be possible to ascertain why.
  3. Judicial transparency. Any involvement by an autonomous system in judicial decision-making should provide a satisfactory explanation auditable by a competent human authority.
  4. 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.
  5. 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.
  6. Human values. AI systems should be designed and operated to be compatible with ideals of human dignity, rights, freedoms and cultural diversity.
  7. 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.
  8. Liberty and privacy. The application of AI to personal data must not unreasonably curtail people's real or perceived liberty.
  9. Shared benefit. AI technologies should benefit and empower as many people as possible. This, for example, includes the use of AI to make jobs easier, the optimization of energy use or as expert Knowledge-based systems.
  10. Shared prosperity. The economic prosperity created by AI should be shared broadly, to benefit all of humanity.
  11. Human control. Humans should choose how and whether to delegate decisions to AI systems to accomplish human-chosen objectives.
  12. 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.
  13. AI arms race. An arms race in lethal autonomous weapons should be avoided.

Longer-term issues

This subsection of five AI principles revolves around the importance, risks and potential good AI can provide in the long term:

  1. Capability caution. There being no consensus, we should avoid strong assumptions regarding upper limits on future AI capabilities.
  2. 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.
  3. Risks. Risks posed by AI systems, especially catastrophic or existential risks, must be subject to planning and mitigation efforts commensurate with their expected impact.
  4. 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.
  5. 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.

Learn more about AI governance and privacy and what it looks like in a framework.

This was last updated in March 2023

Continue Reading About Asilomar AI Principles

Networking
  • subnet (subnetwork)

    A subnet, or subnetwork, is a segmented piece of a larger network. More specifically, subnets are a logical partition of an IP ...

  • Transmission Control Protocol (TCP)

    Transmission Control Protocol (TCP) is a standard protocol on the internet that ensures the reliable transmission of data between...

  • secure access service edge (SASE)

    Secure access service edge (SASE), pronounced sassy, is a cloud architecture model that bundles together network and cloud-native...

Security
  • intrusion detection system (IDS)

    An intrusion detection system monitors (IDS) network traffic for suspicious activity and sends alerts when such activity is ...

  • cyber attack

    A cyber attack is any malicious attempt to gain unauthorized access to a computer, computing system or computer network with the ...

  • digital signature

    A digital signature is a mathematical technique used to validate the authenticity and integrity of a digital document, message or...

CIO
  • What is data privacy?

    Data privacy, also called information privacy, is an aspect of data protection that addresses the proper storage, access, ...

  • product development (new product development)

    Product development -- also called new product management -- is a series of steps that includes the conceptualization, design, ...

  • innovation culture

    Innovation culture is the work environment that leaders cultivate to nurture unorthodox thinking and its application.

HRSoftware
  • organizational network analysis (ONA)

    Organizational network analysis (ONA) is a quantitative method for modeling and analyzing how communications, information, ...

  • HireVue

    HireVue is an enterprise video interviewing technology provider of a platform that lets recruiters and hiring managers screen ...

  • Human Resource Certification Institute (HRCI)

    Human Resource Certification Institute (HRCI) is a U.S.-based credentialing organization offering certifications to HR ...

Customer Experience
  • What is an outbound call?

    An outbound call is one initiated by a contact center agent to prospective customers and focuses on sales, lead generation, ...

  • What is lead-to-revenue management (L2RM)?

    Lead-to-revenue management (L2RM) is a set of sales and marketing methods focusing on generating revenue throughout the customer ...

  • What is relationship marketing?

    Relationship marketing is a facet of customer relationship management (CRM) that focuses on customer loyalty and long-term ...

Close