Why and How Governments Should Monitor AI Development
- URL: http://arxiv.org/abs/2108.12427v2
- Date: Tue, 31 Aug 2021 12:49:31 GMT
- Title: Why and How Governments Should Monitor AI Development
- Authors: Jess Whittlestone, Jack Clark
- Abstract summary: We outline a proposal for improving the governance of artificial intelligence (AI) by investing in government capacity to systematically measure and monitor the capabilities and impacts of AI systems.
It would also create infrastructure that could rapidly identify potential threats or harms that could occur as a consequence of changes in the AI ecosystem.
We discuss this proposal in detail, outlining what specific things governments could focus on measuring and monitoring, and the kinds of benefits this would generate for policymaking.
- Score: 0.22395966459254707
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: In this paper we outline a proposal for improving the governance of
artificial intelligence (AI) by investing in government capacity to
systematically measure and monitor the capabilities and impacts of AI systems.
If adopted, this would give governments greater information about the AI
ecosystem, equipping them to more effectively direct AI development and
deployment in the most societally and economically beneficial directions. It
would also create infrastructure that could rapidly identify potential threats
or harms that could occur as a consequence of changes in the AI ecosystem, such
as the emergence of strategically transformative capabilities, or the
deployment of harmful systems.
We begin by outlining the problem which motivates this proposal: in brief,
traditional governance approaches struggle to keep pace with the speed of
progress in AI. We then present our proposal for addressing this problem:
governments must invest in measurement and monitoring infrastructure. We
discuss this proposal in detail, outlining what specific things governments
could focus on measuring and monitoring, and the kinds of benefits this would
generate for policymaking. Finally, we outline some potential pilot projects
and some considerations for implementing this in practice.
Related papers
- Using AI Alignment Theory to understand the potential pitfalls of regulatory frameworks [55.2480439325792]
This paper critically examines the European Union's Artificial Intelligence Act (EU AI Act)
Uses insights from Alignment Theory (AT) research, which focuses on the potential pitfalls of technical alignment in Artificial Intelligence.
As we apply these concepts to the EU AI Act, we uncover potential vulnerabilities and areas for improvement in the regulation.
arXiv Detail & Related papers (2024-10-10T17:38:38Z) - Strategic AI Governance: Insights from Leading Nations [0.0]
Artificial Intelligence (AI) has the potential to revolutionize various sectors, yet its adoption is often hindered by concerns about data privacy, security, and the understanding of AI capabilities.
This paper synthesizes AI governance approaches, strategic themes, and enablers and challenges for AI adoption by reviewing national AI strategies from leading nations.
arXiv Detail & Related papers (2024-09-16T06:00:42Z) - Open Problems in Technical AI Governance [93.89102632003996]
Technical AI governance refers to technical analysis and tools for supporting the effective governance of AI.
This paper is intended as a resource for technical researchers or research funders looking to contribute to AI governance.
arXiv Detail & Related papers (2024-07-20T21:13:56Z) - Societal Adaptation to Advanced AI [1.2607853680700076]
Existing strategies for managing risks from advanced AI systems often focus on affecting what AI systems are developed and how they diffuse.
We urge a complementary approach: increasing societal adaptation to advanced AI.
We introduce a conceptual framework which helps identify adaptive interventions that avoid, defend against and remedy potentially harmful uses of AI systems.
arXiv Detail & Related papers (2024-05-16T17:52:12Z) - Particip-AI: A Democratic Surveying Framework for Anticipating Future AI Use Cases, Harms and Benefits [54.648819983899614]
General purpose AI seems to have lowered the barriers for the public to use AI and harness its power.
We introduce PARTICIP-AI, a framework for laypeople to speculate and assess AI use cases and their impacts.
arXiv Detail & Related papers (2024-03-21T19:12:37Z) - Computing Power and the Governance of Artificial Intelligence [51.967584623262674]
Governments and companies have started to leverage compute as a means to govern AI.
compute-based policies and technologies have the potential to assist in these areas, but there is significant variation in their readiness for implementation.
naive or poorly scoped approaches to compute governance carry significant risks in areas like privacy, economic impacts, and centralization of power.
arXiv Detail & Related papers (2024-02-13T21:10:21Z) - Managing extreme AI risks amid rapid progress [171.05448842016125]
We describe risks that include large-scale social harms, malicious uses, and irreversible loss of human control over autonomous AI systems.
There is a lack of consensus about how exactly such risks arise, and how to manage them.
Present governance initiatives lack the mechanisms and institutions to prevent misuse and recklessness, and barely address autonomous systems.
arXiv Detail & Related papers (2023-10-26T17:59:06Z) - Think About the Stakeholders First! Towards an Algorithmic Transparency
Playbook for Regulatory Compliance [14.043062659347427]
Laws are being proposed and passed by governments around the world to regulate Artificial Intelligence (AI) systems implemented into the public and private sectors.
Many of these regulations address the transparency of AI systems, and related citizen-aware issues like allowing individuals to have the right to an explanation about how an AI system makes a decision that impacts them.
We propose a novel stakeholder-first approach that assists technologists in designing transparent, regulatory compliant systems.
arXiv Detail & Related papers (2022-06-10T09:39:00Z) - Machines and Influence [0.0]
This paper surveys AI capabilities and tackles this very issue.
We introduce a Matrix of Machine Influence to frame and navigate the adversarial applications of AI.
We suggest that better regulation and management of information systems can more optimally offset the risks of AI.
arXiv Detail & Related papers (2021-11-26T08:58:09Z) - Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable
Claims [59.64274607533249]
AI developers need to make verifiable claims to which they can be held accountable.
This report suggests various steps that different stakeholders can take to improve the verifiability of claims made about AI systems.
We analyze ten mechanisms for this purpose--spanning institutions, software, and hardware--and make recommendations aimed at implementing, exploring, or improving those mechanisms.
arXiv Detail & Related papers (2020-04-15T17:15:35Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.