Responsible Reporting for Frontier AI Development
- URL: http://arxiv.org/abs/2404.02675v1
- Date: Wed, 3 Apr 2024 12:18:45 GMT
- Title: Responsible Reporting for Frontier AI Development
- Authors: Noam Kolt, Markus Anderljung, Joslyn Barnhart, Asher Brass, Kevin Esvelt, Gillian K. Hadfield, Lennart Heim, Mikel Rodriguez, Jonas B. Sandbrink, Thomas Woodside,
- Abstract summary: Mitigating the risks from frontier AI systems requires up-to-date and reliable information about those systems.
Organizations that develop and deploy frontier systems have significant access to such information.
By reporting safety-critical information to actors in government, industry, and civil society, these organizations could improve visibility into new and emerging risks posed by frontier systems.
- Score: 2.6591642690968067
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Mitigating the risks from frontier AI systems requires up-to-date and reliable information about those systems. Organizations that develop and deploy frontier systems have significant access to such information. By reporting safety-critical information to actors in government, industry, and civil society, these organizations could improve visibility into new and emerging risks posed by frontier systems. Equipped with this information, developers could make better informed decisions on risk management, while policymakers could design more targeted and robust regulatory infrastructure. We outline the key features of responsible reporting and propose mechanisms for implementing them in practice.
Related papers
- Multi-Agent Risks from Advanced AI [90.74347101431474]
Multi-agent systems of advanced AI pose novel and under-explored risks.
We identify three key failure modes based on agents' incentives, as well as seven key risk factors.
We highlight several important instances of each risk, as well as promising directions to help mitigate them.
arXiv Detail & Related papers (2025-02-19T23:03:21Z) - Open Problems in Machine Unlearning for AI Safety [61.43515658834902]
Machine unlearning -- the ability to selectively forget or suppress specific types of knowledge -- has shown promise for privacy and data removal tasks.
In this paper, we identify key limitations that prevent unlearning from serving as a comprehensive solution for AI safety.
arXiv Detail & Related papers (2025-01-09T03:59:10Z) - Towards Responsible Governing AI Proliferation [0.0]
The paper introduces the Proliferation' paradigm, which anticipates the rise of smaller, decentralized, open-sourced AI models.
It posits that these developments are both probable and likely to introduce both benefits and novel risks.
arXiv Detail & Related papers (2024-12-18T13:10:35Z) - Position Paper: Model Access should be a Key Concern in AI Governance [0.0]
downstream use cases, benefits, and risks of AI systems depend significantly on the access afforded to the system, and to whom.
We spotlight Model Access Governance, an emerging field focused on helping organisations and governments make responsible, evidence-based access decisions.
We make four sets of recommendations, aimed at helping AI evaluation organisations, frontier AI companies, governments and international bodies build consensus around empirically-driven access governance.
arXiv Detail & Related papers (2024-12-01T14:59:07Z) - Security Threats in Agentic AI System [0.0]
The complexity of AI systems combined with their ability to process and analyze large volumes of data increases the chances of data leaks or breaches.
As AI agents evolve with greater autonomy, their capacity to bypass or exploit security measures becomes a growing concern.
arXiv Detail & Related papers (2024-10-16T06:40:02Z) - A Framework for Exploring the Consequences of AI-Mediated Enterprise Knowledge Access and Identifying Risks to Workers [3.4568218861862556]
This paper presents the Consequence-Mechanism-Risk framework to identify risks to workers from AI-mediated enterprise knowledge access systems.
We have drawn on wide-ranging literature detailing risks to workers, and categorised risks as being to worker value, power, and wellbeing.
Future work could apply this framework to other technological systems to promote the protection of workers and other groups.
arXiv Detail & Related papers (2023-12-08T17:05:40Z) - The risks of risk-based AI regulation: taking liability seriously [46.90451304069951]
The development and regulation of AI seems to have reached a critical stage.
Some experts are calling for a moratorium on the training of AI systems more powerful than GPT-4.
This paper analyses the most advanced legal proposal, the European Union's AI Act.
arXiv Detail & Related papers (2023-11-03T12:51:37Z) - ThreatKG: An AI-Powered System for Automated Open-Source Cyber Threat Intelligence Gathering and Management [65.0114141380651]
ThreatKG is an automated system for OSCTI gathering and management.
It efficiently collects a large number of OSCTI reports from multiple sources.
It uses specialized AI-based techniques to extract high-quality knowledge about various threat entities.
arXiv Detail & Related papers (2022-12-20T16:13:59Z) - Trustworthy AI Inference Systems: An Industry Research View [58.000323504158054]
We provide an industry research view for approaching the design, deployment, and operation of trustworthy AI inference systems.
We highlight opportunities and challenges in AI systems using trusted execution environments.
We outline areas of further development that require the global collective attention of industry, academia, and government researchers.
arXiv Detail & Related papers (2020-08-10T23:05:55Z) - 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.