Catastrophic Liability: Managing Systemic Risks in Frontier AI Development
- URL: http://arxiv.org/abs/2505.00616v1
- Date: Thu, 01 May 2025 15:47:14 GMT
- Title: Catastrophic Liability: Managing Systemic Risks in Frontier AI Development
- Authors: Aidan Kierans, Kaley Rittichier, Utku Sonsayar,
- Abstract summary: frontier AI development poses potential systemic risks that could affect society at a massive scale.<n>Current practices at many AI labs lack sufficient transparency around safety measures, testing procedures, and governance structures.<n>We propose a comprehensive approach to safety documentation and accountability in frontier AI development.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: As artificial intelligence systems grow more capable and autonomous, frontier AI development poses potential systemic risks that could affect society at a massive scale. Current practices at many AI labs developing these systems lack sufficient transparency around safety measures, testing procedures, and governance structures. This opacity makes it challenging to verify safety claims or establish appropriate liability when harm occurs. Drawing on liability frameworks from nuclear energy, aviation software, and healthcare, we propose a comprehensive approach to safety documentation and accountability in frontier AI development.
Related papers
- A Framework for the Assurance of AI-Enabled Systems [0.0]
This paper proposes a claims-based framework for risk management and assurance of AI systems.
The paper's contributions are a framework process for AI assurance, a set of relevant definitions, and a discussion of important considerations in AI assurance.
arXiv Detail & Related papers (2025-04-03T13:44:01Z) - An Approach to Technical AGI Safety and Security [72.83728459135101]
We develop an approach to address the risk of harms consequential enough to significantly harm humanity.<n>We focus on technical approaches to misuse and misalignment.<n>We briefly outline how these ingredients could be combined to produce safety cases for AGI systems.
arXiv Detail & Related papers (2025-04-02T15:59:31Z) - AI threats to national security can be countered through an incident regime [55.2480439325792]
We propose a legally mandated post-deployment AI incident regime that aims to counter potential national security threats from AI systems.
Our proposed AI incident regime is split into three phases. The first phase revolves around a novel operationalization of what counts as an 'AI incident'
The second and third phases spell out that AI providers should notify a government agency about incidents, and that the government agency should be involved in amending AI providers' security and safety procedures.
arXiv Detail & Related papers (2025-03-25T17:51:50Z) - Engineering Trustworthy AI: A Developer Guide for Empirical Risk Minimization [53.80919781981027]
Key requirements for trustworthy AI can be translated into design choices for the components of empirical risk minimization.
We hope to provide actionable guidance for building AI systems that meet emerging standards for trustworthiness of AI.
arXiv Detail & Related papers (2024-10-25T07:53:32Z) - EARBench: Towards Evaluating Physical Risk Awareness for Task Planning of Foundation Model-based Embodied AI Agents [53.717918131568936]
Embodied artificial intelligence (EAI) integrates advanced AI models into physical entities for real-world interaction.<n>Foundation models as the "brain" of EAI agents for high-level task planning have shown promising results.<n>However, the deployment of these agents in physical environments presents significant safety challenges.<n>This study introduces EARBench, a novel framework for automated physical risk assessment in EAI scenarios.
arXiv Detail & Related papers (2024-08-08T13:19:37Z) - Materiality and Risk in the Age of Pervasive AI Sensors [9.180189171335744]
We highlight the dimensions of risk associated with AI systems that arise from the material affordances of sensors and their underlying calculative models.<n>We propose a sensor-sensitive framework for diagnosing these risks, complementing existing approaches such as the US National Institute of Standards and Technology AI Risk Management Framework and the European Union AI Act.
arXiv Detail & Related papers (2024-02-17T03:47:38Z) - 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) - AI Hazard Management: A framework for the systematic management of root
causes for AI risks [0.0]
This paper introduces the AI Hazard Management (AIHM) framework.
It provides a structured process to systematically identify, assess, and treat AI hazards.
It builds upon an AI hazard list from a comprehensive state-of-the-art analysis.
arXiv Detail & Related papers (2023-10-25T15:55:50Z) - Safe AI -- How is this Possible? [0.45687771576879593]
Traditional safety engineering is coming to a turning point moving from deterministic, non-evolving systems operating in well-defined contexts to increasingly autonomous and learning-enabled AI systems acting in largely unpredictable operating contexts.
We outline some of underlying challenges of safe AI and suggest a rigorous engineering framework for minimizing uncertainty, thereby increasing confidence, up to tolerable levels, in the safe behavior of AI systems.
arXiv Detail & Related papers (2022-01-25T16:32:35Z) - 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.