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Safety Co-Option and Compromised National Security: The Self-Fulfilling Prophecy of Weakened AI Risk Thresholds [0.0]
We show how "safety revisionism" has allowed AI technologists to engage in "safety revisionism" We explore how the current trajectory for AI risk determination and evaluation for foundation model use within national security is poised for a race to the bottom.
arXiv Detail & Related papers (2025-04-21T13:20:56Z)
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)
AI Safety for Everyone [3.440579243843689]
Recent discussions and research in AI safety have increasingly emphasized the deep connection between AI safety and existential risk from advanced AI systems. This framing may exclude researchers and practitioners who are committed to AI safety but approach the field from different angles. We find a vast array of concrete safety work that addresses immediate and practical concerns with current AI systems.
arXiv Detail & Related papers (2025-02-13T13:04:59Z)
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)
Position: A taxonomy for reporting and describing AI security incidents [57.98317583163334]
We argue that specific are required to describe and report security incidents of AI systems. Existing frameworks for either non-AI security or generic AI safety incident reporting are insufficient to capture the specific properties of AI security.
arXiv Detail & Related papers (2024-12-19T13:50:26Z)
Landscape of AI safety concerns -- A methodology to support safety assurance for AI-based autonomous systems [0.0]
AI has emerged as a key technology, driving advancements across a range of applications. The challenge of assuring safety in systems that incorporate AI components is substantial. We propose a novel methodology designed to support the creation of safety assurance cases for AI-based systems.
arXiv Detail & Related papers (2024-12-18T16:38:16Z)
Safetywashing: Do AI Safety Benchmarks Actually Measure Safety Progress? [59.96471873997733]
We propose an empirical foundation for developing more meaningful safety metrics and define AI safety in a machine learning research context. We aim to provide a more rigorous framework for AI safety research, advancing the science of safety evaluations and clarifying the path towards measurable progress.
arXiv Detail & Related papers (2024-07-31T17:59:24Z)
AI Risk Management Should Incorporate Both Safety and Security [185.68738503122114]
We argue that stakeholders in AI risk management should be aware of the nuances, synergies, and interplay between safety and security. We introduce a unified reference framework to clarify the differences and interplay between AI safety and AI security.
arXiv Detail & Related papers (2024-05-29T21:00:47Z)
Towards Guaranteed Safe AI: A Framework for Ensuring Robust and Reliable AI Systems [88.80306881112313]
We will introduce and define a family of approaches to AI safety, which we will refer to as guaranteed safe (GS) AI. The core feature of these approaches is that they aim to produce AI systems which are equipped with high-assurance quantitative safety guarantees. We outline a number of approaches for creating each of these three core components, describe the main technical challenges, and suggest a number of potential solutions to them.
arXiv Detail & Related papers (2024-05-10T17:38:32Z)
System Safety and Artificial Intelligence [0.0]
New applications of AI across societal domains come with new hazards. The field of system safety has dealt with accidents and harm in safety-critical systems. This chapter honors system safety pioneer Nancy Leveson.
arXiv Detail & Related papers (2022-02-18T16:37:54Z)

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