Two Types of AI Existential Risk: Decisive and Accumulative
- URL: http://arxiv.org/abs/2401.07836v3
- Date: Fri, 17 Jan 2025 16:35:27 GMT
- Title: Two Types of AI Existential Risk: Decisive and Accumulative
- Authors: Atoosa Kasirzadeh,
- Abstract summary: This paper contrasts the conventional "decisive AI x-risk hypothesis" with an "accumulative AI x-risk hypothesis"
It argues that the accumulative view can reconcile seemingly incompatible perspectives on AI risks.
- Score: 3.5051464966389116
- License:
- Abstract: The conventional discourse on existential risks (x-risks) from AI typically focuses on abrupt, dire events caused by advanced AI systems, particularly those that might achieve or surpass human-level intelligence. These events have severe consequences that either lead to human extinction or irreversibly cripple human civilization to a point beyond recovery. This discourse, however, often neglects the serious possibility of AI x-risks manifesting incrementally through a series of smaller yet interconnected disruptions, gradually crossing critical thresholds over time. This paper contrasts the conventional "decisive AI x-risk hypothesis" with an "accumulative AI x-risk hypothesis." While the former envisions an overt AI takeover pathway, characterized by scenarios like uncontrollable superintelligence, the latter suggests a different causal pathway to existential catastrophes. This involves a gradual accumulation of critical AI-induced threats such as severe vulnerabilities and systemic erosion of economic and political structures. The accumulative hypothesis suggests a boiling frog scenario where incremental AI risks slowly converge, undermining societal resilience until a triggering event results in irreversible collapse. Through systems analysis, this paper examines the distinct assumptions differentiating these two hypotheses. It is then argued that the accumulative view can reconcile seemingly incompatible perspectives on AI risks. The implications of differentiating between these causal pathways -- the decisive and the accumulative -- for the governance of AI as well as long-term AI safety are discussed.
Related papers
- Fully Autonomous AI Agents Should Not be Developed [58.88624302082713]
This paper argues that fully autonomous AI agents should not be developed.
In support of this position, we build from prior scientific literature and current product marketing to delineate different AI agent levels.
Our analysis reveals that risks to people increase with the autonomy of a system.
arXiv Detail & Related papers (2025-02-04T19:00:06Z) - Hype, Sustainability, and the Price of the Bigger-is-Better Paradigm in AI [67.58673784790375]
We argue that the 'bigger is better' AI paradigm is not only fragile scientifically, but comes with undesirable consequences.
First, it is not sustainable, as its compute demands increase faster than model performance, leading to unreasonable economic requirements and a disproportionate environmental footprint.
Second, it implies focusing on certain problems at the expense of others, leaving aside important applications, e.g. health, education, or the climate.
arXiv Detail & Related papers (2024-09-21T14:43:54Z) - AI Safety: A Climb To Armageddon? [0.0]
The paper examines three response strategies: Optimism, Mitigation, and Holism.
The surprising robustness of the argument forces a re-examination of core assumptions around AI safety.
arXiv Detail & Related papers (2024-05-30T08:41:54Z) - Near to Mid-term Risks and Opportunities of Open-Source Generative AI [94.06233419171016]
Applications of Generative AI are expected to revolutionize a number of different areas, ranging from science & medicine to education.
The potential for these seismic changes has triggered a lively debate about potential risks and resulted in calls for tighter regulation.
This regulation is likely to put at risk the budding field of open-source Generative AI.
arXiv Detail & Related papers (2024-04-25T21:14:24Z) - Artificial Intelligence: Arguments for Catastrophic Risk [0.0]
We review two influential arguments purporting to show how AI could pose catastrophic risks.
The first argument -- the Problem of Power-Seeking -- claims that advanced AI systems are likely to engage in dangerous power-seeking behavior.
The second argument claims that the development of human-level AI will unlock rapid further progress.
arXiv Detail & Related papers (2024-01-27T19:34:13Z) - 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) - An Overview of Catastrophic AI Risks [38.84933208563934]
This paper provides an overview of the main sources of catastrophic AI risks, which we organize into four categories.
Malicious use, in which individuals or groups intentionally use AIs to cause harm; AI race, in which competitive environments compel actors to deploy unsafe AIs or cede control to AIs.
organizational risks, highlighting how human factors and complex systems can increase the chances of catastrophic accidents.
rogue AIs, describing the inherent difficulty in controlling agents far more intelligent than humans.
arXiv Detail & Related papers (2023-06-21T03:35:06Z) - Fairness in AI and Its Long-Term Implications on Society [68.8204255655161]
We take a closer look at AI fairness and analyze how lack of AI fairness can lead to deepening of biases over time.
We discuss how biased models can lead to more negative real-world outcomes for certain groups.
If the issues persist, they could be reinforced by interactions with other risks and have severe implications on society in the form of social unrest.
arXiv Detail & Related papers (2023-04-16T11:22:59Z) - Current and Near-Term AI as a Potential Existential Risk Factor [5.1806669555925975]
We problematise the notion that current and near-term artificial intelligence technologies have the potential to contribute to existential risk.
We propose the hypothesis that certain already-documented effects of AI can act as existential risk factors.
Our main contribution is an exposition of potential AI risk factors and the causal relationships between them.
arXiv Detail & Related papers (2022-09-21T18:56:14Z) - Adversarial Interaction Attack: Fooling AI to Misinterpret Human
Intentions [46.87576410532481]
We show that, despite their current huge success, deep learning based AI systems can be easily fooled by subtle adversarial noise.
Based on a case study of skeleton-based human interactions, we propose a novel adversarial attack on interactions.
Our study highlights potential risks in the interaction loop with AI and humans, which need to be carefully addressed when deploying AI systems in safety-critical applications.
arXiv Detail & Related papers (2021-01-17T16:23:20Z)
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.