Quantifying AI Vulnerabilities: A Synthesis of Complexity, Dynamical Systems, and Game Theory
- URL: http://arxiv.org/abs/2404.10782v1
- Date: Sun, 7 Apr 2024 07:05:59 GMT
- Title: Quantifying AI Vulnerabilities: A Synthesis of Complexity, Dynamical Systems, and Game Theory
- Authors: B Kereopa-Yorke,
- Abstract summary: We propose a novel approach that introduces three metrics: System Complexity Index (SCI), Lyapunov Exponent for AI Stability (LEAIS), and Nash Equilibrium Robustness (NER)
SCI quantifies the inherent complexity of an AI system, LEAIS captures its stability and sensitivity to perturbations, and NER evaluates its strategic robustness against adversarial manipulation.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The rapid integration of Artificial Intelligence (AI) systems across critical domains necessitates robust security evaluation frameworks. We propose a novel approach that introduces three metrics: System Complexity Index (SCI), Lyapunov Exponent for AI Stability (LEAIS), and Nash Equilibrium Robustness (NER). SCI quantifies the inherent complexity of an AI system, LEAIS captures its stability and sensitivity to perturbations, and NER evaluates its strategic robustness against adversarial manipulation. Through comparative analysis, we demonstrate the advantages of our framework over existing techniques. We discuss the theoretical and practical implications, potential applications, limitations, and future research directions. Our work contributes to the development of secure and trustworthy AI technologies by providing a holistic, theoretically grounded approach to AI security evaluation. As AI continues to advance, prioritising and advancing AI security through interdisciplinary collaboration is crucial to ensure its responsible deployment for the benefit of society.
Related papers
- Imagining and building wise machines: The centrality of AI metacognition [78.76893632793497]
We argue that shortcomings stem from one overarching failure: AI systems lack wisdom.
While AI research has focused on task-level strategies, metacognition is underdeveloped in AI systems.
We propose that integrating metacognitive capabilities into AI systems is crucial for enhancing their robustness, explainability, cooperation, and safety.
arXiv Detail & Related papers (2024-11-04T18:10:10Z) - 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) - Trustworthy, Responsible, and Safe AI: A Comprehensive Architectural Framework for AI Safety with Challenges and Mitigations [14.150792596344674]
AI Safety is an emerging area of critical importance to the safe adoption and deployment of AI systems.
Our goal is to promote advancement in AI safety research, and ultimately enhance people's trust in digital transformation.
arXiv Detail & Related papers (2024-08-23T09:33:48Z) - 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) - 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 Potentiality and Awareness: A Position Paper from the Perspective of
Human-AI Teaming in Cybersecurity [18.324118502535775]
We argue that human-AI teaming is worthwhile in cybersecurity.
We emphasize the importance of a balanced approach that incorporates AI's computational power with human expertise.
arXiv Detail & Related papers (2023-09-28T01:20:44Z) - AI Maintenance: A Robustness Perspective [91.28724422822003]
We introduce highlighted robustness challenges in the AI lifecycle and motivate AI maintenance by making analogies to car maintenance.
We propose an AI model inspection framework to detect and mitigate robustness risks.
Our proposal for AI maintenance facilitates robustness assessment, status tracking, risk scanning, model hardening, and regulation throughout the AI lifecycle.
arXiv Detail & Related papers (2023-01-08T15:02:38Z) - Proceedings of the Robust Artificial Intelligence System Assurance
(RAISA) Workshop 2022 [0.0]
The RAISA workshop will focus on research, development and application of robust artificial intelligence (AI) and machine learning (ML) systems.
Rather than studying robustness with respect to particular ML algorithms, our approach will be to explore robustness assurance at the system architecture level.
arXiv Detail & Related papers (2022-02-10T01:15:50Z) - Trustworthy AI: From Principles to Practices [44.67324097900778]
Many current AI systems were found vulnerable to imperceptible attacks, biased against underrepresented groups, lacking in user privacy protection, etc.
In this review, we strive to provide AI practitioners a comprehensive guide towards building trustworthy AI systems.
To unify the current fragmented approaches towards trustworthy AI, we propose a systematic approach that considers the entire lifecycle of AI systems.
arXiv Detail & Related papers (2021-10-04T03:20:39Z) - An interdisciplinary conceptual study of Artificial Intelligence (AI)
for helping benefit-risk assessment practices: Towards a comprehensive
qualification matrix of AI programs and devices (pre-print 2020) [55.41644538483948]
This paper proposes a comprehensive analysis of existing concepts coming from different disciplines tackling the notion of intelligence.
The aim is to identify shared notions or discrepancies to consider for qualifying AI systems.
arXiv Detail & Related papers (2021-05-07T12:01:31Z) - AAAI FSS-19: Human-Centered AI: Trustworthiness of AI Models and Data
Proceedings [8.445274192818825]
It is crucial for predictive models to be uncertainty-aware and yield trustworthy predictions.
The focus of this symposium was on AI systems to improve data quality and technical robustness and safety.
submissions from broadly defined areas also discussed approaches addressing requirements such as explainable models, human trust and ethical aspects of AI.
arXiv Detail & Related papers (2020-01-15T15:30:29Z)
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.