Rethinking Technological Readiness in the Era of AI Uncertainty

• URL: http://arxiv.org/abs/2506.11001v1
• Date: Tue, 15 Apr 2025 14:09:50 GMT
• Title: Rethinking Technological Readiness in the Era of AI Uncertainty
• Authors: S. Tucker Browne, Mark M. Bailey,
• Abstract summary: We argue that current technology readiness assessments fail to capture critical AI-specific factors.<n>We propose a new AI Readiness Framework to evaluate the maturity and trustworthiness of AI components in military systems.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Artificial intelligence (AI) is poised to revolutionize military combat systems, but ensuring these AI-enabled capabilities are truly mission-ready presents new challenges. We argue that current technology readiness assessments fail to capture critical AI-specific factors, leading to potential risks in deployment. We propose a new AI Readiness Framework to evaluate the maturity and trustworthiness of AI components in military systems. The central thesis is that a tailored framework - analogous to traditional Technology Readiness Levels (TRL) but expanded for AI - can better gauge an AI system's reliability, safety, and suitability for combat use. Using current data evaluation tools and testing practices, we demonstrate the framework's feasibility for near-term implementation. This structured approach provides military decision-makers with clearer insight into whether an AI-enabled system has met the necessary standards of performance, transparency, and human integration to be deployed with confidence, thus advancing the field of defense technology management and risk assessment.

Related papers

• Bridging Ethical Principles and Algorithmic Methods: An Alternative Approach for Assessing Trustworthiness in AI Systems [0.0]
  This paper introduces an assessment method that combines the ethical components of Trustworthy AI with the algorithmic processes of PageRank and TrustRank.<n>The goal is to establish an assessment framework that minimizes the subjectivity inherent in the self-assessment techniques prevalent in the field.
  arXiv  Detail & Related papers  (2025-06-28T06:27:30Z)
• 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.<n>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)
• Decoding the Black Box: Integrating Moral Imagination with Technical AI Governance [0.0]
  We develop a comprehensive framework designed to regulate AI technologies deployed in high-stakes domains such as defense, finance, healthcare, and education.<n>Our approach combines rigorous technical analysis, quantitative risk assessment, and normative evaluation to expose systemic vulnerabilities.
  arXiv  Detail & Related papers  (2025-03-09T03:11:32Z)
• Imagining and building wise machines: The centrality of AI metacognition [78.76893632793497]
  We examine what is known about human wisdom and sketch a vision of its AI counterpart.<n>We argue that AI systems particularly struggle with metacognition.<n>We discuss how wise AI might be benchmarked, trained, and implemented.
  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)
• Combining AI Control Systems and Human Decision Support via Robustness and Criticality [53.10194953873209]
  We extend a methodology for adversarial explanations (AE) to state-of-the-art reinforcement learning frameworks. We show that the learned AI control system demonstrates robustness against adversarial tampering. In a training / learning framework, this technology can improve both the AI's decisions and explanations through human interaction.
  arXiv  Detail & Related papers  (2024-07-03T15:38:57Z)
• 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)
• Quantifying AI Vulnerabilities: A Synthesis of Complexity, Dynamical Systems, and Game Theory [0.0]
  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.
  arXiv  Detail & Related papers  (2024-04-07T07:05:59Z)
• 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)
• 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)

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.