Identifying the Supply Chain of AI for Trustworthiness and Risk Management in Critical Applications

• URL: http://arxiv.org/abs/2511.15763v1
• Date: Wed, 19 Nov 2025 14:52:02 GMT
• Title: Identifying the Supply Chain of AI for Trustworthiness and Risk Management in Critical Applications
• Authors: Raymond K. Sheh, Karen Geappen,
• Abstract summary: Gap exists in the systematic assessment of supply chain risks associated with the complex web of data sources, pre-trained models, agents, services, and other systems that contribute to the output of modern AI systems.<n>We survey the current state of AI risk assessment and management, with a focus on the supply chain of AI and risks relating to the behavior and outputs of the AI system.<n>We present a proposed taxonomy specifically for categorizing AI supply chain entities.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Risks associated with the use of AI, ranging from algorithmic bias to model hallucinations, have received much attention and extensive research across the AI community, from researchers to end-users. However, a gap exists in the systematic assessment of supply chain risks associated with the complex web of data sources, pre-trained models, agents, services, and other systems that contribute to the output of modern AI systems. This gap is particularly problematic when AI systems are used in critical applications, such as the food supply, healthcare, utilities, law, insurance, and transport. We survey the current state of AI risk assessment and management, with a focus on the supply chain of AI and risks relating to the behavior and outputs of the AI system. We then present a proposed taxonomy specifically for categorizing AI supply chain entities. This taxonomy helps stakeholders, especially those without extensive AI expertise, to "consider the right questions" and systematically inventory dependencies across their organization's AI systems. Our contribution bridges a gap between the current state of AI governance and the urgent need for actionable risk assessment and management of AI use in critical applications.

Related papers

• Federated Agentic AI for Wireless Networks: Fundamentals, Approaches, and Applications [60.721304295812445]
  Federated learning (FL) has the potential to improve the overall loop of agentic AI.<n>We first summarize fundamentals of agentic AI and mainstream FL types. Then, we illustrate how each FL type can strengthen a specific component of agentic AI's loop.<n>We conduct a case study on using FRL to improve the performance of agentic AI's action decision in low-altitude wireless networks.
  arXiv  Detail & Related papers  (2026-03-02T11:26:56Z)
• Barbarians at the Gate: How AI is Upending Systems Research [58.95406995634148]
  We argue that systems research, long focused on designing and evaluating new performance-oriented algorithms, is particularly well-suited for AI-driven solution discovery.<n>We term this approach as AI-Driven Research for Systems ( ADRS), which iteratively generates, evaluates, and refines solutions.<n>Our results highlight both the disruptive potential and the urgent need to adapt systems research practices in the age of AI.
  arXiv  Detail & Related papers  (2025-10-07T17:49:24Z)
• An Artificial Intelligence Value at Risk Approach: Metrics and Models [0.0]
  The state of the art of artificial intelligence risk management seems to be highly immature due to upcoming AI regulations.<n>The purpose of this paper is to orientate AI stakeholders about the depths of AI risk management.
  arXiv  Detail & Related papers  (2025-09-22T20:27:29Z)
• Envisioning an AI-Enhanced Mental Health Ecosystem [1.534667887016089]
  We explore various AI applications in peer support, self-help interventions, proactive monitoring, and data-driven insights.<n>We propose a hybrid ecosystem where AI assists but does not replace human providers, emphasising responsible deployment and evaluation.
  arXiv  Detail & Related papers  (2025-03-19T04:21:38Z)
• AI Risk Atlas: Taxonomy and Tooling for Navigating AI Risks and Resources [24.502423087280008]
  We introduce the AI Risk Atlas, a structured taxonomy that consolidates AI risks from diverse sources and aligns them with governance frameworks.<n>We also present the Risk Atlas Nexus, a collection of open-source tools designed to bridge the divide between risk definitions, benchmarks, datasets, and mitigation strategies.
  arXiv  Detail & Related papers  (2025-02-26T12:23:14Z)
• Position: Mind the Gap-the Growing Disconnect Between Established Vulnerability Disclosure and AI Security [56.219994752894294]
  We argue that adapting existing processes for AI security reporting is doomed to fail due to fundamental shortcomings for the distinctive characteristics of AI systems.<n>Based on our proposal to address these shortcomings, we discuss an approach to AI security reporting and how the new AI paradigm, AI agents, will further reinforce the need for specialized AI security incident reporting advancements.
  arXiv  Detail & Related papers  (2024-12-19T13:50:26Z)
• Risks and Opportunities of Open-Source Generative AI [64.86989162783648]
  Applications of Generative AI (Gen 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 the potential risks of the technology, 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  (2024-05-14T13:37:36Z)
• Testing autonomous vehicles and AI: perspectives and challenges from cybersecurity, transparency, robustness and fairness [53.91018508439669]
  The study explores the complexities of integrating Artificial Intelligence into Autonomous Vehicles (AVs) It examines the challenges introduced by AI components and the impact on testing procedures. The paper identifies significant challenges and suggests future directions for research and development of AI in AV technology.
  arXiv  Detail & Related papers  (2024-02-21T08:29:42Z)
• 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)
• 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.