Accountability of Robust and Reliable AI-Enabled Systems: A Preliminary Study and Roadmap

• URL: http://arxiv.org/abs/2506.16831v1
• Date: Fri, 20 Jun 2025 08:35:11 GMT
• Title: Accountability of Robust and Reliable AI-Enabled Systems: A Preliminary Study and Roadmap
• Authors: Filippo Scaramuzza, Damian A. Tamburri, Willem-Jan van den Heuvel,
• Abstract summary: This vision paper presents initial research on assessing the robustness and reliability of AI-enabled systems.<n>By exploring evolving definitions of these concepts and reviewing current literature, the study highlights major challenges and approaches in the field.<n>The incorporation of accountability is crucial for building trust and ensuring responsible AI development.
• Score: 1.8816378259778017
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This vision paper presents initial research on assessing the robustness and reliability of AI-enabled systems, and key factors in ensuring their safety and effectiveness in practical applications, including a focus on accountability. By exploring evolving definitions of these concepts and reviewing current literature, the study highlights major challenges and approaches in the field. A case study is used to illustrate real-world applications, emphasizing the need for innovative testing solutions. The incorporation of accountability is crucial for building trust and ensuring responsible AI development. The paper outlines potential future research directions and identifies existing gaps, positioning robustness, reliability, and accountability as vital areas for the development of trustworthy AI systems of the future.

Related papers

• The AI Imperative: Scaling High-Quality Peer Review in Machine Learning [49.87236114682497]
  We argue that AI-assisted peer review must become an urgent research and infrastructure priority.<n>We propose specific roles for AI in enhancing factual verification, guiding reviewer performance, assisting authors in quality improvement, and supporting ACs in decision-making.
  arXiv  Detail & Related papers  (2025-06-09T18:37:14Z)
• 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)
• Towards Trustworthy GUI Agents: A Survey [64.6445117343499]
  This survey examines the trustworthiness of GUI agents in five critical dimensions.<n>We identify major challenges such as vulnerability to adversarial attacks, cascading failure modes in sequential decision-making.<n>As GUI agents become more widespread, establishing robust safety standards and responsible development practices is essential.
  arXiv  Detail & Related papers  (2025-03-30T13:26:00Z)
• Towards Trustworthy Retrieval Augmented Generation for Large Language Models: A Survey [92.36487127683053]
  Retrieval-Augmented Generation (RAG) is an advanced technique designed to address the challenges of Artificial Intelligence-Generated Content (AIGC)<n>RAG provides reliable and up-to-date external knowledge, reduces hallucinations, and ensures relevant context across a wide range of tasks.<n>Despite RAG's success and potential, recent studies have shown that the RAG paradigm also introduces new risks, including privacy concerns, adversarial attacks, and accountability issues.
  arXiv  Detail & Related papers  (2025-02-08T06:50:47Z)
• Safety is Essential for Responsible Open-Ended Systems [47.172735322186]
  Open-Endedness is the ability of AI systems to continuously and autonomously generate novel and diverse artifacts or solutions.<n>This position paper argues that the inherently dynamic and self-propagating nature of Open-Ended AI introduces significant, underexplored risks.
  arXiv  Detail & Related papers  (2025-02-06T21:32:07Z)
• Confronting the Reproducibility Crisis: A Case Study of Challenges in Cybersecurity AI [0.0]
  A key area in AI-based cybersecurity focuses on defending deep neural networks against malicious perturbations. We attempt to validate results from prior work on certified robustness using the VeriGauge toolkit. Our findings underscore the urgent need for standardized methodologies, containerization, and comprehensive documentation.
  arXiv  Detail & Related papers  (2024-05-29T04:37:19Z)
• What is Reproducibility in Artificial Intelligence and Machine Learning Research? [0.7373617024876725]
  We introduce a framework that clarifies the roles and definitions of key validation efforts.<n>This structured framework aims to provide AI/ML researchers with the necessary clarity on these essential concepts.
  arXiv  Detail & Related papers  (2024-04-29T18:51:20Z)
• 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)
• Predictable Artificial Intelligence [77.1127726638209]
  This paper introduces the ideas and challenges of Predictable AI.<n>It explores the ways in which we can anticipate key validity indicators of present and future AI ecosystems.<n>We argue that achieving predictability is crucial for fostering trust, liability, control, alignment and safety of AI ecosystems.
  arXiv  Detail & Related papers  (2023-10-09T21:36:21Z)
• Statistical Perspectives on Reliability of Artificial Intelligence Systems [6.284088451820049]
  We provide statistical perspectives on the reliability of AI systems. We introduce a so-called SMART statistical framework for AI reliability research. We discuss recent developments in modeling and analysis of AI reliability.
  arXiv  Detail & Related papers  (2021-11-09T20:00:14Z)

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.