The Contribution of XAI for the Safe Development and Certification of AI: An Expert-Based Analysis

• URL: http://arxiv.org/abs/2408.02379v1
• Date: Mon, 22 Jul 2024 16:08:21 GMT
• Title: The Contribution of XAI for the Safe Development and Certification of AI: An Expert-Based Analysis
• Authors: Benjamin Fresz, Vincent Philipp Göbels, Safa Omri, Danilo Brajovic, Andreas Aichele, Janika Kutz, Jens Neuhüttler, Marco F. Huber,
• Abstract summary: The black-box nature of machine learning models limits the use of conventional avenues of approach towards certifying complex technical systems. As a potential solution, methods to give insights into this black-box could be used. We find that XAI methods can be a helpful asset for safe AI development, but since certification relies on comprehensive and correct information about technical systems, their impact is expected to be limited.
• Score: 4.119574613934122
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Developing and certifying safe - or so-called trustworthy - AI has become an increasingly salient issue, especially in light of upcoming regulation such as the EU AI Act. In this context, the black-box nature of machine learning models limits the use of conventional avenues of approach towards certifying complex technical systems. As a potential solution, methods to give insights into this black-box - devised in the field of eXplainable AI (XAI) - could be used. In this study, the potential and shortcomings of such methods for the purpose of safe AI development and certification are discussed in 15 qualitative interviews with experts out of the areas of (X)AI and certification. We find that XAI methods can be a helpful asset for safe AI development, as they can show biases and failures of ML-models, but since certification relies on comprehensive and correct information about technical systems, their impact is expected to be limited.

Related papers

• 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)
• Applications of Explainable artificial intelligence in Earth system science [12.454478986296152]
  This review aims to provide a foundational understanding of explainable AI (XAI) XAI offers a set of powerful tools that make the models more transparent. We identify four significant challenges that XAI faces within the Earth system science (ESS) A visionary outlook for ESS envisions a harmonious blend where process-based models govern the known, AI models explore the unknown, and XAI bridges the gap by providing explanations.
  arXiv  Detail & Related papers  (2024-06-12T15:05:29Z)
• Evolutionary Computation and Explainable AI: A Roadmap to Understandable Intelligent Systems [37.02462866600066]
  Evolutionary computation (EC) offers significant potential to contribute to explainable AI (XAI) This paper provides an introduction to XAI and reviews current techniques for explaining machine learning models. We then explore how EC can be leveraged in XAI and examine existing XAI approaches that incorporate EC techniques.
  arXiv  Detail & Related papers  (2024-06-12T02:06:24Z)
• 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)
• Explainable Artificial Intelligence Techniques for Accurate Fault Detection and Diagnosis: A Review [0.0]
  We review the eXplainable AI (XAI) tools and techniques in this context. We focus on their role in making AI decision-making transparent, particularly in critical scenarios where humans are involved. We discuss current limitations and potential future research that aims to balance explainability with model performance.
  arXiv  Detail & Related papers  (2024-04-17T17:49:38Z)
• 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)
• Explainable AI for Safe and Trustworthy Autonomous Driving: A Systematic Review [12.38351931894004]
  We present the first systematic literature review of explainable methods for safe and trustworthy autonomous driving. We identify five key contributions of XAI for safe and trustworthy AI in AD, which are interpretable design, interpretable surrogate models, interpretable monitoring, auxiliary explanations, and interpretable validation. We propose a modular framework called SafeX to integrate these contributions, enabling explanation delivery to users while simultaneously ensuring the safety of AI models.
  arXiv  Detail & Related papers  (2024-02-08T09:08:44Z)
• 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)
• 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)
• Seamful XAI: Operationalizing Seamful Design in Explainable AI [59.89011292395202]
  Mistakes in AI systems are inevitable, arising from both technical limitations and sociotechnical gaps. We propose that seamful design can foster AI explainability by revealing sociotechnical and infrastructural mismatches. We explore this process with 43 AI practitioners and real end-users.
  arXiv  Detail & Related papers  (2022-11-12T21:54:05Z)
• 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.