The Uneasy Marriage of AI and Dependability: Integrating Taxonomy and Methods for Dependability and Accuracy Enhancement

[2608.14564] The Uneasy Marriage of AI and Dependability: Integrating Taxonomy and Methods for Dependability and Accuracy Enhancement Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Distributed, Parallel, and Cluster Computing arXiv:2608.14564 (cs) [Submitted on 27 May 2026] Title: The Uneasy Marriage of AI and Dependability: Integrating Taxonomy and Methods for Dependability and Accuracy Enhancement Authors: Aad van Moorsel View a PDF of the paper titled The Uneasy Marriage of AI and Dependability: Integrating Taxonomy and Methods for Dependability and Accuracy Enhancement, by Aad van Moorsel View PDF HTML (experimental) Abstract: In this paper we discuss the connection between fault-tolerance mechanisms in traditional computer systems, and approaches in accuracy enhancement for AI-based services. We will find that AI mechanisms such as ensembles and reject option have direct counterparts in hardware and software dependability through N-modular redundancy and acceptance tests, even though their motivation, justification and implementation are quite different. We augment the traditional dependability taxonomy to include critical defining features of faults and failures in AI-based services. We propose to consider incorrect outcomes from AI as errors, even if the system hardware and software operates error free. AI then becomes a third system layer (after hardware and software) for which dependability needs to be considered, and for which dependability has specific characteristics. The existing fault classes in the dependability taxonomy are not suited for AI, and we propose to introduce AI Output Faults, representing the inherent possibly incorrect (and therefore faulty) outcome of AI algorithms. We then map and compare fault tolerance mechanisms with AI accuracy enhancement mechanisms, and we see they carry strike resemblances. We hope the work presented in this paper will help in establishing a truly integrated and unified understanding of dependability for modern-day AI-based systems. Subjects: Distributed, Parallel, and Cluster Computing (cs.DC) Cite as: arXiv:2608.14564 [cs.DC] (or arXiv:2608.14564v1 [cs.DC] for this version) https://doi.org/10.48550/arXiv.2608.14564 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Aad Van Moorsel [ view email ] [v1] Wed, 27 May 2026 16:28:48 UTC (241 KB) Full-text links: Access Paper: View a PDF of the paper titled The Uneasy Marriage of AI and Dependability: Integrating Taxonomy and Methods for Dependability and Accuracy Enhancement, by Aad van Moorsel View PDF HTML (experimental) TeX Source view license Current browse context: cs.DC < prev | next > new | recent | 2026-08 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

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