[2304.14352] Epistemic reflections on AI answering our questions: overwatch, erudite, logician, interlocutor Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Computers and Society arXiv:2304.14352 (cs) [Submitted on 23 Apr 2023 ( v1 ), last revised 30 Apr 2026 (this version, v2)] Title: Epistemic reflections on AI answering our questions: overwatch, erudite, logician, interlocutor Authors: Johan F. Hoorn , Ella-Jenna Oosterglorenwoud View a PDF of the paper titled Epistemic reflections on AI answering our questions: overwatch, erudite, logician, interlocutor, by Johan F. Hoorn and Ella-Jenna Oosterglorenwoud View PDF Abstract: Currently, there is a trend for the wider public to rely on LLMs for financial or legal consultation, medical and mental support (Chatterji et al., 2025), often accepting the advice provided without necessarily seeking logical verification or empirical validation. While one might be fortunate enough to encounter a model with a particularly solid 'ground truth' or with auxiliary logic-symbolic reasoning capabilities, it remains a somewhat uncertain endeavour. Output is simply taken at face value, without further question. Yet, careless reliance on AI to answer our questions and to judge our output is a violation of Grice's Maxim of Quality as well as a violation of Lemoine's legal Maxim of Innocence. A low-sensitivity plagiarism scanner may produce a Type II error by failing to detect difference (the null hypothesis wrongly maintained). The fallacy of affirming the consequent occurs when the failure to detect difference is then interpreted as evidence of equivalence or demonstration of AI authorship. If the test is specified so that 'AI-generated' is effectively treated as the default H0, then a finding of 'no difference from AI' is taken as support for that null. Such a mis-specified test results in students being treated as guilty (AI/plagiarism) unless suspects can generate sufficient detectable difference from AI output, which yields false accusations under a fair null hypothesis (that the student wrote the work). To avoid LLMs becoming a sorcerer's apprentice, knowledge is required about which inference systems are or should become integrated for an LLM to become a trustworthy sparring partner. We end on a wider perspective where the formalisation of the observer effect shows that uncertainty, classification, and interpretation are already shaped by the human or artificial agency's belief system, affective state, and tolerance for ambiguity, rather than at the stage of LLM output. Comments: 22 pages, 1 figure Subjects: Computers and Society (cs.CY) ; Artificial Intelligence (cs.AI); Logic in Computer Science (cs.LO) Cite as: arXiv:2304.14352 [cs.CY] (or arXiv:2304.14352v2 [cs.CY] for this version) https://doi.org/10.48550/arXiv.2304.14352 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Johan F. Hoorn [ view email ] [v1] Sun, 23 Apr 2023 08:26:42 UTC (608 KB) [v2] Thu, 30 Apr 2026 09:26:47 UTC (729 KB) Full-text links: Access Paper: View a PDF of the paper titled Epistemic reflections on AI answering our questions: overwatch, erudite, logician, interlocutor, by Johan F. Hoorn and Ella-Jenna Oosterglorenwoud View PDF view license Current browse context: cs.CY < prev | next > new | recent | 2023-04 Change to browse by: cs cs.AI cs.LO 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