REVIEW 2 major objections 4 minor 2 references
Understanding and Evaluating Trust in Generative AI and Large Language Models for Spreadsheets
T0 review · 2 major / 4 minor · reviewed 2026-08-11 · deepseek-v4-flash
Pith's one-line read This paper proposes a transparency-and-dependability framework for judging whether to trust AI-generated spreadsheet formulas.
desk verdict A clear, honest taxonomy for thinking about trust in LLM-generated spreadsheet formulas, but the 'objective metrics' claim outruns what the paper actually specifies. read the letter →
The pith
A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.
The reading
What carries the argument
The central object is the Transparency and Trustworthiness Framework, a set of four evaluative dimensions: explainability (the AI's reasoning for a formula), visibility (inspection of model architecture, training data, and parameters), reliability (consistency and accuracy, assessable through benchmark tests), and ethical considerations (bias and fairness, assessable through audit tools). The framework does the work of turning an abstract attitude—trust—into checkable criteria, with prompt engineering as the main lever for improving the first two dimensions and benchmarking and auditing as the lever for the last two.
What would settle it
A concrete test: give spreadsheet experts a set of AI-generated formulas, half correct and half hallucinated, ask them to score each formula on explainability, visibility, reliability, and ethical considerations with a defined rubric, and check whether the scores predict correctness. If the scores do not separate good from bad formulas, or if different experts arrive at wildly different scores, the framework's objective-metric claim is undermined.
Extended reading notes
Core claim
The central claim is that existing trust-in-automation dimensions can be adapted into a workable framework for evaluating LLM-generated spreadsheet formulas. The framework groups trust into transparency (explainability of the formula's reasoning and visibility of the underlying model and data) and dependability (reliability of outputs under testing and ethical soundness regarding bias and fairness). The paper presents these as objective metrics—'designed to give users some objective metrics for dimensions of trust'—and ties them to concrete tools: prompt engineering for explainability and visibility, benchmark tests for reliability, and bias-audit toolkits for ethics. It further argues that hallucinations, bias, magical thinking, reification, and prompt deficiencies are the drivers that erode these metrics, and that the cost of ignoring them can be measured in lives and public trust, as the Reinhart-Rogoff, Test and Trace, and Post Office Horizon cases show.
Load-bearing premise
The framework's value depends on the claim that its four dimensions can be turned into objective, measurable metrics; the paper names this goal but does not define the scales, thresholds, or validation procedure that would make it real.
Editorial extensions
If this is right
- Users could audit an AI-generated formula against four named dimensions instead of relying on the AI's confident tone.
- Prompt engineering gains a clear purpose: eliciting explainability and visibility statements that can be checked.
- Benchmark suites for spreadsheet formulas, organised around hallucination triggers like negation and inference, could give reliability a quantitative score.
- Bias audits and fairness documentation could become routine parts of AI-supported spreadsheet workflows.
- The same dimensions could be extended to a quantitative risk or trustworthiness score, as the paper's future-research section notes.
Reading between the lines
- The framework's real test is whether the four dimensions can be operationalised as a rubric with defined scales; nothing in the paper yet provides thresholds or a scoring procedure.
- A natural experiment would be to have spreadsheet experts rate a set of correct and hallucinated formulas using the four dimensions and check whether the ratings separate the two groups.
- If the dimensions generalise, the same transparency and dependability split could apply to AI-generated code in other domains, not just spreadsheet formulas.
- The mistrust cases suggest a caution: even a well-intentioned framework will fail if organisations treat a high trust score as a substitute for verifying the final artefact.
Signed reviews
Editorial analysis
A structured set of objections, weighed in public.
Referee Report
Summary. This paper proposes a conceptual framework for evaluating trust in generative AI and LLM-produced spreadsheet formulas. The framework has two pillars, transparency (comprising explainability and visibility) and dependability (comprising reliability and ethical considerations), with an additional section on user-centric design. The paper also discusses sources of error such as hallucinations, bias, magical thinking, and poor prompt engineering, and it uses historical spreadsheet failures (Reinhart-Rogoff, UK Test and Trace, Post Office Horizon) to illustrate the consequences of misplaced trust. No empirical evaluation is reported; the contribution is a taxonomy of trust dimensions and a set of suggestions for future validation.
Significance. If fully operationalized, the framework could give spreadsheet users and auditors a structured vocabulary for assessing LLM-generated formulas, moving beyond the intuitive sense that an output may or may not be reliable. The paper draws sensibly on established trust-in-automation literature (Muir and Moray, Lee and See) and links to concrete mechanisms such as prompt engineering, benchmark testing, and bias-audit toolkits (IBM AI Fairness 360). Its honest admission in Section 2.2 that the proposals are 'simply a starting point' is a strength, but it also exposes that the central claim of delivering 'objective metrics' is, as yet, unrealized. The framework is best read as a research agenda rather than a measurement instrument, and the manuscript should be revised to make that status explicit.
major comments (2)
- [Section 2.1 and 2.1.1] The central claim that the proposed metrics 'are designed to give users some objective metrics for dimensions of trust tailored to generative AI formula production' is not supported by the manuscript. Four dimensions are named, but no scales, thresholds, rubrics, or validation procedures are defined for any of them. More seriously, Section 2.1.1 concedes that, for the visibility dimension, 'some of this information is "unknowable" due to deep learning neural networks being "black boxes"'. If the underlying algorithm is unknowable, the transparency pillar cannot be scored as an objective metric for real models, and the framework cannot be applied as stated. The paper should either revise the claim to present the dimensions as a qualitative checklist for discussion, or provide an operationalization plan (for example, model cards, API disclosures, benchmark scores for explainability, user surveys, or external bias audit results) and explicitly acknowledge the partial measurability of each dimension.
- [Section 2.2 and Section 3.1] The paper's own conclusion in Section 2.2 states that the proposals are 'simply a starting point', and Section 3.1 answers the research questions by restating the framework rather than providing evidence that the dimensions can be measured or that they correspond to user trust. This creates a tension with the earlier objective-metrics claim: the manuscript offers a taxonomy but not a validated instrument. For a revision, the authors should either temper the abstract and Section 2.1 language to say that the framework is a step toward objective evaluation, or add a concrete measurement proposal (including how each dimension would be scored by a user or auditor) and a small demonstration on one or two example formulas. Without such a change, the reader cannot distinguish the framework from a loose vocabulary of trust-related terms.
minor comments (4)
- [Section 2.1.3] The framework is introduced as having two pillars (transparency and dependability), but Section 2.1.3 adds user-centric design as a third component. The abstract and the opening of Section 2.1 should be updated to acknowledge this third area, or the user-feedback mechanisms should be presented as a cross-cutting feature rather than a separate pillar.
- [Section 2.3.1] The sentence 'Hallucinations seem to be triggered by certain conditions present in the prompt' is followed by a list of prompt characteristics (uncertainty, deduction, negation, mathematical operations) with citations. The word 'seem' is appropriate, but the strength and consistency of the evidence for each characteristic varies across the cited sources; the paper would benefit from a sentence indicating that some of these findings are initial or contested.
- [References] Several references contain typographical or dating errors that should be corrected in a revision: 'Lee and See 2024' likely refers to the 2004 article in Human Factors; 'Haung' should be 'Huang'; 'Britsh Medicial Journal Open' should be 'British Medical Journal Open'; 'Regularing' should be 'Regulating'; and 'Rodreguez' should be 'Rodriguez'. A careful proofread of the reference list is needed.
- [Section 3.1] The research questions are posed in Section 1.0 in the order (1) differences, (2) sources of error, (3) adaptation of trust dimensions, but Section 3.1 answers them in the order (1), (3), (2). The conclusion should follow the original numbering or explicitly renumber the questions for consistency.
Circularity Check
No circularity: the paper is a conceptual trust framework with no fitted parameters, derived predictions, or load-bearing self-citations.
full rationale
The paper proposes a Transparency and Trustworthiness Framework for generative AI spreadsheet formulas, organized around transparency (explainability, visibility) and dependability (reliability, ethics). It does not derive equations, fit parameters, or make empirical predictions from its own assumptions. The central claim that the metrics are ‘designed to give users some objective metrics’ is asserted rather than demonstrated, and the paper itself concedes in Section 2.2 that the proposals are ‘simply a starting point’; this is a feasibility or completeness limitation, not circularity. The only self-citation (Thorne 2023) is offered as a suggested benchmark starting point alongside O'Beirne (2023), and no framework dimension or conclusion depends on it. The conceptual inspiration is external (Muir and Moray, Lee and See, Bellamy et al., etc.), and no result is reduced to an input by construction. Therefore no specific circular step can be identified under the required evidentiary standard.
Assumptions & free parameters
assumptions (5)
- domain assumption Trust in automation models can be adapted to generative AI for spreadsheet formulas.
- domain assumption LLMs hallucinate under conditions of uncertainty, deduction, negation, and some mathematical operations.
- domain assumption LLM training data contains biases that can propagate into outputs.
- ad hoc to paper Spreadsheet errors from past incidents are relevant analogies for generative AI mistrust.
- domain assumption Prompt engineering can mitigate hallucinations and improve output quality.
Cite this review
Pith. "Pith review of Understanding and Evaluating Trust in Generative AI and Large Language Models for Spreadsheets." pith.science (2026). https://pith.science/paper/UL4PAO5N
@misc{pith2026241214062,
author = {Pith},
title = {Pith review of: Understanding and Evaluating Trust in Generative AI and Large Language Models for Spreadsheets},
year = {2026},
howpublished = {\url{https://pith.science/paper/UL4PAO5N}},
note = {Machine review of arXiv:2412.14062}
}
read the original abstract
Generative AI and Large Language Models (LLMs) hold promise for automating spreadsheet formula creation. However, due to hallucinations, bias and variable user skill, outputs obtained from generative AI cannot be assumed to be accurate or trustworthy. To address these challenges, a trustworthiness framework is proposed based on evaluating the transparency and dependability of the formula. The transparency of the formula is explored through explainability (understanding the formula's reasoning) and visibility (inspecting the underlying algorithms). The dependability of the generated formula is evaluated in terms of reliability (consistency and accuracy) and ethical considerations (bias and fairness). The paper also examines the drivers to these metrics in the form of hallucinations, training data bias and poorly constructed prompts. Finally, examples of mistrust in technology are considered and the consequences explored.
Reference graph
Works this paper leans on
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[2020]
The Reification of an Incorrect and Inappropriate Spreadsheet Model
Language Models are Few-Shot Learners. Arxiv. Chen, Yuyan, Qiang Fu, Yichen Yuan, Zhihao Wen, Ge Fan, Dayiheng Liu, Dongmei Zhang, Zhixu Li, and Yanghua Xia. 2023. “Hallucination Detection: Robustly Discerning Reliable Answers in Large Language Models.” In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management (CIKM '...
work page Pith review arXiv 2023
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[2023]
End User Computing: The Dark Matter (and Dark Energy) of Corporate IT
London. https://eusprig.org/wp-content/uploads/2309.00120.pdf. Panko, R. 2013. “End User Computing: The Dark Matter (and Dark Energy) of Corporate IT.” Journal of Organisational and End User Computing 25 (3). doi:DOI: 10.4018/joeuc.2013070101. Plevris, V, G Papazafeiropoulos, and A. Jiménez Rios. 2023. “Chatbots Put to the Test in Math and Logic Problems:...
arXiv 2013
Reviewed August 11, 2026 · model on record in the stance chip above.
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