{"id":"c58e409c-befc-4177-8d5b-23800fe317aa","arxiv_id":"2412.14062","paper_version":1,"verdict":"CONDITIONAL","confidence":"MODERATE","novelty_score":5.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":0,"one_line_summary":"A framework for evaluating trust in AI-generated spreadsheet formulas, built from transparency and dependability dimensions and an analysis of error sources.","lead":"This paper proposes a checklist for deciding whether spreadsheet formulas written by AI chatbots can be trusted, grouping checks into transparency and dependability. It reviews the causes of AI errors and uses past spreadsheet disasters to argue that trust needs to be formally evaluated.","discovery_kind":"new_application","skeptic_critique":{"model":"deepseek-v4-flash","headline":"The paper's own §2.1.1 admits LLM internals are largely 'unknowable', so the visibility half of the transparency pillar cannot yield objective metrics, undermining the central claim.","rationale":"The reader's weakest assumption is correct but under-specified. The specific vulnerability is the visibility dimension, which the paper itself concedes is largely unobtainable. This is more load-bearing than the general measurability worry because it identifies a concrete gap in one of the two pillars of transparency. If visibility cannot be measured, the framework cannot produce the objective transparency metric it promises, and the claim reduces to a vocabulary for discussion. The proposed test would empirically settle whether the framework can be turned into a measurement instrument: if raters cannot agree or if visibility data is simply absent, the framework's central claim fails. This is a proposal paper, so a conditional verdict is appropriate: the framework is useful as a taxonomy but requires operationalization and validation before the objectivity claim is warranted.","tokens_in":10773,"tokens_out":3982,"duration_ms":34151,"concrete_test":"Implement the framework as a scoring rubric for a fixed set of 50 LLM-generated spreadsheet formulas drawn from existing benchmarks (e.g., Thorne 2023; O'Beirne 2023). For each formula, two independent raters score: explainability (are step-by-step justifications present?), visibility (is the model's architecture/training data disclosed and verifiable?), reliability (does the formula pass a battery of test cases?), ethics (does a bias audit flag issues?). Compute inter-rater reliability (Cohen's kappa) and completeness (fraction of formulas with all four scores available). If kappa < 0.6 or visibility scores are unavailable for more than a small fraction of models, the framework does not currently deliver objective metrics.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The central claim (Section 2.1) is that the proposed metrics 'are designed to give users some objective metrics for dimensions of trust tailored to generative AI formula production.' This requires that every dimension be measurable. But Section 2.1.1, under Visibility, states that 'due to the manner in which generative AI is trained, some of this information is \"unknowable\" due to deep learning neural networks being \"black boxes\"'. If a dimension's data is unknowable, the framework cannot populate it for real models, so the transparency pillar cannot be scored. No alternative operationalization (e.g., model cards, API disclosures) is defined, nor are thresholds or scales for explainability, reliability, or ethics. The paper's own conclusion (Section 2.2) calls the proposals 'simply a starting point', which is honest but confirms that the objective metrics are not yet specified. Thus the central claim is a promissory note: the framework exists as a taxonomy, not as a measurement instrument. This is not an external disagreement; it is an internal tension between the claim of objective metrics and the admitted impossibility of complete visibility.","agreement_with_reader":"partial"},"referee_report":{"model":"deepseek-v4-flash","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.","tokens_in":10972,"tokens_out":3937,"duration_ms":35023,"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":[{"comment":"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":"Section 2.1 and 2.1.1"},{"comment":"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.","section":"Section 2.2 and Section 3.1"}],"minor_comments":[{"comment":"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":"Section 2.1.3"},{"comment":"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.","section":"Section 2.3.1"},{"comment":"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":"References"},{"comment":"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.","section":"Section 3.1"}],"recommendation":"major_revision","confidential_remarks":"The paper reads like a workshop or position paper rather than a full journal article. Its main value is as a structured research agenda, and the historical case studies provide a compelling motivation. However, the 'objective metrics' language in the abstract and Section 2.1 overstates what is actually delivered, and the self-identified limitation in Section 2.2 should be reflected in the framing. I see no sign of problematic citation behavior; the single self-citation is incidental. If the scope of the journal allows clearly-labeled conceptual work, a thoughtful revision addressing the measurability gap could make this a useful contribution."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"The useful core of this paper is a simple taxonomy: trust in LLM-generated spreadsheet formulas can be discussed along four dimensions—explainability, visibility, reliability, and ethical considerations—and degraded by hallucinations, bias, and weak prompts. That framing is sensible, clearly written, and honestly grounded in the older trust-in-automation literature (Muir, Lee and See). It is a genuinely useful way to organize thinking and talk about a real problem, not a fake result. The paper also deserves credit for not overclaiming in its conclusion: it says the proposals are \"simply a starting point.\"\n\nThe soft spot is the gap between that honest conclusion and the earlier claim, in Section 2.1, that these metrics \"are designed to give users some objective metrics.\" The stress-test note lands: Section 2.1.1 admits the visibility half of transparency is partly \"unknowable\" because LLMs are black boxes, and the paper never defines scales, thresholds, or a measurement procedure for any of the four dimensions. So the framework is a taxonomy with a promissory note attached, not an operational instrument. That is not fatal for a position paper, but it does mean the central claim, as stated, is not yet supported. The paper would be stronger if it either dropped the word \"objective\" or sketched how a metric for each dimension could actually be populated, even approximately.\n\nThe three long historical examples (Reinhart-Rogoff, Test & Trace, Horizon) are vivid but out of proportion to the rest of the paper; they illustrate the cost of misplaced trust in spreadsheets generally, but they do not validate the proposed framework. They could be cut or shortened without loss.\n\nCitation pattern looks fine: the self-citation (Thorne 2023) points to a benchmark source and is not load-bearing, and the references to hallucination triggers and bias are appropriate and current.\n\nWho is this for? Practitioners and researchers in spreadsheet risk and AI-assisted data work who want a vocabulary for talking about trust before the measurement tools exist. It is worth sending to peer review, but with the expectation of revision: specify what is taxonomy and what is measurement, or operationalize at least one dimension.\n\nFor the record, I agree with the reader's conditional verdict and think the stress-test concern is real, though the paper survives it as a conceptual contribution.","headline":"A clear, honest taxonomy for thinking about trust in LLM-generated spreadsheet formulas, but the 'objective metrics' claim outruns what the paper actually specifies.","tokens_in":11463,"tokens_out":1227,"would_cite":true,"duration_ms":12537,"reading_group":"maybe","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"deepseek-v4-flash","headline":"This paper proposes a transparency-and-dependability framework for judging whether to trust AI-generated spreadsheet formulas.","keywords":["generative AI","large language models","spreadsheet formulas","trust","transparency","dependability","hallucination","prompt engineering"],"falsifier":"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.","tokens_in":10555,"feed_emoji":"📊","tokens_out":4277,"duration_ms":33636,"temperature":0.7,"pith_summary":"This paper argues that trust in AI-generated spreadsheet formulas should not be left to the user's gut reaction to the AI's confident tone. It proposes a 'Transparency and Trustworthiness Framework' with four dimensions: explainability, visibility, reliability, and ethical considerations. Each dimension is meant to supply objective metrics for judging whether a formula deserves trust. The paper also identifies the main threats to that trust—hallucinations, training-data bias, magical thinking, and poor prompt engineering—and illustrates the cost of misplaced trust with spreadsheet-linked failures such as Reinhart and Rogoff's debt analysis and the UK Test and Trace data loss. If the framework works, spreadsheet users would have a structured audit path for AI output rather than an all-or-nothing leap of faith.","feed_headline":"Four metrics decide if an AI spreadsheet formula is trustworthy","feed_subtitle":"A proposed framework rates transparency and dependability so users can audit AI output instead of trusting its tone.","key_machinery":"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.","core_discovery":"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.","pith_inferences":["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."],"forward_implications":["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."],"supporting_citations":[{"why":"Provides the foundational experimental model of trust in automation that the paper adapts to generative AI.","marker":"Muir and Moray 1996"},{"why":"Supplies the trust-in-automation design principles for appropriate reliance that inform the framework's dimensions.","marker":"Lee and See 2024"},{"why":"Offers the narrative review of trust measurement models and metrics that the paper draws on for its dimension set.","marker":"Kohn, et al. 2021"},{"why":"Contributes the trust-but-verify approach and spreadsheet-specific benchmark ideas for evaluating AI output.","marker":"O'Beirne 2023"},{"why":"Supplies evidence on hallucination detection in LLMs, grounding the paper's claim that hallucinations are a central threat.","marker":"Chen, et al. 2023"},{"why":"Provides the AI Fairness 360 toolkit referenced as a concrete tool for detecting and mitigating algorithmic bias.","marker":"Bellamy, et al. 2019"},{"why":"Supports the claim that causal explainability approaches can mitigate the black-box problem in visibility.","marker":"Bhattacharjee, et al. 2023"}],"fun_headline_variants":["AI spreadsheet formulas get a four-part trust test","Framework measures trust in AI-generated formulas","Four metrics to audit AI spreadsheet formulas","Trust AI formulas? Check transparency and dependability","New trust framework for AI spreadsheet formulas"],"cache_read_input_tokens":3200,"weakest_assumption_plain":"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.","fun_headline_variants_meta":{"raw":{"variants":["AI spreadsheet formulas get a four-part trust test","Framework measures trust in AI-generated formulas","Four metrics to audit AI spreadsheet formulas","Trust AI formulas? Check transparency and dependability","New trust framework for AI spreadsheet formulas"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.000539,"raw_usage":{"total_tokens":2542,"prompt_tokens":855,"completion_tokens":1687,"prompt_tokens_details":{"cached_tokens":384},"prompt_cache_hit_tokens":384,"prompt_cache_miss_tokens":471,"completion_tokens_details":{"reasoning_tokens":1622}},"tokens_in":471,"tokens_out":1687,"duration_ms":10684,"temperature":1.0,"reasoning_tokens":1622,"cache_read_input_tokens":384,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-11T12:30:33.273843+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"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.","supporting_citations":[],"review_version":1}