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A User Study Evaluating Argumentative Explanations in Diagnostic Decision Support

T0 review · 2 major / 7 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read Attribution-based explanations beat counterfactual and exclusion arguments in physician ratings, yet doctors would not use any AI-generated explanation to explain a case to a colleague.

desk verdict Honest small pilot study with a useful qualitative insight about uncertainty, but the 'significantly better' conclusion is not backed by any inferential test. read the letter →

arxiv 2505.10188 v1 pith:Q474RHYM submitted 2025-05-15 cs.AI

Felix Liedeker , Olivia Sanchez-Graillet , Moana Seidler , Christian Brandt , Jörg Wellmer , Philipp Cimiano This is my paper · ORCID
classification cs.AI
keywords explainableAIargumentativeexplanationsdiagnosticdecisionsupportuserstudycounterfactualfeatureattributiontransientlossofconsciousnessBayesiannetwork
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper reports a user study in which eight neurologists rated three kinds of AI-generated explanations produced for a diagnostic-support system that classifies transient loss of consciousness. The central claim is that attribution-based explanations, which state that a feature supports a given diagnosis, were rated significantly higher in plausibility and comprehensibility than counterfactual or exclusion-based explanations. The paper also claims that, despite moderate-to-good scores on those dimensions, the doctors would not use any of the AI explanations to explain a case to a colleague, because the explanations gave no information about prediction quality, reliability, or uncertainty. If this is right, explanation type is a real lever on clinician acceptance, but no verbalization format can replace explicit uncertainty signaling.

What carries the argument

The central object is a set of three natural-language argument templates: attribution ('Because feature is A, the patient most likely has diagnosis X'), counterfactual ('If feature were A, the diagnosis would most likely change to Y'), and exclusion ('Because feature is A, diagnosis Y can most likely be ruled out'). These templates are instantiated with feature-value pairs coming from LIME and from a counterfactual generator, using a Bayesian network (a causal probabilistic model with layers of risk factors, diseases, and symptoms) trained on 300 patient records as the underlying predictor. The templates are what allow the comparison of XAI methods to proceed over explicit arguments instead of raw feature lists.

What would settle it

Run a follow-up study in which each case is paired with several independently generated instantiations of each explanation template. If attribution-based arguments do not consistently outrank counterfactual and exclusion arguments once the concrete features and wording are varied, the reported advantage is an artifact of the specific instantiations rather than of the argument type.

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Extended reading notes

Core claim

On the paper's own terms, the discovery is that when the outputs of standard XAI methods are turned into natural-language arguments, physicians evaluating real TLOC cases rate the simple attribution argument as the most plausible and comprehensible, significantly above counterfactual and exclusion-based arguments, while ratings for completeness and 'would I use this with a colleague' are low for all three. The follow-up interviews identify the missing information about the quality and reliability of the prediction as the main cause of the doctors' reluctance, together with the lack of case-specific detail in the templated explanations.

Load-bearing premise

The comparison assumes that each doctor's score reflects the explanation type, not the particular features and wording chosen for that type in each specific case; if the instantiations differed in quality for reasons unrelated to the method, the ranking could be an artifact of the examples.

Editorial extensions

If this is right

  • Explanation format is not presentationally neutral: converting the same model prediction into attribution, counterfactual, or exclusion wording changes how clinicians judge the explanation.
  • For this diagnostic task, simpler feature-attribution arguments appear to be a safer default than counterfactual or exclusion arguments, at least when judged on plausibility and comprehensibility.
  • A diagnostic-support explanation that omits prediction confidence, reliability, and uncertainty will be rejected for inter-physician communication regardless of which XAI method generated it.
  • Tailoring the explanation to the complexity of the case and the experience of the clinician, rather than generating one template for every case, is a necessary next step for acceptance.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • If the specific features selected for the counterfactual and exclusion templates in the chosen ten cases happened to be less clinically salient than those used for attribution templates, the reported ranking could partly reflect content rather than argument type; a re-run with multiple independent instantiations per case would separate the two.
  • The doctors' own explanations frequently combined a prototype statement with ruled-out alternatives, which suggests a 'typical case, and here is what it is not' template might match clinical communication more closely than any single current template.
  • A direct design fix suggested by the interview data is to append a confidence or reliability statement to each explanation; the study predicts this would raise the 'use with a colleague' rating.
  • The results favor evaluating XAI by use-oriented acceptance tests, such as whether a clinician would relay the explanation to a peer, rather than by perceived quality ratings alone.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

2 major / 7 minor

Summary. The paper presents a user study with eight physicians evaluating three template-based natural-language explanations for a diagnostic decision-support system in transient loss of consciousness (TLOC). The three explanation types are attribution-based (derived from LIME), counterfactual, and exclusion-based. Participants rated the explanations on comprehensibility, plausibility, completeness, and willingness to use them when explaining a case to a colleague, and later took part in semi-structured follow-up interviews. The paper reports that attribution-based explanations received the best ratings on plausibility and comprehensibility, that willingness to use any AI-generated explanation was low, and that interview participants attributed this reluctance to missing information about prediction quality, reliability, uncertainty, and to the lack of case-specific tailoring.

Significance. The study addresses a relevant and under-explored question in XAI and medical decision support: how clinicians perceive different argumentative explanation types. Its strengths include the use of real patient cases, domain-expert participants, a concrete diagnostic task, and the combination of quantitative ratings with qualitative interview data. The descriptive findings and the interview quotes about uncertainty and reliability signaling are valuable for designers of explanation interfaces. However, the central comparative claim is not supported by the statistical analysis as reported, and the explanation-type comparison may be confounded by the specific feature instantiations used for each case. If the authors can provide appropriate inferential statistics and address the confound, the contribution would be a useful empirical data point for argumentative XAI; in its current form the main conclusion overstates what the data show.

major comments (2)
  1. [Section 6, with evidence in Section 5 (Figures 2-5)] The sentence "attribution-based arguments were evaluated significantly better than counterfactual or exclusion-based arguments" is not supported by any inferential statistical analysis in the manuscript. Section 5 reports only visual boxplot comparisons and descriptive statements; no test statistic, p-value, confidence interval, or effect size is provided, and no repeated-measures model is fitted. With eight raters and repeated measurements on ten cases, apparent median differences can easily be driven by a few participants or by case-level variation. Please add an appropriate analysis (e.g., Friedman test with post-hoc comparisons, or a mixed-effects model with by-participant and by-case random effects) or remove the word "significantly" and frame the finding as a descriptive trend.
  2. [Sections 4.3 and 4.4] The comparison across explanation types is potentially confounded by the specific instantiation of each template. For each case, participants saw one realization of each template filled with particular LIME or counterfactual features, and the attribution template even permits replacing the top LIME features with other, "less important" features when the top features are not among the expert-defined relevant variables. Ratings may therefore reflect the suitability, wording, or case-fit of the particular features rather than the argument type itself. In addition, the number of feature mentions differs (two in the attribution template versus four in the counterfactual and exclusion templates) and the latter two include alternative diagnoses. Please provide evidence that the instantiations are matched on relevant properties, or soften the cross-type claims and explicitly discuss this confound as a limitation.
minor comments (7)
  1. [Section 5, paragraph after Figure 2] The sentence "For both CFs and attribution-based explanations, the median value is significantly lower" appears to contain a typo: the context suggests it should refer to "CFs and exclusion-based explanations"; "significantly" should also be removed unless a statistical test is reported.
  2. [Section 4.3] The feature selection for exclusion-based explanations is not described: the text explains how LIME features are chosen for attribution-based explanations and how counterfactuals are generated, but it does not state how the features in the exclusion-principle template are selected.
  3. [Section 4.4 and Appendix] The manuscript does not include a statement on ethics approval or informed consent for the user study with physicians; please add this information, as is standard for empirical studies with human participants.
  4. [Figures 2 and 3] Several boxplots have medians that coincide with quartiles, making the plots hard to read; adding jittered individual data points would improve interpretability.
  5. [References] References [25] and [26] appear to be the same paper (Krause, Ambler, Elvang-Gøransson, and Fox, "A logic of argumentation for reasoning under uncertainty", Computational Intelligence 11:113-131, 1995) and should be consolidated.
  6. [Table 2] The term "prototype" in the explanation patterns is not defined in the text; please explain what constitutes a prototype-based explanation in this context.
  7. [Section 6] The conclusion states that "all explanations score reasonably well along the first three dimensions," but Section 5 reports that completeness is perceived as "rather low, or moderate in the case of attribution-based arguments"; please align this wording with the reported results.

Circularity Check

0 steps flagged · score 2.0 of 10

No significant circularity; conclusions rest on external user ratings and interviews. One minor non-load-bearing self-citation appears in the template design justification.

full rationale

The paper's chain is an empirical user study: XAI outputs (LIME and counterfactuals) are verbalized through templates, physicians rate the explanations, and follow-up interviews explain the ratings. There is no derivation from model assumptions to conclusions that reduces to the inputs. The central comparative claim and the uncertainty/reliability finding are grounded in external questionnaire and interview data, not in the authors' prior work or in the definitions of the templates. The only self-citation is to [29] (Liedeker et al.) supporting the two-feature template design; however, it is paired with an independent external citation [22], and the design choice is not the paper's central claim, so this self-citation is not load-bearing. One non-circularity concern: Section 6's wording 'significantly better' is not supported by any reported inferential test (no p-values, effect sizes, confidence intervals, or repeated-measures model) in Section 5; this is a statistical overstatement or correctness risk, not circularity. The paper itself acknowledges the small sample in Section 6, which is a limitation rather than a circular step.

Assumptions & free parameters 3 free parameters · 4 assumptions · 0 invented entities

The central claim rests on the Bayesian network and feature set inherited from Wardrope et al., on manually designed templates, and on the assumption that eight experts are representative. No new physical or conceptual entities are introduced. The Bayesian network parameters and the two-feature template are the main fitted or hand-chosen numbers.

free parameters (3)
  • Bayesian network conditional probabilities = not reported
    Learned from 300 Wardrope et al. cases; they determine the diagnoses, LIME weights, and counterfactuals that underlie all rated explanations (Section 4.1).
  • Number of features per explanation = 2
    Fixed by the authors to keep explanations simple, citing Keane and Smyth [22] and Liedeker et al. [29]; this choice shapes every template and the comparison (Section 4.3).
  • LIME feature replacement rule = not formalized
    If the top LIME features are not in the pre-defined relevant variables for a diagnosis, they are manually replaced with less important features; this rule is not algorithmic (Section 4.3).
assumptions (4)
  • domain assumption The 36 variables from Wardrope et al. are the relevant clinical features for TLOC differential diagnosis.
    The Bayesian network structure and every generated explanation depend on this externally selected feature set without independent clinical validation in this paper (Section 4.1).
  • domain assumption The ground-truth diagnoses annotated in the 32 outpatient letters are correct.
    These labels are used to build and evaluate cases and to measure expert agreement (Section 4.1, Section 5).
  • domain assumption Two-feature explanations are a reasonable representation of an explanation for clinicians.
    The template design fixes two features per explanation and justifies this by prior literature, but it is not derived in this paper (Section 4.3).
  • ad hoc to paper Ratings from eight physicians are representative of the target user population.
    The authors state that despite the small population they regard the answers as representative; this assumption is necessary to generalize the findings beyond the sample (Section 6, limitations).

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Cite this review

Pith. "Pith review of A User Study Evaluating Argumentative Explanations in Diagnostic Decision Support." pith.science (2026). https://pith.science/paper/Q474RHYM

@misc{pith2026250510188,
  author       = {Pith},
  title        = {Pith review of: A User Study Evaluating Argumentative Explanations in Diagnostic Decision Support},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/Q474RHYM}},
  note         = {Machine review of arXiv:2505.10188}
}
read the original abstract

As the field of healthcare increasingly adopts artificial intelligence, it becomes important to understand which types of explanations increase transparency and empower users to develop confidence and trust in the predictions made by machine learning (ML) systems. In shared decision-making scenarios where doctors cooperate with ML systems to reach an appropriate decision, establishing mutual trust is crucial. In this paper, we explore different approaches to generating explanations in eXplainable AI (XAI) and make their underlying arguments explicit so that they can be evaluated by medical experts. In particular, we present the findings of a user study conducted with physicians to investigate their perceptions of various types of AI-generated explanations in the context of diagnostic decision support. The study aims to identify the most effective and useful explanations that enhance the diagnostic process. In the study, medical doctors filled out a survey to assess different types of explanations. Further, an interview was carried out post-survey to gain qualitative insights on the requirements of explanations incorporated in diagnostic decision support. Overall, the insights gained from this study contribute to understanding the types of explanations that are most effective.

Figures

Figures reproduced from arXiv: 2505.10188 by the authors.

Figure 1
Figure 1. Three-layer Bayesian Network with the 36 most relevant variables determined by Wardrope et al. [53]. 4.2 XAI Methods The basis for generating our explanations are two XAI standard methods: LIME [41] and CFs. LIME identifies the features that have the most significant impact on the model’s output, thus highlighting the most important features for that particular prediction. The most [PITH_FULL_IMAGE:figures/full_fig… view at source ↗
Figure 2
Figure 2. User ratings on the comprehensibility of the three generated argument types. In the following, results are visualized with boxplots and it must be noted, that in some cases (e.g. attribution and counterfactual argu￾ments in [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. User ratings on the plausibility of the three generated argument types. The completeness of all argument types is perceived as rather low, or moderate in the case of attribution-based arguments as shown in [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figures from the paper (1 more)
Figure 6
Figure 6. Figure 6: Ratings for the statement “I would use a similar explanation to explain the facts to a colleague” for our three argument types. To address RQ4, which is the question of what types of explana￾tions experts would use and how they compare to XAI-generated ex￾planations, w…

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Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.