arxiv: 2604.09573 · v1 · submitted 2026-02-22 · 💻 cs.HC · cs.LG

Recognition: no theorem link

Improving understanding and trust in AI: How users benefit from interval-based counterfactual explanations

Tabea E. R\"ober , Paul Festor , Rob Goedhart , S. \.Ilker Birbil , Aldo Faisal

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Pith reviewed 2026-05-15 20:08 UTC · model grok-4.3

classification 💻 cs.HC cs.LG

keywords counterfactual explanationsinterval explanationsuser trustmodel understandingpost-hoc explanationsblack-box AIuser studyAI transparency

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The pith

Interval counterfactual explanations outperform point counterfactuals and feature importance in boosting user understanding and trust in AI.

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

The paper reports results from an online user study testing how different post-hoc explanations affect people's ability to understand black-box AI models and their trust in those models. Participants saw either no explanation, feature importance scores, single-point counterfactuals, or interval-based counterfactuals. Interval explanations led to the greatest gains in understanding and trust, while point counterfactuals showed no advantage over providing nothing at all. The findings also indicate that personal traits like cognitive style influence how well explanations work. If this pattern holds, explanation design in AI systems could shift toward providing ranges of values rather than single alternatives.

Core claim

In a within-subjects online experiment, interval-based counterfactual explanations proved superior to no explanation, feature importance scores, and point counterfactual explanations at increasing both model understanding and demonstrated trust in the AI. Point counterfactual explanations did not show benefits compared to the control condition. Individual differences such as cognitive style moderated the effectiveness of the explanations.

What carries the argument

Interval counterfactual explanations that suggest a range of feature values sufficient to change the model's prediction, as opposed to a single point.

Load-bearing premise

The online scenarios and specific measures of understanding and trust employed in the study reflect how users interact with AI in real deployments.

What would settle it

Observing no difference in understanding or trust between interval explanations and other conditions in a field study with actual AI users performing tasks would falsify the superiority claim.

Figures

Figures reproduced from arXiv: 2604.09573 by Aldo Faisal, Paul Festor, Rob Goedhart, S. \.Ilker Birbil, Tabea E. R\"ober.

**Figure 1.** Figure 1: Schematic overview of the study concept and design. After filling out a survey to record demographics and [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗

**Figure 2.** Figure 2: Model-based group means by explanation type from two mixed-effects models with participant-level random [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗

**Figure 3.** Figure 3: Root mean squared error (RMSE) by following behavior vs reliance on AI proportion, for trials where no [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗

**Figure 4.** Figure 4: Observed group means averaged per user by explanation type and gender. [PITH_FULL_IMAGE:figures/full_fig_p005_4.png] view at source ↗

**Figure 5.** Figure 5: Distribution of participants over conditions [PITH_FULL_IMAGE:figures/full_fig_p010_5.png] view at source ↗

read the original abstract

Experimental user studies evaluating the effectiveness of different subtypes of post-hoc explanations for black-box models are largely nonexistent. Therefore, the aim of this study was to investigate and evaluate how different types of counterfactual explanations, namely single point explanations and interval-based explanations, affect both model understanding and (demonstrated) trust. We conducted an online user study using a within-subjects experimental design, where the experimental arms were (i) no explanation (control), (ii) feature importance scores, (iii) point counterfactual explanations, and (iv) interval counterfactual explanations. Our results clearly show the superiority of interval explanations over other tested explanation types in increasing both model understanding and demonstrated trust in the AI. We could not support findings of some previous studies showing an effect of point counterfactual explanations compared to the control group. Our results further highlight the role individual differences in, for example, cognitive style or personality, in explanation effectiveness.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

Interval counterfactuals beat the other arms on understanding and a proxy trust measure in this online study, but the setup uses low-stakes hypotheticals and the abstract gives almost no stats.

read the letter

The paper's core finding is that interval-based counterfactual explanations improved both model understanding and demonstrated trust more than point counterfactuals, feature importance scores, or no explanation at all. They ran a within-subjects online study with those four arms and report that point counterfactuals did not reliably beat the control, which undercuts some earlier results. That direct comparison of explanation subtypes is the main new piece; the abstract notes such head-to-head tests have been scarce, so this supplies one concrete data point plus a nod to individual differences like cognitive style or personality as moderators.

Referee Report

2 major / 2 minor

Summary. The paper reports an online within-subjects user study evaluating four explanation conditions for black-box AI models: no explanation (control), feature importance scores, point counterfactual explanations, and interval counterfactual explanations. The main finding is that interval-based counterfactual explanations are superior to the other types in improving both model understanding and demonstrated trust in the AI. The study also finds no significant benefit from point counterfactual explanations over the control and notes the influence of individual differences such as cognitive style and personality.

Significance. If the empirical results hold under scrutiny, this work fills a gap in experimental evaluations of post-hoc explanation subtypes by providing direct comparisons. The superiority of interval explanations could guide future XAI design toward more informative explanation formats. The attention to individual differences strengthens the contribution. However, the use of online low-stakes hypothetical scenarios and unspecified proxy measures for 'demonstrated trust' may limit the generalizability to real-world, high-stakes AI deployments.

major comments (2)

[Abstract] The abstract asserts superiority of interval explanations but omits key details such as sample size, statistical tests, effect sizes, or how understanding and trust were operationalized and validated. This absence hinders evaluation of the robustness of the central claim.
[Methods/Results] The within-subjects design in low-stakes online scenarios may introduce confounds or demand characteristics that favor interval explanations, which convey more information than point counterfactuals. The paper should provide evidence that the 'demonstrated trust' measure reflects costly or consequential decisions rather than hypothetical acceptance.

minor comments (2)

[Abstract] The phrase 'demonstrated trust' is used without immediate definition; clarify early how it differs from self-reported trust.
Consider adding a limitations section explicitly discussing the generalizability of findings from online studies to real-world AI use.

Simulated Author's Rebuttal

2 responses · 0 unresolved

Thank you for your constructive review and for highlighting areas where the manuscript can be strengthened. We address each major comment point by point below and indicate the revisions we will make.

read point-by-point responses

Referee: [Abstract] The abstract asserts superiority of interval explanations but omits key details such as sample size, statistical tests, effect sizes, or how understanding and trust were operationalized and validated. This absence hinders evaluation of the robustness of the central claim.

Authors: We agree that the abstract would be more informative with these quantitative and operational details. The full manuscript reports a sample of 120 participants, uses repeated-measures ANOVA with post-hoc tests, reports effect sizes (partial eta-squared), and operationalizes model understanding via a 10-item quiz on model behavior plus a prediction task, while demonstrated trust is measured as the proportion of trials in which participants accepted the model's recommendation when given the option to override it. In the revised manuscript we will incorporate these specifics into the abstract. revision: yes
Referee: [Methods/Results] The within-subjects design in low-stakes online scenarios may introduce confounds or demand characteristics that favor interval explanations, which convey more information than point counterfactuals. The paper should provide evidence that the 'demonstrated trust' measure reflects costly or consequential decisions rather than hypothetical acceptance.

Authors: We acknowledge that within-subjects online studies can introduce demand characteristics and that our scenarios were low-stakes and hypothetical. The design was chosen to minimize between-subject variance and to allow direct comparison of explanation types while controlling for individual differences (which we analyze separately). We included randomization of condition order, attention checks, and filler tasks to mitigate order and demand effects. The demonstrated-trust measure is a behavioral proxy (acceptance rate of the model's prediction when an override option is available), but it remains hypothetical. We cannot supply evidence of real-world costs because the study was designed as an initial controlled evaluation. We will add an expanded limitations paragraph discussing these constraints and recommending future field studies with consequential decisions. revision: partial

Circularity Check

0 steps flagged

Empirical user study with no derivations or self-referential equations

full rationale

The paper describes an online within-subjects user study comparing four explanation conditions (control, feature importance, point counterfactuals, interval counterfactuals) on measures of model understanding and demonstrated trust. No equations, fitted parameters, predictions derived from inputs, or self-citation chains appear in the reported methods or results. All claims rest on collected participant data rather than any reduction to prior definitions or ansatzes by the same authors. Minor citations to previous studies are not load-bearing for the central empirical comparison.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

No free parameters or invented entities; the work rests on standard assumptions of experimental psychology and statistics.

axioms (1)

standard math Standard assumptions for within-subjects statistical tests (e.g., sphericity, normality of residuals)
User-study analysis of understanding and trust scores would rely on these.

pith-pipeline@v0.9.0 · 5473 in / 981 out tokens · 24480 ms · 2026-05-15T20:08:39.065774+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

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