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REVIEW 3 major objections 5 minor 1 cited by

Exploring the Impact of Explainable AI and Cognitive Capabilities on Users' Decisions

T0 review · 3 major / 5 minor · reviewed 2026-08-16 · deepseek-v4-flash

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Pith's one-line read This paper reports an online experiment showing that high AI confidence significantly increases users' reliance on AI and reduces their cognitive load, while feature-based explanations do not improve accuracy.

desk verdict Well-run empirical study whose headline confidence effects are confounded with instance difficulty; the counterfactual accuracy claim in the abstract is also a post-hoc stretch. read the letter →

arxiv 2505.01192 v1 pith:6A3WIDU3 submitted 2025-05-02 cs.AI cs.HC

Federico Maria Cau , Lucio Davide Spano This is my paper · ORCID
classification cs.AIcs.HC
keywords LoanapprovalpredictionAI-assisteddecisionsExplainableAIRelianceAccuracyNeedforCognitionCognitiveloadCounterfactualexplanations
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 an online experiment with 288 participants who decided whether to approve loan applications with varying levels of AI assistance: no AI, AI prediction/confidence/accuracy alone, or the same AI information plus one of four explanation styles (example-based, feature-based, rule-based, counterfactual). Its central claim is that the confidence score, not the explanation, is the main driver of user behavior: high AI confidence significantly increases reliance on the AI and reduces self-reported cognitive load, while feature-based explanations fail to improve accuracy over other conditions. The paper also claims that counterfactual explanations, although rated as less understandable, can increase overall accuracy and reduce cognitive load when the AI's prediction is correct. A third claim is that Need for Cognition, a personality trait widely used to split users, did not produce significant differences in accuracy, cognitive load, or how people ranked interface elements, suggesting the trait's effects may be task- and context-specific. A sympathetic reader would care because these results bear directly on how to design explainable AI interfaces that neither overburden users nor push them into blind agreement.

What carries the argument

The experimental engine is a mixed-factorial online study built on a Random Forest classifier trained on a public loan-prediction dataset with about 83% test accuracy. AI confidence is defined from test-set quartiles of an entropy-based epistemic uncertainty estimate, with low below 44.3 and high above 61.6, and each participant sees eight main-session instances balanced across AI correctness, confidence, predicted class, and true class. Explanation styles are generated with nearest-neighbor examples, SHAP feature contributions, Anchors rules, and DiCE counterfactuals. The statistical machinery is a set of mixed-effects logistic regressions, generalized estimation equations, and Friedman/Nemenyi ranking tests, with a power analysis used to set the sample size and reduced alpha thresholds.

What would settle it

Run the same six-condition loan task with instances selected so the AI's observed accuracy matches its stated accuracy (about 83%), keeping confidence levels balanced; if high confidence still raises reliance and lowers cognitive load, the confidence effect survives the stated-observed accuracy mismatch. Alternatively, use a two-stage paradigm where users decide first and then see the AI's confidence; if the effect disappears, the result is anchoring rather than confidence-based delegation.

Watch

Extended reading notes

Core claim

On its own terms, the paper establishes that the information accompanying an AI suggestion changes behavior, and that the most consequential piece is the confidence value. Participants shown high-confidence AI predictions agreed with the AI significantly more often than those shown low-confidence predictions (log-odds 1.22), and they reported lower cognitive load (log-odds -0.41). The paper frames this as evidence that users treat confidence as a tiebreaker and that an uncalibrated or overconfident score can lead to over-reliance. It also finds that feature-based explanations, despite their dominance in XAI practice, did not outperform other assistance conditions on accuracy; counterfactual explanations, which users found harder to understand, improved accuracy overall and reduced load when paired with correct AI advice. Finally, the paper reports that low- and high-Need-for-Cognition participants both ranked loan attributes first, explanations second, and AI information last, and did not differ in accuracy or cognitive load, a null result the authors attribute to task complexity.

Load-bearing premise

The load-bearing assumption is that the eight hand-picked instances and the quartile split capture how people respond to confidence itself, rather than how they respond to the unusual gap between the AI's stated 83% accuracy and its observed 62.5% accuracy on those instances.

Editorial extensions

If this is right

  • Interfaces that display confidence scores will systematically nudge users toward the AI's recommendation, so deployments should calibrate confidence estimates to the true likelihood of correctness rather than showing raw model output.
  • Feature-based (Shapley-contribution) explanations, the current default in many AI systems, offer no measurable accuracy advantage over other explanation styles in a complex tabular task.
  • Counterfactual explanations, despite being rated less understandable, can raise overall decision accuracy and lower cognitive load when the AI advice is correct, making them a candidate for hybrid explanation designs.
  • Need for Cognition may not be a reliable personalization axis in complex, high-stakes tasks; adaptive interfaces may need other user characteristics or direct confidence calibration.
  • When the AI displays high confidence, users rank the AI information above the explanation, whereas low confidence blurs that ordering, so the confidence display changes which interface elements people actually use.

Reading between the lines

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

  • A testable extension: measure decision quality under high confidence when the AI is systematically wrong; if users are delegating rather than deliberating, accuracy should collapse exactly on those instances, which would make confidence displays a risk factor rather than a transparency feature.
  • The fixed 83% stated accuracy with 62.5% observed accuracy probably amplified both reliance and distrust; a replication that matches stated and observed accuracy would clarify whether the confidence effect is about the number itself or about resolving uncertainty.
  • The NFC null result suggests median-split personality measurement may be too coarse for complex tasks; traits like epistemic curiosity, or state measures like self-reported confidence, could capture variance this design missed.
  • The authors' one-stage paradigm leaves open that high confidence simply anchors the final answer; a two-stage design separating independent judgment from AI-informed judgment would test whether the cognitive-load reduction is genuine offloading.
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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

3 major / 5 minor

Summary. This paper reports an online user study (N = 288) on AI-assisted loan approval decisions, comparing six AI assistance conditions (No AI, AI with prediction/confidence/accuracy, and the latter plus example-based, feature-based, rule-based, or counterfactual explanations). The authors test hypotheses about AI confidence effects on reliance and cognitive load (H1a, H1b), the accuracy benefit of feature-based explanations (H1c), NFC-based differences in interface-element prioritization (H2), and NFC effects on accuracy and cognitive load (H3). They report that high AI confidence increases reliance and reduces cognitive load, that feature-based explanations do not improve accuracy, that both low and high NFC users rank explanations above AI information, and that no NFC differences in accuracy or cognitive load were found. Post hoc analyses suggest counterfactual explanations increase reliance and reduce cognitive load when AI predictions are correct, and the abstract claims they also enhance overall accuracy.

Significance. If the central confidence effects were causal, the paper would make a useful contribution to human-AI decision-making and XAI personalization. The study has concrete strengths: it uses an a priori power analysis, attention checks, mixed-effects and GEE models, a publicly available dataset, and a stated open pipeline for data processing and statistical analysis. The null results for feature-based explanations and for NFC differences are also informative in a literature with mixed findings. However, the main causal claims about confidence are threatened by a stimulus-level confound, and the counterfactual accuracy claim in the abstract goes beyond what the pre-specified analyses support.

major comments (3)
  1. [3.2.3 and Table 1] H1a and H1b are tested by comparing high- and low-confidence loan applications, but AI confidence is a property of the instance, not a factor manipulated within the same instance. The eight main-session instances were balanced on AI correctness, predicted class, and true class, but not on human-perceived difficulty or prototypicality. If high-confidence instances are also easier for humans, the observed increases in reliance (Log-Odds = 1.22) and decreases in cognitive load (Log-Odds = -0.41) could occur without any causal role for the displayed confidence. The manuscript already contains data for a concrete test: the No AI condition saw the same instances without confidence information, so the authors can compare human accuracy and cognitive load on high- versus low-confidence instances in that condition; if no-AI performance also differs by confidence subgroup, the H1 effects are confounded. This issue is load-bearing for the paper's central claim and should be addressed directly.
  2. [4.2 and 3.2.3] The statistical models include a random intercept for participant but no by-item random effects for the eight main-session loan applications, and the instances were not randomly sampled from the test set. With only one or two instances per confidence-by-correctness combination, the reported H1 effects may be driven by idiosyncratic features of the selected applicants rather than by the confidence construct. I ask the authors to report per-instance estimates (e.g., reliance and SEQ means for each of the eight instances) and, if feasible, an item-level analysis or a sensitivity analysis that excludes the most extreme instances.
  3. [Abstract and 5.3] The claim that counterfactual explanations 'enhanced overall accuracy' is not supported by the pre-specified analysis. H1c was evaluated at alpha = .01, and the feature-based comparison did not reach this threshold (Log-Odds = 0.34, p = .0349); the footnote reports a counterfactual effect at p = .0149, which also fails the stated threshold. The post hoc model in Section 5.3 reports a positive main effect for counterfactual explanations (Log-Odds = 0.87, p = .0015) but simultaneously reports a negative interaction with correct AI predictions (Log-Odds = -0.84, p < .0133), and the text acknowledges a 'trend' of decreased accuracy in that context. An exploratory main effect in a model with a significant interaction does not warrant the unqualified statement in the abstract and contribution list that counterfactuals 'enhanced overall accuracy'; the claim should be reworded as an exploratory finding with the negative interaction reported in the same sentence.
minor comments (5)
  1. [5.2.3] The last sentence says 'we fail to reject the null hypothesis for H3c'; this should refer to H3b.
  2. [5.3] In the second paragraph, 'delining' should be 'delineating'.
  3. [5.2.1 and footnote 7] Describing p = .0149 as showing an effect conflicts with the stated alpha = .01 threshold; the text should either call it a trend or explicitly note that it does not meet the threshold.
  4. [Figure 3 caption] The caption mentions 'lower and higher confidence intervals based on standard errors' where 'error bars' would be clearer, and the H1a panel does not appear to show the ticks referenced in the caption.
  5. [5.2.2] The sentence reporting H2a says users prioritize the explanation second and AI information third; this is the pattern predicted by H2b, so the wording should clarify that H2a is rejected precisely because the observed order differs from H2a's prediction.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the study's claims are empirical comparisons, not derivations from fitted inputs or self-citation chains.

full rationale

This paper reports an empirical user study (N = 288) rather than a derivation. The central claims—that high AI confidence increases reliance and reduces cognitive load (H1a, H1b)—are tested by mixed-effects logistic regression and GEE models on participant decisions and self-reported cognitive load. No parameter is fitted to the outcome and then renamed as a prediction; the low/high AI confidence split is a design choice based on test-set quartiles of model confidence, and the outcome measurements come from independent participant behavior. Similarly, the NFC median split is a standard grouping convention cited to prior work and does not enter into the construction of the dependent variables. The paper's self-citations to Cau et al. (2023a,b) are used to motivate hypotheses, but the present comparisons and statistical tests rely on newly collected data, not on those prior results. The skeptical concern that high- and low-confidence instances differ in objective difficulty is a potential validity confound, not circularity: the observed effect could be explained by instance properties, but it is not true by construction from the inputs. The paper's own limitations (uncalibrated confidence, one-stage paradigm, generalizability) are acknowledged and are also correctness/validity issues rather than circular reasoning. Therefore, no circular step is present.

Assumptions & free parameters 6 free parameters · 6 assumptions · 0 invented entities

The study's claims rest on several measurement and design choices: data-derived confidence and NFC splits, a hand-picked instance set with an accuracy mismatch, and operationalizations of cognitive load and explanation faithfulness. These are domain assumptions rather than derived facts. No new entities are postulated.

free parameters (6)
  • AI confidence split thresholds = low < 44.3; high > 61.6 (test-set quartiles Q2 and Q3)
    Defines the low and high confidence levels used in H1a and H1b; computed from the model's test-set confidence distribution, so the grouping is data-dependent.
  • NFC median split = sample median of NCS-6 scores
    Participants are divided into low and high Need for Cognition based on the sample median, which determines all H2 and H3 comparisons.
  • Anchors precision threshold = 95%
    Rule-based explanations were generated with this threshold; it affects which rules participants saw.
  • Number of neighbors/counterfactuals = 3 and 3
    Example-based and counterfactual explanations showed three instances to each participant; this limits the information available.
  • Instance selection for main session = 8 of 16 balanced instances; observed AI accuracy 62.5%
    The specific loan applications were hand-picked to balance AI correctness, confidence, predicted class, and true class, while deliberately lowering observed accuracy relative to the displayed 83%.
  • Random Forest estimators = 100
    Model hyperparameter chosen by the authors for the loan prediction task.
assumptions (6)
  • domain assumption The six-item Need for Cognition Scale (NCS-6) is a valid measure of the trait.
    Used to split participants into low and high NFC groups (Section 4.1 and Appendix A.2).
  • domain assumption The Single Ease Question (SEQ) captures cognitive load.
    Cognitive load was measured with a single 7-point ease question (Section 4.1).
  • domain assumption SHAP, Anchors, and DiCE explanations are faithful to the random forest's decisions.
    Feature-based, rule-based, and counterfactual conditions rely on these algorithms to explain the AI (Section 3.2.4).
  • domain assumption The loan dataset and random forest model constitute a realistic high-stakes AI assistance scenario.
    The authors converted the task to high-stakes with a monetary bonus, assuming this induces realistic decision behavior (Sections 3.2.1 and 4.3).
  • domain assumption Online participant behavior generalizes to real-world loan approval decision-making.
    The study uses Prolific participants unfamiliar with loan approval; the paper assumes results transfer to actual decision-makers.
  • standard math Statistical test assumptions (logistic regression, GEE, Friedman) are met.
    Mixed-effects models and ranking tests assume appropriate distributional properties; the paper states models converged (Section 4.2).

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Cite this review

Pith. "Pith review of Exploring the Impact of Explainable AI and Cognitive Capabilities on Users' Decisions." pith.science (2026). https://pith.science/paper/6A3WIDU3

@misc{pith2026250501192,
  author       = {Pith},
  title        = {Pith review of: Exploring the Impact of Explainable AI and Cognitive Capabilities on Users' Decisions},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/6A3WIDU3}},
  note         = {Machine review of arXiv:2505.01192}
}
read the original abstract

Artificial Intelligence (AI) systems are increasingly used for decision-making across domains, raising debates over the information and explanations they should provide. Most research on Explainable AI (XAI) has focused on feature-based explanations, with less attention on alternative styles. Personality traits like the Need for Cognition (NFC) can also lead to different decision-making outcomes among low and high NFC individuals. We investigated how presenting AI information (prediction, confidence, and accuracy) and different explanation styles (example-based, feature-based, rule-based, and counterfactual) affect accuracy, reliance on AI, and cognitive load in a loan application scenario. We also examined low and high NFC individuals' differences in prioritizing XAI interface elements (loan attributes, AI information, and explanations), accuracy, and cognitive load. Our findings show that high AI confidence significantly increases reliance on AI while reducing cognitive load. Feature-based explanations did not enhance accuracy compared to other conditions. Although counterfactual explanations were less understandable, they enhanced overall accuracy, increasing reliance on AI and reducing cognitive load when AI predictions were correct. Both low and high NFC individuals prioritized explanations after loan attributes, leaving AI information as the least important. However, we found no significant differences between low and high NFC groups in accuracy or cognitive load, raising questions about the role of personality traits in AI-assisted decision-making. These findings highlight the need for user-centric personalization in XAI interfaces, incorporating diverse explanation styles and exploring multiple personality traits and other user characteristics to optimize human-AI collaboration.

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Cited by 1 Pith paper

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Pith tools

Reviewed August 16, 2026 · model on record in the stance chip above.