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REVIEW 4 major objections 5 minor 44 references

Comparative Analysis of Black-Box and White-Box Machine Learning Model in Phishing Detection

T0 review · 4 major / 5 minor · reviewed 2026-08-11 · deepseek-v4-flash

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Pith's one-line read This paper claims that an Explainable Boosting Machine matches XGBoost's phishing detection accuracy while producing more stable, accurate, and actionable explanations.

desk verdict Useful accuracy benchmark of XGBoost vs EBM on 12 phishing datasets, but the explainability conclusion rests on unreproducible visual inspection of SHAP plots. read the letter →

arxiv 2412.02084 v1 pith:D3DFAEQ5 submitted 2024-12-03 cs.CR cs.AI

Abdullah Fajar , Setiadi Yazid , Indra Budi This is my paper · ORCID
classification cs.CRcs.AI
keywords phishingdetectionexplainableAIwhite-boxmodelblack-boxBoostingMachineXGBoostSHAPinterpretability
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 aims to settle which type of machine learning model, a black-box gradient-boosted tree model or a white-box explainable boosting machine, should be used for phishing detection when explanations matter. After running both models on twelve public phishing datasets, it finds that XGBoost and EBM achieve nearly matching accuracy, precision, recall, false-positive rate, and ROC AUC. The difference appears in explainability: the paper claims EBM delivers more stable, more accurate, and more actionable feature explanations than XGBoost, while the two are comparable on consistency and interpretability. The practical conclusion is that organizations needing transparent, actionable phishing alerts should prefer EBM, and that model choice should depend on whether the goal is raw performance or human-understandable insight.

What carries the argument

The central object is the Explainable Boosting Machine (EBM), a white-box model whose prediction is a sum of one-dimensional shape functions over individual features plus optional pairwise interactions, so each feature's contribution to a phishing score can be read directly. The comparison mechanism is SHAP: for both EBM and XGBoost, the paper visualizes SHAP value distributions on selected datasets and interprets their spread, direction, and ordering using a five-part rubric covering stability, consistency, accuracy of explanation, interpretability, and actionability. That rubric, applied to the plots, carries the claim that EBM explains better than XGBoost.

What would settle it

One concrete test is to train both models multiple times with different random seeds on the same twelve datasets, then measure the variance of SHAP values across seeds and across small input perturbations; if XGBoost's variance is not larger than EBM's, the claimed stability advantage fails. A user study where security analysts are shown EBM and XGBoost explanations and asked to identify the phishing-triggering feature would likewise settle whether the claimed actionability difference is real.

Watch

Extended reading notes

Core claim

The paper's central claim is that in phishing detection, a white-box Explainable Boosting Machine can match the predictive performance of XGBoost, a black-box gradient boosting model, while providing better explanations for its decisions. Across twelve public datasets, both models scored close on accuracy, precision, recall, false-positive rate, and ROC AUC, with EBM roughly 80 to 100 percent accuracy and XGBoost roughly 82 to 100 percent. The paper then rates explainability by inspecting SHAP value distributions and concludes that EBM's feature contributions are more stable, its explanations align better with domain knowledge, and its top features support more actionable security decisions. The recommendation is that applications needing explainability and actionable insight are better served by EBM.

Load-bearing premise

The load-bearing premise is that visually inspecting SHAP value distributions using the paper's ad hoc rating guidance is a valid and objective measure of explanation stability, consistency, accuracy, interpretability, and actionability; if that measurement is not valid, the claim that EBM explains better than XGBoost is unsupported.

Editorial extensions

If this is right

  • Phishing detection systems can adopt EBM without sacrificing much detection accuracy, since EBM is reported to match XGBoost on accuracy, precision, recall, false-positive rate, and AUC across all twelve datasets.
  • Security teams using EBM should be able to see which individual URL or content features pushed a site toward a phishing verdict, and turn that directly into a blocking rule or user warning.
  • XGBoost retains an advantage in scalability and training runtime, particularly on very large datasets where EBM's runtime grows sharply, such as the 129-thousand-instance and 662-thousand-instance datasets.
  • The paper's conclusion implies that model choice should be driven by deployment objective: EBM when explainability and actionability matter most, XGBoost when raw scalability and flexibility dominate.

Reading between the lines

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

  • A direct user study, in which security analysts or end users act on EBM versus XGBoost explanations, would test the actionability claim more rigorously than the paper's visual SHAP inspection, which is only a proxy.
  • Because both models are explained with SHAP, the measured differences in explanation stability and accuracy may partly reflect how SHAP attributes additive models versus tree ensembles; comparing multiple attribution methods would separate model effects from explanation-method effects.
  • The near-parity in accuracy suggests that interpretable models should be considered a strong default for phishing detection rather than a fallback, although the reported runtimes indicate that EBM's computational cost on very large datasets is a real constraint.
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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

4 major / 5 minor

Summary. This paper presents a comparative analysis of black-box (XGBoost) and white-box (Explainable Boosting Machine, EBM) models for phishing detection. The study evaluates both models on 12 public datasets in terms of predictive accuracy (accuracy, precision, recall, FP rate, ROC AUC, runtime) and explainability metrics (fidelity, simplicity, comprehensiveness, consistency, stability, accuracy of explanations, interpretability, actionability). The predictive performance comparison is based on straightforward experiments. The explainability comparison, however, relies on the authors' qualitative interpretation of SHAP value distributions, guided by ad hoc criteria listed in Section IV.B. The paper concludes that EBM outperforms XGBoost in actionability, explanation accuracy, and stability, and is generally better suited for applications requiring explainability and actionable insights. The abstract and conclusions also note that both models are comparable in interpretability and consistency, with room for improvement on diverse datasets.

Significance. If the central conclusion were supported, the paper would provide practical guidance for practitioners choosing between a black-box and a white-box model for phishing detection, where explainability is often a critical requirement. The study covers a wide range of public datasets, which is a strength, and the accuracy results are transparently reported in tabular form. The machine-checked nature of the accuracy experiments (though without code or data links) gives some confidence in the performance comparisons. However, the paper's main contribution — the explainability advantage of EBM — is not established by the presented evidence, because the ratings in Table 4 are subjective visual assessments. The paper also promises statistical tests that are not reported, and it contains internal inconsistencies in the stability ratings. These issues undermine the paper's central claim and, as currently written, the paper does not meet the standards of a rigorous comparative study in explainable AI.

major comments (4)
  1. [Section IV.B / Table 4] The central claim that 'EBM outperforms XGBoost in terms of actionability, explanation accuracy, and stability' rests entirely on Table 4, which rates the models based on the authors' visual interpretation of SHAP value distributions following the ad hoc guidance in points (a)–(e) of Section IV.B. No thresholds, blinded evaluation, inter-rater reliability, or quantitative operationalization is provided. For instance, 'narrow spreads indicate high stability' is not a measurable criterion, and the same guidance could plausibly lead different analysts to different ratings. To support the claim, the authors need to provide quantitative metrics—for example, the variance of SHAP values across perturbed inputs for stability, cosine similarity or rank correlation between explanations for consistency, and feature-ranking agreement with domain knowledge for explanation accuracy—or alternatively they must explicitly reframe the comparisons as qualitative observations and temper the conclusions accordingly.
  2. [Section III.B] Section III.B promises that 'statistical tests will validate the significance of observed differences between models' and that 'robustness analysis will be performed,' but no statistical tests, confidence intervals, or error bars are reported anywhere in Section IV. The accuracy tables (Tables 1 and 2) present single-point estimates without variance, so the reader cannot determine whether the small differences between XGBoost and EBM (e.g., 89.68% vs 88.97% on ds_100K20) are meaningful. The paper should either include paired statistical tests across the 12 datasets (e.g., Wilcoxon signed-rank test) or explicitly state that no such tests were conducted and adjust the strength of the comparative claims.
  3. [Table 3 vs. Section V/Conclusions] There is an internal inconsistency in the stability assessment. Table 3 rates both EBM and XGBoost as 'High (except Moderate for noisy datasets)' on the stability dimension, but Section V and the Conclusions state that EBM is better than XGBoost in stability, with Section IV.B's Table 4 rating EBM as 'Moderate to High' and XGBoost as 'Moderate.' The authors need to reconcile these two assessments; as written, the quantitative-looking Table 3 directly contradicts the qualitative conclusion, and the reader cannot tell which evidence is authoritative.
  4. [Reproducibility] The experiments are described without sufficient detail for reproduction: no code is provided, no hyperparameter settings are given (only 'optimized hyperparameters' is mentioned in Section III.B), and the specific dataset sources and preprocessing steps are not described. Dataset names such as 'ds_100K20' and 'ds_600K11_rev.csv' are not mapped to publicly identifiable resources, and the paper states only that datasets were 'taken from Kaggle.com and data.mendeley.com.' Without a reproducibility statement, the accuracy numbers cannot be verified, and the runtime comparisons are meaningless without hardware and software environment details. The authors should provide a data availability statement and, at minimum, an appendix with hyperparameters and the exact train-validation-test split methodology.
minor comments (5)
  1. [Figure captions] Figure 2's caption reads 'EDM and XGB in ds_11055 Explanation Plot' but the text refers to ds_10K18; Figure 5's caption says 'EBM and XGB in ds_600K11 Explanation Plot' while the text refers to ds_11K89. The captions should be corrected, and 'EDM' should be 'EBM.'
  2. [Abstract] The abstract states 'Experimental metrics covers 3 measurements such as predictive accuracy and explainability metrics,' but only two categories are listed; the sentence should be clarified to indicate exactly what the three measurements are.
  3. [Article history and keywords] The article history contains placeholder dates ('Received 5 April 20XX') that should be removed or filled, and the keywords contain typos ('Detction', 'Analisys', 'Explaiability') that must be corrected.
  4. [References] Several references are incomplete or malformed, including entries with 'undefined' as the author (e.g., [16], [23]) and missing page numbers or venue details. The reference list should be checked against a standard format.
  5. [Section II.C] The explainability metrics defined in Section II.C (fidelity, simplicity, comprehensiveness, etc.) are not operationalized in Section IV.B; for example, the 'Accuracy of Explanation' metric is assessed by 'validating feature ranking against domain knowledge' without any systematic scoring procedure. The authors should either define concrete measures for each metric or clearly label the assessments as subjective.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the comparison is an external benchmark on public datasets, and the qualitative SHAP-based ratings are a reproducibility concern, not a circular derivation.

full rationale

The paper's central claim is an empirical comparison of EBM and XGBoost for phishing detection, supported by accuracy, precision, recall, FP rate, ROC AUC, and runtime measured on twelve public datasets (Section IV.B). These performance numbers are external measurements, not outputs of a fitted parameter that is later relabeled as a prediction. The explainability ratings in Table 4 are the authors' visual interpretations of SHAP value distributions, guided by the ad hoc statements in Section IV.B such as 'narrow spreads indicate high stability.' Those ratings are subjective and not quantitatively operationalized, and Section III.B's promised statistical tests are not reported; however, subjectivity and missing validation are not circularity. Nothing in the paper defines the models' explainability metrics in terms of the conclusions, and no load-bearing argument reduces to a self-citation chain. The citations used for the analytical tables (e.g., [36]–[43]) are external sources, not the authors' prior work, and no uniqueness theorem or ansatz is smuggled in via self-citation. The internal tension between Table 3's 'High' stability rating for both models and the later claim that EBM is more stable is an inconsistency, but it does not make the derivation circular. Therefore, the circularity score is 0.

Assumptions & free parameters 1 free parameters · 3 assumptions · 0 invented entities

The paper introduces no new entities or fitted constants, but it relies on several unexamined domain assumptions, most importantly that SHAP visual patterns are a sufficient basis for labeling explainability dimensions. The model hyperparameters are undisclosed, which is a hidden free parameter.

free parameters (1)
  • Model hyperparameters (XGBoost and EBM) = Not reported
    The paper does not disclose hyperparameter values or tuning procedures for either model, so the reported accuracy and explainability results cannot be separated from these choices.
assumptions (3)
  • domain assumption The selected public phishing datasets are correctly labeled and representative of real-world phishing detection.
    The paper uses 12 unnamed Kaggle and Mendeley datasets without inspecting label quality or distribution shifts; the comparison's validity depends on this.
  • domain assumption SHAP value distributions are a valid proxy for the qualitative explainability dimensions (stability, consistency, accuracy, interpretability, actionability).
    Section IV.B states that SHAP spreads and patterns are interpreted from ad hoc guidance, but no external validation links SHAP visual properties to these constructs.
  • domain assumption The train/test splits and preprocessing are standard and do not introduce leakage.
    The method section mentions preparation and partitioning but gives no details; the conclusions assume the reported metrics are unbiased.

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Cite this review

Pith. "Pith review of Comparative Analysis of Black-Box and White-Box Machine Learning Model in Phishing Detection." pith.science (2026). https://pith.science/paper/D3DFAEQ5

@misc{pith2026241202084,
  author       = {Pith},
  title        = {Pith review of: Comparative Analysis of Black-Box and White-Box Machine Learning Model in Phishing Detection},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/D3DFAEQ5}},
  note         = {Machine review of arXiv:2412.02084}
}
read the original abstract

Background: Explainability in phishing detection model can support a further solution of phishing attack mitigation by increasing trust and understanding how phishing can be detected. Objective: The aims of this study to determine and best recommendation to apply an approach which has several components with abilities to fulfil the critical needs Methods: A methodology starting with analyzing both black-box and white-box models to get the pros and cons specifically in phishing detection. The conclusion of the analysis will be validated by experiment using a set of well-known algorithms and public phishing datasets. Experimental metrics covers 3 measurements such as predictive accuracy and explainability metrics. Conclusion: Both models are comparable in terms of interpretability and consistency, with room for improvement in diverse datasets. EBM as an example of white-box model is generally better suited for applications requiring explainability and actionable insights. Finally, each model, white-box and black-box model has positive and negative aspects both for performance metric and for explainable metric. It is important to consider the objective of model usage.

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

44 extracted references · 21 canonical work pages

  1. [1]

    ‘How do I fool you?’: Manipulating User Trust via Misleading Black Box Explanations,

    H. Lakkaraju and O. Bastani, “‘How do I fool you?’: Manipulating User Trust via Misleading Black Box Explanations,” Cornell University. Jan. 2019. doi: 10.48550/arxiv.1911.06473

    arXiv doi:10.48550/arxiv.1911.06473 2019

  2. [2]

    Exquisite Analysis of Popular Machine Learning –Based Phishing Detection Techniques for Cyber Systems,

    M. Das, S. Saraswathi, R. Panda, A. K. Mishra, and A. K. Tripathy, “Exquisite Analysis of Popular Machine Learning –Based Phishing Detection Techniques for Cyber Systems,” Taylor & Francis, vol. 16, no. 4, pp. 538–562, Sep. 2020, doi: 10.1080/19361610.2020.1816440

    arXiv 2020

  3. [3]

    Explainable Artificial Intelligence Approaches: A Survey,

    S. R. Islam, W. Eberle, S. Ghafoor, and M. Ahmed, “Explainable Artificial Intelligence Approaches: A Survey,” Cornell University. Jan

    work page

  4. [4]

    Explainable AI (XAI): A Systematic Meta-Survey of Current Challenges and Future Opportunities,

    W. Saeed and C. Omlin, “Explainable AI (XAI): A Systematic Meta-Survey of Current Challenges and Future Opportunities,” Knowl. Based Syst., vol. 263, p. 110273, 2021, doi: 10.1016/j.knosys.2023.110273

    arXiv 2021

  5. [5]

    Sok: Explainable machine learning for computer security applications,

    A. Nadeem, D. Vos, C. Cao, L. Pajola, and ..., “Sok: Explainable machine learning for computer security applications,” 2023 IEEE 8th …, 2023, [Online]. Available: https://ieeexplore.ieee.org/abstract/document/10190524/

    work page arXiv 2023

  6. [6]

    ‘Why Should I Trust You?’: Explaining the Predictions of Any Classifier,

    M. T. Ribeiro, S. Singh, and C. Guestrin, “‘Why Should I Trust You?’: Explaining the Predictions of Any Classifier,” Cornell University. Jan. 2016. doi: 10.48550/arXiv.1602

    doi:10.48550/arxiv.1602 2016

  7. [7]

    Evaluating Explanation Methods for Deep Learning in Security

    A. Warnecke, D. J. Arp, C. Wressnegger, and K. Rieck, “Don’t Paint It Black: White -Box Explanations for Deep Learning in Computer Security.,” Cornell University, Jun. 2019, [Online]. Available: https://arxiv.org/abs/1906.02108v1

    work page Pith review arXiv 2019

  8. [8]

    Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges,

    C. Rudin, C. Chen, Z. Chen, H. Huang, L. Semenova, and C. Zhong, “Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges,” Cornell University. Jan. 2021. doi: 10.48550/arXiv.2103

    doi:10.48550/arxiv.2103 2021

Show all 44 references
  1. [9]

    VORTEX : Visual phishing detectiOns aRe Through EXplanations,

    F. Charmet, T. Morikawa, A. Tanaka, and T. Takahashi, “VORTEX : Visual phishing detectiOns aRe Through EXplanations,” ACM Trans. Internet Technol., vol. 24, no. 2, pp. 1–24, May 2024, doi: 10.1145/3654665

    2024 doi

  2. [10]

    Intelligent explanation generation system for phishing webpages by employing an inference system,

    G. Ramesh, “Intelligent explanation generation system for phishing webpages by employing an inference system,” Behaviour and Information Technology, vol. 36, no. 12, pp. 1244–1260, 2017, doi: 10.1080/0144929X.2017.1369569

    2017

  3. [11]

    An Innovative Information Theory -based Approach to Tackle and Enhance The Transparency in Phishing Detection,

    V. Nguyen, T. Wu, X. Yuan, M. Grobler, S. Nepal, and C. Rudolph, “An Innovative Information Theory -based Approach to Tackle and Enhance The Transparency in Phishing Detection,” 2024. [Online]. Available: https://api.semanticscholar.org/CorpusID:268032530

    2024

  4. [12]

    Explaining URL Phishing Detection by Glass Box Models,

    S. Mittal, “Explaining URL Phishing Detection by Glass Box Models,” Proceedings of the 2023 Fifteenth International Conference on Contemporary Computing, 2023, [Online]. Available: https://api.semanticscholar.org/CorpusID:263147290

    2023

  5. [13]

    Explainable Machine Learning for Bag of Words-Based Phishing Detection,

    M. C. Calzarossa, P. Giudici, and R. Zieni, “Explainable Machine Learning for Bag of Words-Based Phishing Detection,” … on Explainable Artificial Intelligence, 2023, doi: 10.1007/978-3-031-44064-9_28

    2023 doi

  6. [14]

    Explanations in warning dialogs to help users defend against phishi ng attacks,

    G. Desolda, J. Aneke, C. Ardito, R. Lanzilotti, and ..., “Explanations in warning dialogs to help users defend against phishi ng attacks,” International Journal of …, 2023, [Online]. Available: https://www.sciencedirect.com/science/article/pii/S1071581923000654

    2023

  7. [15]

    An explainable AI model to help users avoid being victims of phishing attacks,

    F. Greco, “An explainable AI model to help users avoid being victims of phishing attacks,” Master Degree in Computer Science, Computer …. 2022

    2022

  8. [16]

    The State of Phishing

    undefined, “The State of Phishing.” Apr. 2023. [Online]. Available: https://www.slashnext.com/wp-content/uploads/2022/10/SlashNext-The- State-of-Phishing-2022.pdf

    2023

  9. [17]

    Phishing Trends 2023

    “Phishing Trends 2023.” [Online]. Available: https://get.zerofox.com/rs/143 -DHV-007/images/ZeroFox-Intelligence-Assessment-2023- Phishing-Trends-Report.pdf

    2023

  10. [18]

    CERT strategy to deal with phishing attacks,

    S. Sedaghat, “CERT strategy to deal with phishing attacks,” Cornell University. Jan. 2017. doi: 10.48550/arXiv.1706

    2017 doi

  11. [19]

    How Good Are We at Detecting a Phishing Attack? Investigating the Evolving Phishing Attack Email and Why It Continues to Successfully Deceive Society,

    F. Carroll, J. A. Adejobi, and R. Montasari, “How Good Are We at Detecting a Phishing Attack? Investigating the Evolving Phishing Attack Email and Why It Continues to Successfully Deceive Society,” SN Computer Science, vol. 3, no. 2, p. 170, Feb. 2022, doi: 10.1007/s42979- 022-01069-1

    2022 doi

  12. [20]

    Scam Pandemic: How Attackers Exploit Public Fear through Phishing,

    M. Bitaab et al. , “Scam Pandemic: How Attackers Exploit Public Fear through Phishing,” Nov. 2020, doi: 10.1109/ecrime51433.2020.9493260

    2020

  13. [21]

    Techniques for interpretable machine learning,

    M. Du, N. Liu, and X. Hu, “Techniques for interpretable machine learning,” Association for Computing Machinery, vol. 63, no. 1, pp. 68 – 77, Dec. 2019, doi: 10.1145/3359786

    2019 doi

  14. [22]

    Explaining Explanations: An Overview of Interpretability of Machine Learning,

    L. H. Gilpin, D. Bau, B. Z. Yuan, A. Bajwa, M. Specter, and L. Kagal, “Explaining Explanations: An Overview of Interpretability of Machine Learning,” Cornell University. Jan. 2018. doi: 10.48550/arxiv.1806.00069

    2018 doi arXiv

  15. [23]

    InterpretML

    undefined, “InterpretML.” Jan. 2023. [Online]. Available: https://interpret.ml/

    2023

  16. [24]

    Explainable AI: Interpreting, Explaining and Visualizing Deep Learning,

    W. Samek, G. Montavon, A. Vedaldi, L. K. Hansen, and Klaus Müller, “Explainable AI: Interpreting, Explaining and Visualizing Deep Learning,” Explainable AI, 2019

    2019

  17. [25]

    Understanding Global Feature Contributions With Additive Importance Measures,

    I. Covert, S. Lundberg, and S.-I. Lee, “Understanding Global Feature Contributions With Additive Importance Measures,” Cornell University. Jan. 2020. doi: 10.48550/arXiv.2004

    2020 doi

  18. [26]

    Unpack Local Model Interpretation for GBDT,

    W. Fang, J. Zhou, X. Li, and K. Q. Zhu, “Unpack Local Model Interpretation for GBDT,” Springer Science+Business Media. pp. 764–775, Jan. 2018. doi: 10.1007/978-3-319-91458-9_48

    2018 doi

  19. [27]

    Using Rule Extraction to Improve the Comprehensibility of Predictive Models,

    J. Huysmans, B. Baesens, and J. Vanthienen, “Using Rule Extraction to Improve the Comprehensibility of Predictive Models,” RELX Group (Netherlands), Jan. 2006, doi: 10.2139/ssrn.961358

    2006 doi

  20. [28]

    General Pitfalls of Model-Agnostic Interpretation Methods for Machine Learning Models,

    C. Molnar et al., “General Pitfalls of Model-Agnostic Interpretation Methods for Machine Learning Models,” Cornell University. Jan. 2020. doi: 10.48550/arxiv.2007.04131

    2020 doi arXiv

  21. [29]

    Please Stop Explaining Black Box Models for High Stakes Decisions.,

    C. Rudin, “Please Stop Explaining Black Box Models for High Stakes Decisions.,” Cornell University, Nov. 2018, [Online]. Available: https://arxiv.org/pdf/1811.10154.pdf

    2018 arXiv

  22. [30]

    The black box problem of AI in oncology,

    M. Hagenbuchner, “The black box problem of AI in oncology,” IOP Publishing, vol. 1662, no. 1, pp. 012012 –012012, Oct. 2020, doi: 10.1088/1742-6596/1662/1/012012

    2020 doi

  23. [31]

    Faithful and Customizable Explanations of Black Box Models,

    H. Lakkaraju, E. Kamar, R. Caruana, and J. Leskovec, “Faithful and Customizable Explanations of Black Box Models,” in Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society, in AIES ’19. New York, NY, USA: Association for Computing Machinery, 2019, pp. 131–138....

    2019

  24. [32]

    How Experts Detect Phishing Scam Emails,

    R. Wash, “How Experts Detect Phishing Scam Emails,” Proceedings of the ACM on human -computer interaction, vol. 4, no. CSCW2. pp. 1–28, Oct. 2020. doi: 10.1145/3415231

    2020 doi

  25. [33]

    Visualizing and Interpreting RNN Models in URL -Based Phishing Detection,

    T. Feng and C. Yue, “Visualizing and Interpreting RNN Models in URL -Based Phishing Detection,” in Proceedings of the 25th ACM Symposium on Access Control Models and Technologies , in SACMAT ’20. New York, NY, USA: Association for Computing Machinery, 2020, pp. 13–24. doi: 10....

    2020

  26. [34]

    Definitions, methods, and applications in interpretable machine learning,

    W. J. Murdoch, C. Singh, K. Kumbier, R. Abbasi-Asl, and B. Yu, “Definitions, methods, and applications in interpretable machine learning,” Proceedings of the National Academy of Sciences, vol. 116, no. 44, pp. 22071–22080, 2019, doi: 10.1073/pnas.1900654116

    2019 doi

  27. [35]

    SoK: A Comprehensive Reexamination of Phishing Research From the Security Perspective,

    A. Das, S. Baki, A. E. Aassal, R. M. Verma, and A. Dunbar, “SoK: A Comprehensive Reexamination of Phishing Research From the Security Perspective,” IEEE Communications Surveys & Tutorials, vol. 22, pp. 671–708, 2019, doi: 10.1109/COMST.2019.2957750

    2019

  28. [36]

    Interpretability as Approximation: Understanding Black -Box Models by Decision Boundary,

    H. Dong, B. Liu, D. Ye, and G. Liu, “Interpretability as Approximation: Understanding Black -Box Models by Decision Boundary,” Electronics, vol. 13, no. 22, 2024, doi: 10.3390/electronics13224339

    2024 doi

  29. [37]

    Explainable Artificial Intelligence: A Review and Case Study on Model -Agnostic Methods,

    K. Letrache and M. Ramdani, “Explainable Artificial Intelligence: A Review and Case Study on Model -Agnostic Methods,” in 2023 14th International Conference on Intelligent Systems: Theories and Applications (SITA), 2023, pp. 1–8. doi: 10.1109/SITA60746.2023.10373722

    2023

  30. [38]

    Interpreting black -box models: a review on explainable artificial intelligence,

    V. Hassija, V. Chamola, A. Mahapatra, A. Singal, and ..., “Interpreting black -box models: a review on explainable artificial intelligence,” Cognitive …. Springer, 2023. doi: 10.1007/s12559-023-10179-8

    2023 doi

  31. [39]

    Benchmarking and survey of explanation methods for black box models,

    F. Bodria, F. Giannotti, R. Guidotti, F. Naretto, D. Pedreschi, and S. Rinzivillo, “Benchmarking and survey of explanation methods for black box models,” Data Mining and Knowledge Discovery, vol. 37, no. 5, pp. 1719–1778, Sep. 2023, doi: 10.1007/s10618-023-00933-9

    2023 doi

  32. [40]

    The Past, Present, and Prospe ctive Future of XAI: A Comprehensive Review,

    M. U. Islam, Md. Mozaharul Mottalib, M. Hassan, Z. I. Alam, S. M. Zobaed, and Md. Fazle Rabby, “The Past, Present, and Prospe ctive Future of XAI: A Comprehensive Review,” in Explainable Artificial Intelligence for Cyber Security: Next Generation Artificial Intelligence, M. Ah...

    2022 doi

  33. [41]

    Measures for explainable AI: Explanation goodness, user satisfaction, menta l models, curiosity, trust, and human-AI performance,

    R. R. Hoffman, S. T. Mueller, G. Klein, and ..., “Measures for explainable AI: Explanation goodness, user satisfaction, menta l models, curiosity, trust, and human-AI performance,” Frontiers in Computer …. frontiersin.org, 2023. doi: 10.3389/fcomp.2023.1096257

    2023

  34. [42]

    Molnar, Interpretable Machine Learning: A Guide for Making Black Box Models Explainable , 2nd ed

    C. Molnar, Interpretable Machine Learning: A Guide for Making Black Box Models Explainable , 2nd ed. 2022. [Online]. Available: https://christophm.github.io/interpretable-ml-book

    2022

  35. [43]

    Measuring Interpretability for Different Types of Machine Learning Models,

    Q. Zhou, F. Liao, C. Mou, and P. Wang, “Measuring Interpretability for Different Types of Machine Learning Models,” in Trends and Applications in Knowledge Discovery and Data Mining , M. Ganji, L. Rashidi, B. C. M. Fung, and C. Wang, Eds., Cham: Springer International Publishi...

    2018

  36. [2021]

    doi: 10.48550/arxiv.2101.09429

    doi arXiv

Pith tools

Reviewed August 11, 2026 · model on record in the stance chip above.