A Comprehensive Guide to Explainable AI: From Classical Models to LLMs

Benji Peng; Bowen Jing; Chia Xin Liang; Chuanqi Jiang; Hong-Ming Tseng; Jiawei Xu; Jinlang Wang; Jintao Ren; Junhao Song; Junjie Yang

REVIEW 3 major objections 4 minor 291 references

A Comprehensive Guide to Explainable AI: From Classical Models to LLMs

T0 review · 3 major / 4 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read This book-length guide argues that explainable AI is a teachable, unified field spanning classical interpretable models, post-hoc attribution methods, and large-language-model probing, and supports the argument with Python examples.

desk verdict A broad, mostly sound XAI survey whose practical value is undercut by a handful of fixable code and structural errors. read the letter →

arxiv 2412.00800 v2 pith:5ZJ4UUMV submitted 2024-12-01 cs.LG cs.AI

Weiche Hsieh , Ziqian Bi , Chuanqi Jiang , Junyu Liu , Benji Peng , Sen Zhang , Xuanhe Pan , Jiawei Xu

show 19 more authors

Jinlang Wang Keyu Chen Pohsun Feng Yizhu Wen Xinyuan Song Tianyang Wang Ming Liu Junjie Yang Ming Li Bowen Jing Jintao Ren Junhao Song Hong-Ming Tseng Yichao Zhang Lawrence K.Q. Yan Qian Niu Silin Chen Yunze Wang Chia Xin Liang

This is my paper · ORCID

classification cs.LGcs.AI

keywords ExplainableAIinterpretabilityfeatureattributionSHAPLIMEGrad-CAMlargelanguagemodelscounterfactualexplanations

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

This work is a book-length guide attempting to establish that explainable AI (XAI) can be presented as one coherent curriculum, from intrinsically interpretable models (decision trees, linear models, GAMs, Bayesian models) through post-hoc attribution techniques (SHAP, LIME, Integrated Gradients, Grad-CAM, LRP) to the special interpretability challenges of CNNs, RNNs, and large language models. A sympathetic reader would take the central claim to be that a reader with basic machine learning knowledge can, by following this guide, acquire both the theoretical distinctions (transparency, interpretability, explainability, fairness) and the practical skills needed to explain real models. The book also claims that the same XAI toolkit applies across healthcare, finance, and policy, and that explanation quality can be assessed with metrics like fidelity, stability, and faithfulness. If this is right, the volume is a practical educational resource rather than a research monograph, and its value lies in how well its code examples and organization deliver that curriculum.

What carries the argument

The organizing machinery is a two-axis view of interpretability: intrinsic versus post-hoc, and model-based versus model-agnostic techniques. The main working objects are feature attribution methods — SHAP values from cooperative game theory, LIME's local surrogate models, Integrated Gradients' path integrals, Grad-CAM's gradient-weighted activation maps, and Layer-wise Relevance Propagation's backward relevance decomposition — together with attention-weight visualization and embedding/probing analysis for transformers and LLMs. Code examples are the load-bearing mechanism for transferring the techniques to readers; each chapter pairs a method with a minimal Python implementation, and the book's promise of practical mastery rests on those implementations being correct.

What would settle it

Run the Section 4.3.1 RNN code and inspect what `model.predict(X)` returns: if the plotted curves are the network's final predictions rather than the 10 hidden-unit activations the caption claims, the example does not demonstrate hidden-state interpretation. More generally, executing each chapter's code and checking that the printed outputs and figures match the described behavior would settle whether the practical promise holds.

Watch

Extended reading notes

Core claim

On the paper's own terms, the central discovery is organizational: the many threads of XAI — intrinsic model interpretability, feature attribution, counterfactual and causal explanation, attention and embedding analysis, evaluation metrics, and tooling — belong in a single narrative that runs from classical models to LLMs. The guide asserts that this narrative can be made hands-on, with Python code for each technique, and that the resulting competence lets practitioners debug models, satisfy regulatory demands for explanation, and audit fairness. No new algorithm or empirical result is claimed; the contribution is the synthesis and its pedagogical packaging.

Load-bearing premise

The guide's value as a practical resource depends on its code examples actually implementing the techniques they describe, and the RNN hidden-state example in Section 4.3.1 already plots the model's output as though it were hidden states, so this premise is not fully met.

Editorial extensions

If this is right

A reader who works through the guide is expected to be able to apply SHAP, LIME, and related methods to tabular, image, and text models, and to distinguish local from global explanations.
The book implies that interpretability is not one property: transparency, interpretability, explainability, and fairness are related but distinct, so evaluation should use multiple metrics.
LLM interpretability is treated as an extension of existing machinery (gradient attribution, probing classifiers, attention analysis) rather than a separate field, so skills transfer from classical models to BERT, GPT, and T5.
Case studies in healthcare, finance, and policy are presented as evidence that XAI methods have practical decision-support value beyond model debugging.

Reading between the lines

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

The guide's own Section 4.3.1 example plots the model's prediction output as if it were hidden-state activations; if similar mismatches occur elsewhere, readers following the code would internalize a misleading picture of what the code computes.
A natural extension the author leaves implicit is a curated, testable companion suite that verifies each code example against the method it claims to illustrate, especially the RNN and attention examples.
The synthesis suggests a research hypothesis: a practitioner trained on classical post-hoc methods can transfer that training to LLM interpretability faster than one starting directly with LLM-specific tools; this could be tested with a controlled learning study.

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Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

This is a survey, not a research paper. It covers a lot: decision trees, SHAP, LIME, Grad-CAM, LLM interpretability, even causal inference and counterfactuals. The organization is sensible, the citations look standard, and many of the code examples are clear and reproducible. If you want a quick map of the XAI landscape, this is a reasonable place to start.

The problems are where the book makes its strongest promise: hands-on practical skills. The stress-test concern is real. In Section 4.3.1 the code defines a SimpleRNN followed by a Dense layer, then sets both y_pred and hidden_states to model.predict(X). Those calls are identical, so 'hidden_states' is the Dense output, not the RNN hidden-unit activations. The plot labeled 'Activations of All Hidden Units Over Time' actually draws a single trace of the model's prediction. A reader following along would not learn how to visualize hidden states. That is a genuine bug in a core deliverable.

There are also structural slips. The 'Advantages and Disadvantages of Logistic Regression' block appears inside the Decision Trees section (3.2), and the same block is duplicated in 3.3. Some chapters, like Applications and Tools, are little more than lists; they don't go deep enough to be called comprehensive. These are soft spots, but they are fixable.

The theoretical descriptions of standard XAI methods are largely correct. Shapley values, LIME's local surrogate objective, Integrated Gradients, and Grad-CAM are all presented accurately. The authors are not inventing anything, but they are also not misrepresenting established work. The citation pattern seems honest; nothing looks self-serving or circular.

The bottom line: as a research contribution, this is a zero. As a textbook draft, it is usable but needs revision. The code error in 4.3.1 should be corrected, the misplaced Logistic Regression material moved, and the thin chapters either expanded or honestly labeled as overviews. I would send it to a serious referee, not for the science, but because it has the potential to be a genuinely useful educational reference once the errors are fixed.

Referee Report

3 major / 4 minor

Summary. This manuscript is a book-length survey/guide to Explainable AI, covering theoretical foundations, interpretability of classical models (decision trees, linear models, SVMs, rule-based systems, GAMs, Bayesian models), deep learning models (CNNs, RNNs, Transformers), large language models (BERT, GPT, T5, LLaMA), a wide range of XAI techniques (SHAP, LIME, Integrated Gradients, Grad-CAM, LRP, counterfactuals, causal methods, graph and multimodal methods), applications, evaluation metrics, tools, and future directions. The stated goal, in the Abstract and §1.4, is to provide a comprehensive and practical guide that 'bridges theory and practice' through Python code examples and a companion GitHub repository.

Significance. If the code examples and organizational structure were reliable, this would be a useful educational resource: the breadth is substantial, and the practical emphasis with runnable Python snippets for SHAP, LIME, Integrated Gradients, DeepLIFT, Grad-CAM, LRP, and several counterfactual methods is a genuine strength. The book also gives credit to many standard references and includes a counterpart GitHub repository, which supports reproducibility in principle. However, the central value proposition—a hands-on, trustworthy guide—depends critically on the correctness of the code and the coherence of the presentation, and both are currently undermined by the errors described below.

major comments (3)

[§4.3.1, lines 24–25] The code defines a SimpleRNN followed by a Dense layer (lines 15–18) and then computes both `y_pred` and `hidden_states` as `model.predict(X)` (lines 24–25). These are identical calls, so `hidden_states` is the Dense output, not the RNN hidden-state activations. Concretely, `hidden_states.shape` is (1, 100, 1), so the loop `for i in range(hidden_states.shape[-1])` draws only one trace, not the activations of all 10 hidden units as the figure caption claims. A reader following the code would not learn how to visualize hidden states; they would see the model's prediction plotted under a misleading label. This error directly contradicts the promise in §1.4 of providing 'hands-on understanding' and must be corrected, for example by building a separate model that outputs `model.layers[0].output`.
[§3.2] The subsection titled 'Advantages and Disadvantages of Logistic Regression' appears in the middle of the Decision Trees section, immediately after the discussion of pruning and before the decision-tree feature-importance code example. This is a structural error: the content belongs in §3.3, which already contains a logistic regression discussion, and its placement here is confusing for a reader using the book as a reference. The guide's claim to be comprehensive and reliable is weakened by such organization mistakes, and this passage should be relocated or removed as a duplicate.
[§1.2 vs. §5.4.3] The book gives inconsistent definitions of its two central terms. In §1.2, 'interpretability' is defined as 'the degree to which a human can understand the cause of a decision,' and 'explainability' is defined as 'the extent to which the internal mechanics of a machine learning model can be understood,' with explainability said to go further by focusing on 'why.' In §5.4.3, however, 'interpretability' is described as understanding internal workings (e.g., neurons and layers), while 'explainability' is described as providing human-understandable reasons such as feature importance or visualizations. These are essentially swapped. Because the book explicitly introduces these terms as foundational concepts, the inconsistency is pedagogically misleading and should be reconciled in a revision.

minor comments (4)

[§2.4.1, code listing] Lines 8 and 14 of the SHAP example appear in the manuscript text as bare sentences ('Load the dataset', 'Split the dataset...') without the '#' comment prefix; if reproduced as written, the code is syntactically invalid. Please ensure all comment lines are properly prefixed in the printed code.
[§5.6.2] The text states that the layer-activation plot shows 'all 13 layers of BERT,' but BERT-base has 12 transformer layers; the 13 hidden states returned by `output_hidden_states=True` include the embedding layer. The wording should be clarified to avoid confusion.
[§4.4.3] The attention-heatmap example uses a synthetic 3×3 weight matrix, but the preceding text discusses the phrase 'The cat sat' and alignment in machine translation; the figure caption and the result explanation should make clear that the heatmap is illustrative and not computed from a real model on that phrase.
[§5.2, GPT-4 bullet] The claim that GPT-4 supports multimodal inputs is referenced to a generic citation [102]; a primary source or clearer specification of the model version would be more helpful to readers.

Circularity Check

0 steps flagged · score 0.0 of 10

No circular reasoning found; the guide is a survey-style resource with no derivation chain whose outputs reduce to its inputs.

full rationale

This manuscript is a comprehensive survey and educational guide to explainable AI, not a research paper making novel predictions or deriving results from fitted parameters. The central claims are descriptive promises about coverage and pedagogy, and the content consists of textbook explanations, literature summaries, and illustrative code examples. I checked for the seven circularity patterns: there is no parameter fitted to a subset and then presented as a prediction, no equation defined in terms of the quantity it is said to explain, no load-bearing self-citation chain, and no imported uniqueness theorem forcing a particular choice. The cited works are standard external references (SHAP, LIME, Grad-CAM, BERT, etc.) that support the survey independently. I also noted that Section 4.3.1 contains a concrete implementation flaw: the code sets both `y_pred` and `hidden_states` to `model.predict(X)`, so the figure labeled "Activations of All Hidden Units Over Time" actually plots the Dense-layer output rather than the RNN hidden-state activations. This is a correctness and pedagogical quality defect that undermines the book's practical usefulness, but it is not a circularity: the mistake is a mismatch between code and narrative, not a reduction of a claimed result to its own input. Therefore, the appropriate circularity score is 0.

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

This is a review book; it does not introduce new parameters, axioms, or entities. It relies on standard definitions and prior literature.

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Cite this review

Pith. "Pith review of A Comprehensive Guide to Explainable AI: From Classical Models to LLMs." pith.science (2026). https://pith.science/paper/5ZJ4UUMV

@misc{pith2026241200800,
  author       = {Pith},
  title        = {Pith review of: A Comprehensive Guide to Explainable AI: From Classical Models to LLMs},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/5ZJ4UUMV}},
  note         = {Machine review of arXiv:2412.00800}
}

read the original abstract

Explainable Artificial Intelligence (XAI) addresses the growing need for transparency and interpretability in AI systems, enabling trust and accountability in decision-making processes. This book offers a comprehensive guide to XAI, bridging foundational concepts with advanced methodologies. It explores interpretability in traditional models such as Decision Trees, Linear Regression, and Support Vector Machines, alongside the challenges of explaining deep learning architectures like CNNs, RNNs, and Large Language Models (LLMs), including BERT, GPT, and T5. The book presents practical techniques such as SHAP, LIME, Grad-CAM, counterfactual explanations, and causal inference, supported by Python code examples for real-world applications. Case studies illustrate XAI's role in healthcare, finance, and policymaking, demonstrating its impact on fairness and decision support. The book also covers evaluation metrics for explanation quality, an overview of cutting-edge XAI tools and frameworks, and emerging research directions, such as interpretability in federated learning and ethical AI considerations. Designed for a broad audience, this resource equips readers with the theoretical insights and practical skills needed to master XAI. Hands-on examples and additional resources are available at the companion GitHub repository: https://github.com/Echoslayer/XAI_From_Classical_Models_to_LLMs.

Figures

Figures reproduced from arXiv: 2412.00800 by the authors.

**Figure 2.1.** SHAP Summary Plot for Feature Importance Visualization [PITH_FULL_IMAGE:figures/full_fig_p014_2_1.png] view at source ↗

**Figure 3.1.** The interpretability-complexity continuum of common machine learning models [ [PITH_FULL_IMAGE:figures/full_fig_p016_3_1.png] view at source ↗

**Figure 3.2.** Visualization of Feature Importance in a Decision Tree [PITH_FULL_IMAGE:figures/full_fig_p020_3_2.png] view at source ↗

Figures from the paper (47 more)

**Figure 3.3.** Figure 3.3: Comparison of True vs Predicted House Prices Using Linear Regression [PITH_FULL_IMAGE:figures/full_fig_p022_3_3.png]

**Figure 3.4.** Figure 3.4: Logistic Regression Model Predictions for Customer Churn [PITH_FULL_IMAGE:figures/full_fig_p024_3_4.png]

**Figure 3.5.** Figure 3.5: Visualization of SVM Decision Boundary and Support Vectors [PITH_FULL_IMAGE:figures/full_fig_p027_3_5.png]

**Figure 3.6.** Figure 3.6: Partial Dependence Plots of Feature x1 and x2 in the GAM Model. Result Explanation In this example, we first generated a synthetic dataset where the target variable y is defined as a non-linear combination of features x1 and x2, specifically y = sin(x1)+0.5 cos(x2)+ε…

**Figure 3.7.** Figure 3.7: Posterior Distributions of Slope, Intercept, and Sigma in the Bayesian Linear Regression [PITH_FULL_IMAGE:figures/full_fig_p037_3_7.png]

**Figure 4.1.** Figure 4.1: Original Cat Image [PITH_FULL_IMAGE:figures/full_fig_p041_4_1.png]

**Figure 4.2.** Figure 4.2: Feature Maps from the First Convolutional Layer of VGG16 [PITH_FULL_IMAGE:figures/full_fig_p042_4_2.png]

**Figure 4.3.** Figure 4.3: Comparison of Original and Predicted Sine Wave [PITH_FULL_IMAGE:figures/full_fig_p045_4_3.png]

**Figure 4.4.** Figure 4.4: Activations of All Hidden Units Over Time [PITH_FULL_IMAGE:figures/full_fig_p046_4_4.png]

**Figure 4.5.** Figure 4.5: Sample Attention Heatmap helping us understand how the model attends to different parts of the input sequence. By analyzing these attention patterns, we can gain insights into the relationships between tokens and the model’s understanding of the context [94]. 4.4.4 M…

**Figure 5.1.** Figure 5.1: Clear Separation of Transformer Encoder-Decoder Architecture [PITH_FULL_IMAGE:figures/full_fig_p055_5_1.png]

**Figure 5.2.** Figure 5.2: PCA Visualization of BERT Word Embeddings for "king", "queen", "man", and "woman". [PITH_FULL_IMAGE:figures/full_fig_p059_5_2.png]

**Figure 5.3.** Figure 5.3: Mean Activations Across BERT Layers Relevance to Neural Layer-wise Interpretability The varying mean activation values across different layers provide insights into the hierarchical learning process of the BERT model: • Early Layers: Focus on word-level features and…

**Figure 5.4.** Figure 5.4: Output of the probing task showing progress bars and probing task accuracy. [PITH_FULL_IMAGE:figures/full_fig_p064_5_4.png]

**Figure 6.1.** Figure 6.1: Feature Importance Analysis for Decision Tree Classifier on the Iris Dataset [PITH_FULL_IMAGE:figures/full_fig_p070_6_1.png]

**Figure 6.2.** Figure 6.2: SHAP Summary Plot for Breast Cancer Prediction [PITH_FULL_IMAGE:figures/full_fig_p073_6_2.png]

**Figure 6.3.** Figure 6.3: LIME explanation for a positive sentiment review. Words like “classic," “best," and “ex [PITH_FULL_IMAGE:figures/full_fig_p076_6_3.png]

**Figure 6.4.** Figure 6.4: Integrated Gradients attribution map for a sample MNIST image. The highlighted pixels [PITH_FULL_IMAGE:figures/full_fig_p079_6_4.png]

**Figure 6.5.** Figure 6.5: DeepLIFT attribution map for a sample MNIST image. The highlighted areas reveal the [PITH_FULL_IMAGE:figures/full_fig_p083_6_5.png]

**Figure 6.6.** Figure 6.6: Saliency map for an MNIST image. The map shows which pixels the model focused on [PITH_FULL_IMAGE:figures/full_fig_p086_6_6.png]

**Figure 6.7.** Figure 6.7: SmoothGrad attribution map for a sample MNIST image. The map reveals the most [PITH_FULL_IMAGE:figures/full_fig_p089_6_7.png]

**Figure 6.8.** Figure 6.8: Example of Grad-CAM on a Pre-trained VGG16 Model: Original Image, Grad-CAM [PITH_FULL_IMAGE:figures/full_fig_p092_6_8.png]

**Figure 6.9.** Figure 6.9: Partial Dependence Plot for MedInc (Median Income) on California Housing Dataset [PITH_FULL_IMAGE:figures/full_fig_p099_6_9.png]

**Figure 6.10.** Figure 6.10: ICE Plot for MedInc (Median Income) on California Housing Dataset [PITH_FULL_IMAGE:figures/full_fig_p101_6_10.png]

**Figure 6.11.** Figure 6.11: ICE Plot for MedInc (Median Income) on California Housing Dataset [PITH_FULL_IMAGE:figures/full_fig_p103_6_11.png]

**Figure 6.12.** Figure 6.12: SHAP Dependence Plot for AveRooms (Average Number of Rooms per Household) on [PITH_FULL_IMAGE:figures/full_fig_p105_6_12.png]

**Figure 6.13.** Figure 6.13: Feature Importance for California Housing Dataset based on SHAP Values [PITH_FULL_IMAGE:figures/full_fig_p108_6_13.png]

**Figure 6.14.** Figure 6.14: SHAP Force Plot for a Single Prediction Advantages of Force Plots • Intuitive Visualization: The Force Plot provides a clear and intuitive view of how each feature contributes to a specific prediction, making it easier to understand the model’s decision process [23…

**Figure 6.15.** Figure 6.15: SHAP Decision Plot for California Housing Predictions [PITH_FULL_IMAGE:figures/full_fig_p112_6_15.png]

**Figure 6.16.** Figure 6.16: SHAP Summary Plot of Feature Interactions for California Housing Predictions [PITH_FULL_IMAGE:figures/full_fig_p115_6_16.png]

**Figure 6.17.** Figure 6.17: Visualization of SHAP Values for a Time-Series Instance [PITH_FULL_IMAGE:figures/full_fig_p118_6_17.png]

**Figure 6.18.** Figure 6.18: DTW Alignment Visualization with Warping Path [PITH_FULL_IMAGE:figures/full_fig_p120_6_18.png]

**Figure 6.19.** Figure 6.19: Original Data, Predicted Values, and Attention Weights [PITH_FULL_IMAGE:figures/full_fig_p124_6_19.png]

**Figure 6.20.** Figure 6.20: Original Data, Predicted Data, and Saliency Map [PITH_FULL_IMAGE:figures/full_fig_p127_6_20.png]

**Figure 6.21.** Figure 6.21: Causal Graph Identified by the PC Algorithm [PITH_FULL_IMAGE:figures/full_fig_p137_6_21.png]

**Figure 6.22.** Figure 6.22: The original image (left) is classified as the digit â [PITH_FULL_IMAGE:figures/full_fig_p147_6_22.png]

**Figure 6.23.** Figure 6.23: (Left) Original image labeled as "7". (Right) Counterfactual image labeled as "8". [PITH_FULL_IMAGE:figures/full_fig_p150_6_23.png]

**Figure 6.24.** Figure 6.24: Generated Diverse Counterfactuals for an MNIST Image [PITH_FULL_IMAGE:figures/full_fig_p156_6_24.png]

**Figure 6.25.** Figure 6.25: Feature Importance Plot for Top 10 Features [PITH_FULL_IMAGE:figures/full_fig_p175_6_25.png]

**Figure 6.26.** Figure 6.26: Subgraph Visualization Highlighting Influential Edges [PITH_FULL_IMAGE:figures/full_fig_p175_6_26.png]

**Figure 6.27.** Figure 6.27: Example Model Summary [PITH_FULL_IMAGE:figures/full_fig_p182_6_27.png]

**Figure 6.28.** Figure 6.28: Example Model Structure • Improved Performance: Attention mechanisms often lead to better model performance by focusing on the most relevant parts of the input data [229]. Limitations Despite its benefits, this technique has certain limitations: • Computational Over…

**Figure 6.29.** Figure 6.29: Example Model Structure for image features. Peaks in the attribution scores highlight the features that significantly impact the model’s output, providing insights into the model’s interpretability. Advantages of Joint Feature Attribution • Comprehensive Interpretab…

**Figure 6.30.** Figure 6.30: Cross-modal Attention Weights (Text to Image Features) [PITH_FULL_IMAGE:figures/full_fig_p190_6_30.png]

**Figure 6.31.** Figure 6.31: Feature Attribution Disparity between Male and Female. The bar plot shows the dif [PITH_FULL_IMAGE:figures/full_fig_p194_6_31.png]

**Figure 6.32.** Figure 6.32: Comparison of Saliency Maps. The left image shows the original saliency map, while the [PITH_FULL_IMAGE:figures/full_fig_p197_6_32.png]

**Figure 6.33.** Figure 6.33: Comparison of Saliency Maps. The left image shows the original saliency map, while the [PITH_FULL_IMAGE:figures/full_fig_p203_6_33.png]

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