Cited by 1,386Explainable Artificial Intelligence (XAI)Software Testing and Debugging TechniquesAdversarial Robustness in Machine Learning

The DOI matches its Crossref record; 46 of the 53 references it cites were found in a registry or an index; none of the 49 sources its claims cite is checked for support yet.

Explainable AI (XAI): Core Ideas, Techniques, and Solutions

Open access

Dwivedi, Rudresh, Dave, Devam, Naik, Het, Singhal, Smiti, Omer, Rana, Patel, Pankesh, Qian, Bin, Wen, Zhenyu, Shah, Tejal, Morgan, Graham, Ranjan, Rajiv·2023·ACM Computing Surveys

DOI 10.1145/3561048

Open this paper in CoChat — free account Open PDF

Abstract

As our dependence on intelligent machines continues to grow, so does the demand for more transparent and interpretable models. In addition, the ability to explain the model generally is now the gold standard for building trust and deployment of artificial intelligence systems in critical domains. Explainable artificial intelligence (XAI) aims to provide a suite of machine learning techniques that enable human users to understand, appropriately trust, and produce more explainable models. Selecting an appropriate approach for building an XAI-enabled application requires a clear understanding of the core ideas within XAI and the associated programming frameworks. We survey state-of-the-art programming techniques for XAI and present the different phases of XAI in a typical machine learning development process. We classify the various XAI approaches and, using this taxonomy, discuss the key differences among the existing XAI techniques. Furthermore, concrete examples are used to describe these techniques that are mapped to programming frameworks and software toolkits. It is the intention that this survey will help stakeholders in selecting the appropriate approaches, programming frameworks, and software toolkits by comparing them through the lens of the presented taxonomy.

AI snapshot

Written from the paper’s full text.

TL;DR

This survey reviews explainable AI concepts, techniques, and software frameworks, organizing them by model transparency, explanation scope, and model specificity. It maps data, model, feature-based, and example-based explanation methods to practical libraries and toolkits, using illustrative examples.

Key findings

The paper proposes a taxonomy of XAI techniques along white-box vs. black-box, model-specific vs. model-agnostic, and global vs. local dimensions, covering data explainability, model explainability, feature-based methods, example-based methods, toolkits, and distributed learning.
It reports an illustrative bias example: a 2015 Google Images search for “CEO” returned women 11% of the time, compared with a real-world representation of 27%.
Neural-symbolic systems are presented as combining neural learning with symbolic reasoning; the paper cites a Neuro-Symbolic Concept Learner trained on 10% of the original dataset while remaining explainable.
It maps techniques such as LIME, SHAP, PDP, ICE, ALE, anchors, counterfactuals, and integrated gradients to frameworks including ELI5, SHAP, PDPbox, Skater, tf-explain, DiCE, Alibi, InterpretML, What-If Tool, and TensorBoard, while noting a trade-off between interpretability and model performance.

Methods

Design: a comparative survey of state-of-the-art XAI programming techniques, not a primary empirical study; it classifies approaches and compares them through a proposed taxonomy.
Data or sample: illustrative datasets and examples include Breast Cancer Wisconsin, Heart Disease, Iris (4 features and 3 target classes), Fashion MNIST (70,000 grayscale 28×28 images across 10 classes), and an animal price prediction example.
Main analysis: qualitative comparison tables mapping XAI techniques to software libraries and toolkits, plus discussion of ML development phases (understanding and explaining) and implementation design considerations.

Limitations

The paper does not state limitations of the survey itself. It does enumerate limitations of reviewed techniques: PDP assumes feature independence and is realistically limited to about two features; ICE can overcrowd plots and shares correlation concerns; LIME’s local linear approximation may fail with nonlinearity; SHAP can be computationally expensive; global surrogates explain the model rather than the data and may not explain individual predictions; counterfactuals perform worse for multiclass than binary classification.
Evident from the text: no benchmark, user study, or empirical validation of the taxonomy or toolkit comparisons is reported; the examples are illustrative rather than systematic evaluations.
Also evident: some tables and sections in the provided text are incomplete or formatting-corrupted, which limits reproducibility of the comparative claims.

Model-written summary. Check it against the paper before relying on it.

Integrity signals

Registry

Crossref · 10.1145/3561048

Cites

46 of 46 references verified0 citation errors · 0 not checkable

Statistics

Nothing to recompute.

Claims the paper makes

0 of 49 cited sources checked · 0 supported · 0 flagged

In the paper
“However, if these models are trained on biased historical data, then such bias can permeate the entire system negatively impacting the decision outcomes.”
§ Explainable AI (XAI): Core Ideas, Techniques and Solutions > Detecting bias.
What the sources say
- Not checked yetcites [33]
Load-bearingStrength:plain· not yet calibratedCausal:direct· not yet calibrated
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes
In the paper
“They can be used only for a single algorithm class.”
§ Explainable AI (XAI): Core Ideas, Techniques and Solutions > 3.2 Model-specific Techniques vs Model-agnostic Techniques
What the sources say
- Not checked yetcites [44]
Strength:plain· not yet calibrated
Tests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes
In the paper
“Model performance metrics (e.g., model accuracy) do not always exhibit true prediction decisions [9].”
§ Explainable AI (XAI): Core Ideas, Techniques and Solutions > 1 INTRODUCTION
What the sources say
- Not checked yetcites [9] source
Load-bearingStrength:plain· not yet calibrated
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes
In the paper
“With the frequent utilization of complex Deep Learning (DL) architectures, it requires urgent attention to understand the inner workings and to get insights into the outcomes.This is a core motivation of Explainable AI (XAI) [15].”
§ Explainable AI (XAI): Core Ideas, Techniques and Solutions > 1 INTRODUCTION
What the sources say
- Not checked yetcites [15] source
Load-bearingStrength:plain· not yet calibratedCausal:direct· not yet calibrated
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes
None of the sources cited for this claim have been checked yet.
In the paper
“However, the state-of-the-art analysis with respect to existing approaches for building XAI-enabled applications has been investigated to a limited extent.”
§ Explainable AI (XAI): Core Ideas, Techniques and Solutions > 1 INTRODUCTION
What the sources say
- Not checked yetcites Došilović et al. [13]
- Not checked yetcites Bhatt el al. [7]
- Not checked yetcites Arrieta et al. [4]
Load-bearingStrength:plain· not yet calibrated
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes
None of the sources cited for this claim have been checked yet.
In the paper
“More recently, a combination of symbolic and subsymbolic approaches based on artificial neural networks, variously known as neuro-symbolic or neural-symbolic (henceforth NS) systems, has shown significant promise in exploiting the complementary strengths of individual approaches [20,28].”
§ Explainable AI (XAI): Core Ideas, Techniques and Solutions > 5.3 Neural-Symbolic Approaches
What the sources say
- Not checked yetcites [20] source
- Not checked yetcites [28] source
Strength:plain· not yet calibratedCausal:correlational· not yet calibrated
Tests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes

Show all 48 claims

Probably not a substantial claim. 118
It may state two separate claims as one. 54

In the paper
“Global interpretation methods involve an overall analysis of a model and its general behavior.The process of defining variables, their dependency, and interactions goes alongside with the process of assigning importance to these components.”
§ Explainable AI (XAI): Core Ideas, Techniques and Solutions > Global Interpretation.
What the sources say
- Not checked yetcites [44]
Strength:plain· not yet calibratedCausal:correlational· not yet calibrated
Tests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passes
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“AutoML has made explainability more important, as the entire data pipeline is convered into a black box (as opposed to just the machine learning model) [27].When using AutoML, a user does not have the ability to engineer or select features, or has visibility into a model's decision-making process.”
§ Explainable AI (XAI): Core Ideas, Techniques and Solutions > Scientific understanding.
What the sources say
- Not checked yetcites [27]
Strength:plain· not yet calibratedCausal:direct· not yet calibrated
Tests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes
It may state two separate claims as one.
In the paper
“They are far more intuitive to understand when compared to PDP [44].”
§ Explainable AI (XAI): Core Ideas, Techniques and Solutions > 6.3 Individual Conditional Expectation (ICE)
What the sources say
- Not checked yetcites [44]
Strength:plain· not yet calibrated
Tests✓ Asserted: passes✗ Checkable: fails✗ Load-bearing: fails✓ Based: passes
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it.
In the paper
“In fact, deep understanding of cognitive science will most certainly benefit the field of NS-AI particularly for the integration of neuro-symbolic approaches [6] to develop a mature and robust integrated model and consequently, for the design of explainable systems that are closer to how humans understand, think, and communicate.”
§ Explainable AI (XAI): Core Ideas, Techniques and Solutions > --------→ Streptcoccus pyogenes
What the sources say
- Not checked yetcites [6]
Strength:boosted· not yet calibratedCausal:direct· not yet calibrated
Tests✓ Asserted: passes✗ Checkable: fails✗ Load-bearing: fails✓ Based: passes
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it.
In the paper
“Involves an analysis of individual predictions and decisions made by the model, to clarify why the model suggested a particular course of action.”
§ Explainable AI (XAI): Core Ideas, Techniques and Solutions > Local Interpretation.
What the sources say
- Not checked yetcites [44]
Strength:plain· not yet calibratedCausal:direct· not yet calibrated
Tests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passes
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“However, to achieve this explainability, the authors argue the critical need for reasoning to understand the why and how behind a particular outcome [14].”
§ Explainable AI (XAI): Core Ideas, Techniques and Solutions > 5.3 Neural-Symbolic Approaches
What the sources say
- Not checked yetcites [14]
Load-bearingStrength:boosted· not yet calibrated
Tests✓ Asserted: passes✗ Checkable: fails✓ Load-bearing: passes✓ Based: passes
Probably not a substantial claim. It may be too vague to check against evidence.
In the paper
“The ring-allreduce [10] usually achieves better scaleability due to efficient use of network bandwidth.”
§ Explainable AI (XAI): Core Ideas, Techniques and Solutions > 5.6 Distributed learning
What the sources say
- Not checked yetcites [10]
Strength:hedged· not yet calibratedCausal:direct· not yet calibrated
Tests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passes
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“In Asynchronous SGD, the delayed gradients create noise in the global model and delay the convergence speed.”
§ Explainable AI (XAI): Core Ideas, Techniques and Solutions > 5.6 Distributed learning
What the sources say
- Not checked yetcites [11]
Strength:plain· not yet calibratedCausal:direct· not yet calibrated
Tests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes
It may state two separate claims as one.
In the paper
“Ontologies enable reasoning not only to deduce new knowledge and facts but also for causal inference to understand the cause and effect behind the outcomes, such as through transitivity [23].”
§ Explainable AI (XAI): Core Ideas, Techniques and Solutions > 5.3 Neural-Symbolic Approaches
What the sources say
- Not checked yetcites [23]
Strength:plain· not yet calibratedCausal:direct· not yet calibrated
Tests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes
It may state two separate claims as one.
In the paper
“Parameter sever (PS) [37] is most prominent for centralized aggregation of data parallel training.”
§ Explainable AI (XAI): Core Ideas, Techniques and Solutions > 5.6 Distributed learning
What the sources say
- Not checked yetcites [37]
Strength:plain· not yet calibrated
Tests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passes
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Mao et al. [41] present a Neuro-Symbolic Concept Learner, which incorporates symbolic reasoning for better interpretation of visual concepts, and can be trained on less data (10% of the original dataset) while being explainable.”
§ Explainable AI (XAI): Core Ideas, Techniques and Solutions > 5.3 Neural-Symbolic Approaches
What the sources say
- Not checked yetcites [41]
Strength:plain· not yet calibratedCausal:direct· not yet calibrated
Tests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes
It may state two separate claims as one.
In the paper
“DiCE not only provides the most influential feature for the prediction but also recommends the feature modifications needed for the result.”
§ Explainable AI (XAI): Core Ideas, Techniques and Solutions > 7.6 Sotware Libraries for Example-based XAI Techniques
What the sources say
- Not checked yetcites [45]
Strength:plain· not yet calibratedCausal:direct· not yet calibrated
Tests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passes
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“DiCE allows input of relative difficulty by specifying 'feature weights'.A higher feature weight means that the feature is difficult to modify than that of others.”
§ Explainable AI (XAI): Core Ideas, Techniques and Solutions > Diverse Counterfactual Explanations (DiCE) for ML
What the sources say
- Not checked yetcites [45]
Strength:plain· not yet calibratedCausal:direct· not yet calibrated
Tests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passes
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“An important advantage of SHAP value lies in its transparency and interpretability locally, which implies that each observation can have its own SHAP value set.”
§ Explainable AI (XAI): Core Ideas, Techniques and Solutions > An example.
What the sources say
- Not checked yetcites [40]
Strength:plain· not yet calibratedCausal:direct· not yet calibrated
Tests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes
It may state two separate claims as one.
In the paper
“The library consists of a wide range of algorithms, most of which focus on black box models and local interpretation i.e. interpretation for the prediction to be explained at a particular instance.”
§ Explainable AI (XAI): Core Ideas, Techniques and Solutions > ALIBI.
What the sources say
- Not checked yetcites [34]
Strength:plain· not yet calibrated
Tests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passes
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“DiCE [45] is a package that gives the “what-if” explanations for the model prediction.”
§ Explainable AI (XAI): Core Ideas, Techniques and Solutions > 7.6 Sotware Libraries for Example-based XAI Techniques
What the sources say
- Not checked yetcites [45]
Strength:plain· not yet calibratedCausal:direct· not yet calibrated
Tests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passes
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“It implements counterfactual explanations that include perturbed features which in turn will lead to the required result.”
§ Explainable AI (XAI): Core Ideas, Techniques and Solutions > 7.6 Sotware Libraries for Example-based XAI Techniques
What the sources say
- Not checked yetcites [45]
Strength:plain· not yet calibratedCausal:direct· not yet calibrated
Tests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passes
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“This library comprises different types of explainers depending on data we are dealing with.”
§ Explainable AI (XAI): Core Ideas, Techniques and Solutions > ALIBI.
What the sources say
- Not checked yetcites [34]
Strength:plain· not yet calibrated
Tests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passes
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Alibi [34] is an open source Python library which aims at ML-based model inspection and interpretation.”
§ Explainable AI (XAI): Core Ideas, Techniques and Solutions > ALIBI.
What the sources say
- Not checked yetcites [34]
Strength:plain· not yet calibrated
Tests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passes
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Integrated Gradients, also known as Path-Integrated Gradients is an XAI technique that assign an importance value to each feature of the input using the gradients of the model output [57].”
§ Explainable AI (XAI): Core Ideas, Techniques and Solutions > 7.5 Integrated Gradients
What the sources say
- Not checked yetcites [57]
Strength:plain· not yet calibratedCausal:direct· not yet calibrated
Tests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passes
Probably not a substantial claim. The paper’s conclusions may not depend on it.

135 of 135

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

Each contradiction between two passages listed here was confirmed by an independent check.

Outline assigns software toolkits to the wrong section
Minor
The outline says the toolkits are covered in Section 9, whereas Section 9 is about design considerations; software toolkits are in Section 8.
Tree ensembles are classified as both white-box and black-box
Minor
Table 3 lists tree ensembles among white-box models, but Section 5.2 explicitly includes them among black-box models.
Table 5 is described as covering the wrong type of explainability
Minor
Section 5.5 says Table 5 concerns data explainability, but the table caption says it concerns model explainability.
Outline misnumbers the data and model explainability sections
Minor
The outline places data explainability in Section 5 and model explainability in Section 6, but their headings are Sections 4 and 5, respectively.
Intrinsic and post-hoc labels are reversed
Minor
Section 3.1 calls black-box models intrinsic and white-box models post-hoc. The conclusion instead describes white-box models as intrinsically explainable relative to black-box models.
Table 8 conflicts with the stated Skater capabilities
Minor
Table 8 marks Skater as unable to perform feature importance and partial dependence plots, but the Skater description expressly says it can perform both.
Low concave-point values receive opposite interpretations
Minor
The PDP discussion associates lower values of the same feature with a lower chance of malignancy, while the ICE discussion associates them with a higher malignant target value.
Outline gives the wrong conclusion section
Minor
The outline places the conclusion in Section 11, but the conclusion is headed Section 10.
Table 6 is assigned two different subjects
Minor
The text identifies Table 6 as a feature-importance example, while its caption identifies it as a table of distributed-learning frameworks.

9 of 9

Greedy function approximation: A gradient boosting machine
Friedman, J. H. (2001) · The Annals of Statistics · doi:10.1214/aos/1013203451 · Cited by 30,731
Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization
Selvaraju, R. R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., & Batra, D. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.74 · Cited by 24,513
Advances in neural information processing systems 7
(1996) · Computers & Mathematics with Applications · doi:10.1016/s0898-1221(96)90463-0 · Cited by 14,759
Advances in Neural Information Processing Systems 29
Teymur, O., Zygalakis, K., & Calderhead, B. (2016) · Cited by 13,881
From local explanations to global understanding with explainable AI for trees
Lundberg, S. M., Erion, G., Chen, H., DeGrave, A., Prutkin, J. M., Nair, B., Katz, R., Himmelfarb, J., Bansal, N., & Lee, S.-I. (2020) · Nature Machine Intelligence · doi:10.1038/s42256-019-0138-9 · Cited by 10,551
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
Barredo Arrieta, A., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., Garcia, S., Gil-Lopez, S., Molina, D., Benjamins, R., et al. (2020) · Information Fusion · doi:10.1016/j.inffus.2019.12.012 · Cited by 10,423
Axiomatic attribution for deep networks
Sundararajan, M., Taly, A., & Yan, Q. (2017) · arXiv · doi:10.48550/arxiv.1703.01365 · Cited by 4,087
Towards A Rigorous Science of Interpretable Machine Learning
Doshi-Velez, F., & Kim, B. (2017) · arXiv · doi:10.48550/arxiv.1702.08608 · Cited by 3,121
Learning Important Features Through Propagating Activation Differences
Shrikumar, A., Greenside, P., & Kundaje, A. (2017) · arXiv · doi:10.48550/arxiv.1704.02685 · Cited by 2,761
Adversarial machine learning at scale
Kurakin, A., Goodfellow, I., & Bengio, S. (2016) · arXiv · doi:10.48550/arxiv.1611.01236 · Cited by 2,325

Flagged first, then by citations

Show all 53 references

7 of the 53 references couldn’t be matched to a known paper.

Research works printed but not linked: 13

David Beckett, Tim Berners-Lee, Eric Prud’hommeaux, and Gavin Carothers. 2014. RDF 1.1 Turtle. World Wide Web Consortium 2014 (2014), 18–31.
Jeffrey Dean Greg S. Corrado Rajat Monga Kai Chen Matthieu Devin Quoc V. Le Mark Z. Mao et al. 2012. Large scale distributed deep networks. In Proceedings of the 25th International Conference on Neural Information Processing Systems (NIPS’12) . 1223–1231.
CEUR Workshop Proceedings Proceedings of the First International Workshop on Comprehensibility and Explanation in AI and ML 2017 co-located with 16th International Conference of the Italian Association for Artificial Intelligence (AI*IA 2017) Bari Italy November 16th and 17th 2017 2071 Derek Doran Sarah Schulz Tarek R. Besold Tarek R. Besold Oliver Kutz What does explainable AI really mean? A new conceptualization of perspectives 2017
Xin He Kaiyong Zhao and Xiaowen Chu. 2019. AutoML: A survey of the state-of-the-art. arxiv:1908.00709 [cs.LG]. · arXiv
Pascal Hitzler, Markus Krötzsch, Bijan Parsia, Peter F. Patel-Schneider, and Sebastian Rudolph. 2009. OWL 2 web ontology language primer. W3C Recommendation 27, 1 (2009), 123.
SNOMED International. 2022. Data Analytics with SNOMED CT. Retrieved June 29, 2022 from https://confluence.ihtsdotools.org/display/DOCANLYT/Data+Analytics+with+SNOMED+CT.
Accenture Labs. 2020. Understanding Machines: Explainable AI. Technical Report. Retrieved September 9, 2022 from https://www.accenture.com/_acnmedia/pdf-85/accenture-understanding-machines-explainable-ai.pdf.
Mu Li David G. Andersen Jun Woo Park Alexander J. Smola Amr Ahmed Vanja Josifovski James Long Eugene J. Shekita and Bor-Yiing Su. 2014. Scaling distributed machine learning with the parameter server. In Proceedings of the 11th USENIX Symposium on Operating Systems Design and Implementation (OSDI’14) . 583–598.
Joanne S. Luciano, Bosse Andersson, Colin Batchelor, Olivier Bodenreider, Tim Clark, Christine K. Denney, Christopher Domarew, et al. 2011. The Translational Medicine Ontology and Knowledge Base: Driving personalized medicine by bridging the gap between bench and bedside. Journal of Biomedical Semantics 2 (2011), 1–21.
Azalia Mirhoseini, Anna Goldie, Hieu Pham, Benoit Steiner, Quoc V. Le, and Jeff Dean. 2018. A hierarchical model for device placement. In Proceedings of the International Conference on Learning Representations.
Azalia Mirhoseini, Hieu Pham, Quoc V. Le, Benoit Steiner, Rasmus Larsen, Yuefeng Zhou, Naveen Kumar, Mohammad Norouzi, Samy Bengio, and Jeff Dean. 2017. Device placement optimization with reinforcement learning. In Proceedings of the International Conference on Machine Learning. 2430–2439.
CEUR Workshop Proceedings Proceedings of the Twelfth International Workshop on Neural-Symbolic Learning and Reasoning NeSy 2017 London UK July 17-18 2017 2003 Kamruzzaman Sarker Ning Xie Derek Doran Michael L. Raymer Pascal Hitzler Tarek R. Besold Artur S. d’Avila Garcez Isaac Noble Explaining trained neural networks with semantic web technologies: First steps 2017
Ajay Thampi. 2020. Interpretable AI, Building Explainable Machine Learning Systems. Manning Publications.

Sorted by citations

A comprehensive review of model compression techniques in machine learning
Dantas, P. V., da Silva, W. S., Cordeiro, L. C., & Carvalho, C. B. (2024) · Applied Intelligence · doi:10.1007/s10489-024-05747-w · Cited by 314
Unveiling the black box: A systematic review of Explainable Artificial Intelligence in medical image analysis
Muhammad, D., & Bendechache, M. (2024) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2024.08.005 · Cited by 325
Explainable artificial intelligence: A survey of needs, techniques, applications, and future direction
Mersha, M. A., Lâm, K. N., Wood, J., AlShami, A. K., & Kalita, J. K. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.128111 · Cited by 346
Black-Box Access is Insufficient for Rigorous AI Audits
Casper, S., Ezell, C., Siegmann, C., Kolt, N., Curtis, T. L., Bucknall, B., Haupt, A., Wei, K., Scheurer, J., Hobbhahn, M., et al. (2024) · The 2024 ACM Conference on Fairness, Accountability, and Transparency · doi:10.1145/3630106.3659037 · Cited by 86
A Survey of Explainable Artificial Intelligence in Healthcare: Concepts, Applications, and Challenges
Mienye, I. D., Obaido, G., Jere, N., Mienye, E., Aruleba, K., Emmanuel, I. D., & Ogbuokiri, B. (2024) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2024.101587 · Cited by 212
Post-hoc vs ante-hoc explanations: xAI design guidelines for data scientists
Retzlaff, C. O., Angerschmid, A., Saranti, A., Schneeberger, D. M., Röttger, R., Müller, H., & Holzinger, A. (2024) · Cognitive Systems Research · doi:10.1016/j.cogsys.2024.101243 · Cited by 229
Explainable AI for cybersecurity automation, intelligence and trustworthiness in digital twin: Methods, taxonomy, challenges and prospects
Sarker, I. H., Janicke, H., Mohsin, A., Gill, A. Q., & Maglaras, L. A. (2024) · ICT Express · doi:10.1016/j.icte.2024.05.007 · Cited by 227
Explainable and interpretable artificial intelligence in medicine: a systematic bibliometric review
Frasca, M., La Torre, D., Pravettoni, G., & Cutica, I. (2024) · Discover Artificial Intelligence · doi:10.1007/s44163-024-00114-7 · Cited by 190
How Interpretable Machine Learning Can Benefit Process Understanding in the Geosciences
Jiang, S., Sweet, L., Blougouras, G., Brenning, A., Li, W., Reichstein, M., Denzler, J., Shangguan, W., Yu, G., Huang, F., et al. (2024) · Earth s Future · doi:10.1029/2024ef004540 · Cited by 209
From explainable to interactive AI: A literature review on current trends in human-AI interaction
Raees, M., Meijerink, I., Lykourentzou, I., Khan, V.-J., & Papangelis, K. (2024) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2024.103301 · Cited by 180
Modern computing: Vision and challenges
Gill, S. S., Wu, H., Patros, P., Ottaviani, C., Arora, P., Pujol, V. C., Haunschild, D., Parlikad, A. K., Cetinkaya, O., Lutfiyya, H., et al. (2024) · Telematics and Informatics Reports · doi:10.1016/j.teler.2024.100116 · Cited by 197
Self-citation
A Primer on Generative Artificial Intelligence
Kalota, F. (2024) · Education Sciences · doi:10.3390/educsci14020172 · Cited by 225
Recent Emerging Techniques in Explainable Artificial Intelligence to Enhance the Interpretable and Understanding of AI Models for Human
Mathew, D. E., Ebem, D. U., Ikegwu, A. C., Ukeoma, P. E., & Dibiaezue, N. F. (2025) · Neural Processing Letters · doi:10.1007/s11063-025-11732-2 · Cited by 168
Explainable Generative AI (GenXAI): a survey, conceptualization, and research agenda
Schneider, J. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-10916-x · Cited by 214
Explainable AI for Safe and Trustworthy Autonomous Driving: A Systematic Review
Kuznietsov, A., Gyevnár, B., Wang, C., Peters, S., & Albrecht, S. V. (2024) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2024.3474469 · Cited by 206
XAIR: A Systematic Metareview of Explainable AI (XAI) Aligned to the Software Development Process
Clement, T., Kemmerzell, N., Abdelaal, M., & Amberg, M. (2023) · Machine Learning and Knowledge Extraction · doi:10.3390/make5010006 · Cited by 131
Avoiding common machine learning pitfalls
Lones, M. A. (2024) · Patterns · doi:10.1016/j.patter.2024.101046 · Cited by 298
XAI-IDS: Toward Proposing an Explainable Artificial Intelligence Framework for Enhancing Network Intrusion Detection Systems
Arreche, O., Guntur, T. R., & Abdallah, M. (2024) · Applied Sciences · doi:10.3390/app14104170 · Cited by 189
Artificial intelligence and explanation: How, why, and when to explain black boxes
Marcus, E., & Teuwen, J. (2024) · European Journal of Radiology · doi:10.1016/j.ejrad.2024.111393 · Cited by 114
Explainable AI for Medical Data: Current Methods, Limitations, and Future Directions
Hossain, M. I., Zamzmi, G., Mouton, P. R., Salekin, M. S., Sun, Y., & Goldgof, D. B. (2023) · ACM Computing Surveys · doi:10.1145/3637487 · Cited by 135
Streamlit-based enhancing crop recommendation systems with advanced explainable artificial intelligence for smart farming
Akkem, Y., Biswas, S. K., & Varanasi, A. (2024) · Neural Computing and Applications · doi:10.1007/s00521-024-10208-z · Cited by 108
XKT: Toward Explainable Knowledge Tracing Model With Cognitive Learning Theories for Questions of Multiple Knowledge Concepts
Huang, C., Huang, Q., Huang, X., Wang, H., Li, M., Lin, K.-J., & Chang, Y. (2024) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2024.3418098 · Cited by 116
Explainable AI improves task performance in human–AI collaboration
Senoner, J., Schallmoser, S., Kratzwald, B., Feuerriegel, S., & Netland, T. H. (2024) · Scientific Reports · doi:10.1038/s41598-024-82501-9 · Cited by 157
Advancing 6G: Survey for Explainable AI on Communications and Network Slicing
Sun, H., Liu, Y., Al-Tahmeesschi, A., Nag, A., Soleimanpour-Moghadam, M., Canberk, B., Arslan, H., & Ahmadi, H. (2025) · IEEE Open Journal of the Communications Society · doi:10.1109/ojcoms.2025.3534626 · Cited by 128
From Assistive Technologies to Metaverse—Technologies in Inclusive Higher Education for Students With Specific Learning Difficulties: A Review
Yenduri, G., Kaluri, R., Rajput, D. S., Lakshmanna, K., Gadekallu, T. R., Mahmud, M., & Brown, D. J. (2023) · IEEE Access · doi:10.1109/access.2023.3289496 · Cited by 135
Digital Post-Disaster Risk Management Twinning: A Review and Improved Conceptual Framework
Lagap, U., & Ghaffarian, S. (2024) · International Journal of Disaster Risk Reduction · doi:10.1016/j.ijdrr.2024.104629 · Cited by 82
GA-mADAM-IIoT: A new lightweight threats detection in the industrial IoT via genetic algorithm with attention mechanism and LSTM on multivariate time series sensor data
Saheed, Y. K., Omole, A. I., & Sabit, M. O. (2025) · Sensors International · doi:10.1016/j.sintl.2024.100297 · Cited by 88
Optimizing long-short-term memory models via metaheuristics for decomposition aided wind energy generation forecasting
Pavlov, M., Jovanović, L., Bačanin, N., Deveci, M., Zivkovic, M. Z., Tuba, M., Strumberger, I., & Witold Pedrycz, C. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-023-10678-y · Cited by 86
Transforming Agricultural Productivity with AI-Driven Forecasting: Innovations in Food Security and Supply Chain Optimization
Dhal, S. B., & Kar, D. (2024) · Forecasting · doi:10.3390/forecast6040046 · Cited by 96
Digital Twins for Condition and Fleet Monitoring of Aircraft: Toward More-Intelligent Electrified Aviation Systems
Sadeghi, A., Bellavista, P., Song, W., & Yazdani-Asrami, M. (2024) · IEEE Access · doi:10.1109/access.2024.3371902 · Cited by 90
The challenges of integrating explainable artificial intelligence into GeoAI
Jin, X., & Sieber, R. E. (2023) · Transactions in GIS · doi:10.1111/tgis.13045 · Cited by 74
Explainable AI-Based DDOS Attack Identification Method for IoT Networks
Kalutharage, C. S., Liu, X., Chrysoulas, C., Pitropakis, N., & Papadopoulos, P. (2023) · Computers · doi:10.3390/computers12020032 · Cited by 72
Explainable Artificial Intelligence methods for financial time series
Giudici, P., Piergallini, A., Recchioni, M. C., & Raffinetti, E. (2024) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2024.130176 · Cited by 80
AI in credit scoring: A comprehensive review of models and predictive analytics
Addy, W. A., Ajayi-Nifise, A. O., Bello, B. G., Tula, S. T., Odeyemi, O., & Falaiye, T. O. (2024) · Global Journal of Engineering and Technology Advances · doi:10.30574/gjeta.2024.18.2.0029 · Cited by 72
Explainable Artificial Intelligence in Neuroimaging of Alzheimer’s Disease
Taiyeb Khosroshahi, M., Morsali, S., Gharakhanlou, S., Motamedi, A., Hassanbaghlou, S., Vahedi, H., Pedrammehr, S., Kabir, H. M. D., & Jafarizadeh, A. (2025) · Diagnostics · doi:10.3390/diagnostics15050612 · Cited by 68
Toward Explainable Affective Computing: A Review
Cortiñas-Lorenzo, K., & Lacey, G. (2023) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2023.3270027 · Cited by 87
Explainable Artificial Intelligence: A Perspective on Drug Discovery
Qadri, Y. A., Shaikh, S., Ahmad, K., Choi, I., Kim, S. W., & Vasilakos, A. V. (2025) · Pharmaceutics · doi:10.3390/pharmaceutics17091119 · Cited by 83
A review of deep learning in metal additive manufacturing: Impact on process, structure, and properties
Du, Y., Mukherjee, T., Li, R., Hou, Z., Dutta, S., Arnold, C. B., Elwany, A. H., Kung, S., Tang, J., & DebRoy, T. (2025) · Progress in Materials Science · doi:10.1016/j.pmatsci.2025.101587 · Cited by 79
A review and benchmark of feature importance methods for neural networks
Mandler, H., & Weigand, B. (2024) · ACM Computing Surveys · doi:10.1145/3679012 · Cited by 65
The future of AI-driven software engineering
Terragni, V., Vella, A., Roop, P., & Blincoe, K. (2025) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3715003 · Cited by 62
DFU-SIAM a Novel Diabetic Foot Ulcer Classification With Deep Learning
Toofanee, M. S. A., Dowlut, S., Hamroun, M., Tamine, K., Petit, V., Duong, A. K., & Sauveron, D. (2023) · IEEE Access · doi:10.1109/access.2023.3312531 · Cited by 66
Toward Federated Large Language Models: Motivations, Methods, and Future Directions
Cheng, Y., Zhang, W., Zhang, Z., Zhang, C., Wang, S., & Mao, S. (2024) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2024.3503680 · Cited by 79
Can we trust explainable artificial intelligence in wind power forecasting?
Liao, W., Fang, J., Ye, L., Bak‐Jensen, B., Yang, Z., & Porté‐Agel, F. (2024) · Applied Energy · doi:10.1016/j.apenergy.2024.124273 · Cited by 60
CPS-IIoT-P2Attention: Explainable Privacy-Preserving With Scaled Dot-Product Attention in Cyber-Physical System-Industrial IoT Network
Saheed, Y. K., & Chukwuere, J. E. (2025) · IEEE Access · doi:10.1109/access.2025.3566980 · Cited by 54
Leveraging XGBoost and explainable AI for accurate prediction of type 2 diabetes
Rafie, Z., Talab, M. S., Koor, B. E., Garavand, A., Salehnasab, C., & Ghaderzadeh, M. (2025) · BMC Public Health · doi:10.1186/s12889-025-24953-w · Cited by 57
Explainable AI-Based Intrusion Detection Systems for Industry 5.0 and Adversarial XAI: A Systematic Review
Khan, N., Ahmad, K., Tamimi, A. A., Alani, M. M., Bermak, A., & Khalil, I. M. (2025) · Information · doi:10.3390/info16121036 · Cited by 121
Explainable and Data-Efficient Deep Learning for Enhanced Attack Detection in IIoT Ecosystem
Attique, D., Wang, H., Wang, P., et al. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3384374 · Cited by 55
Application of Example-Based Explainable Artificial Intelligence (XAI) for Analysis and Interpretation of Medical Imaging: A Systematic Review
Fontes, M., de Almeida, J. D. S., & Cunha, A. M. T. D. S. (2024) · IEEE Access · doi:10.1109/access.2024.3367606 · Cited by 57
Exploring the Unseen: A Survey of Multi-Sensor Fusion and the Role of Explainable AI (XAI) in Autonomous Vehicles
Yeong, D. J., Panduru, K., & Walsh, J. (2025) · Sensors · doi:10.3390/s25030856 · Cited by 87
The role of generative artificial intelligence in digital agri-food
Shahriar, S., Corradini, M. G., Sharif, S., Moussa, M. A., & Dara, R. A. (2025) · Journal of Agriculture and Food Research · doi:10.1016/j.jafr.2025.101787 · Cited by 72
Harnessing human and machine intelligence for planetary-level climate action
Debnath, R., Creutzig, F., Sovacool, B. K., & Shuckburgh, E. F. (2023) · npj Climate Action · doi:10.1038/s44168-023-00056-3 · Cited by 57
A survey of transformer networks for time series forecasting
Zhao, J., Chu, F., Xie, L., Che, Y., Wu, Y., & Burke, A. F. (2025) · Computer Science Review · doi:10.1016/j.cosrev.2025.100883 · Cited by 71
Explainable AI framework for reliable and transparent automated energy management in buildings
Teixeira, B., Carvalhais, L., Pinto, T. D. O., & Vale, Z. (2025) · Energy and Buildings · doi:10.1016/j.enbuild.2025.116246 · Cited by 77
Explainable Artificial Intelligence (XAI) for Malware Analysis: A Survey of Techniques, Applications, and Open Challenges
Manthena, H., Shajarian, S., Kimmell, J. C., Abdelsalam, M., Khorsandroo, S., & Gupta, M. (2025) · IEEE Access · doi:10.1109/access.2025.3555926 · Cited by 88
Predictive modeling of climate change impacts using Artificial Intelligence: a review for equitable governance and sustainable outcome
Ukoba, K. O., Onisuru, O. R., Jen, T., Madyira, D. M., & Olatunji, K. O. (2025) · Environmental Science and Pollution Research · doi:10.1007/s11356-025-36356-w · Cited by 64
Feature Models
Felfernig, A., Falkner, A. A., & Benavides, D. (2024) · SpringerBriefs in computer science · doi:10.1007/978-3-031-61874-1 · Cited by 47
Improving Explainability and Integrability of Medical AI to Promote Health Care Professional Acceptance and Use: Mixed Systematic Review
Liu, Y., Liu, C., Zheng, J., Xu, C., & Wang, D. (2025) · Journal of Medical Internet Research · doi:10.2196/73374 · Cited by 72
Fuzzy Cognitive Maps: Their Role in Explainable Artificial Intelligence
Apostolopoulos, I. D., & Groumpos, P. P. (2023) · Applied Sciences · doi:10.3390/app13063412 · Cited by 52
Identifying the Public's Beliefs About Generative Artificial Intelligence: A Big Data Approach
Mahmoud, A. B., Kumar, V., & Spyropoulou, S. (2025) · IEEE Transactions on Engineering Management · doi:10.1109/tem.2025.3534088 · Cited by 46
On Evaluating Black-Box Explainable AI Methods for Enhancing Anomaly Detection in Autonomous Driving Systems
Nazat, S., Arreche, O., & Abdallah, M. (2024) · Sensors · doi:10.3390/s24113515 · Cited by 55
Insights into the application of explainable artificial intelligence for biological wastewater treatment plants: Updates and perspectives
Sheik, A. G., Kumar, A., Srungavarapu, C. S., Azari, M., Ambati, S. R., Bux, F., & Patan, A. K. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.110132 · Cited by 47
Exploring the roles of large language models in reshaping transportation systems: A survey, framework, and roadmap
Nie, T., Sun, J., & Ma, W. (2025) · Artificial Intelligence for Transportation · doi:10.1016/j.ait.2025.100003 · Cited by 70
Explainable AI in agriculture: review of applications, methodologies, and future directions
Pai, D. G., Balachandra, M., & Kamath, R. (2025) · Engineering Research Express · doi:10.1088/2631-8695/ae086a · Cited by 55
Emerging Technologies for Sustainable Agriculture: The Power of Humans and the Way Ahead
Louta, M. D., Banti, K., & Karampelia, I. (2024) · IEEE Access · doi:10.1109/access.2024.3428401 · Cited by 43
Explainable AI Frameworks: Navigating the Present Challenges and Unveiling Innovative Applications
Sharma, N. A., Chand, R. R., Buksh, Z., Ali, A. B. M. S., Hanif, A., & Beheshti, A. (2024) · Algorithms · doi:10.3390/a17060227 · Cited by 51
Explanation, semantics, and ontology
Guizzardi, G., & Guarino, N. (2024) · Data & Knowledge Engineering · doi:10.1016/j.datak.2024.102325 · Cited by 76
The performance-interpretability trade-off: a comparative study of machine learning models
Assis, A., Dantas, J. R., & Andrade, E. (2024) · Journal of Reliable Intelligent Environments · doi:10.1007/s40860-024-00240-0 · Cited by 62
Fault prognosis of Li-ion batteries in electric vehicles: Recent progress, challenges and prospects
Li, H., Kaleem, M. B., Liu, K., Wu, Y., Liu, W., & Peng, Q. (2025) · Journal of Energy Storage · doi:10.1016/j.est.2025.116002 · Cited by 48
The role of explainable AI in enhancing breast cancer diagnosis using machine learning and deep learning models
Ansari, Z. A., Tripathi, M. M., & Ahmed, R. (2025) · Discover Artificial Intelligence · doi:10.1007/s44163-025-00307-8 · Cited by 53
Physically Based and Data-Driven Models for Landslide Susceptibility Assessment: Principles, Applications, and Challenges
Ye, C., Wu, H., Oguchi, T., Tang, Y.-T., Pei, X.-J., & Wu, Y. (2025) · Remote Sensing · doi:10.3390/rs17132280 · Cited by 46
transformative potential of Generative Artificial Intelligence (GenAI) in business
Caño-Marín, E. (2024) · ESIC MARKET Economic and Business Journal · doi:10.7200/esicm.55.333 · Cited by 53
Hazard Susceptibility Mapping with Machine and Deep Learning: A Literature Review
Viloria, A. D. J. P., Folini, A., Carrión, D., & Brovelli, M. A. (2024) · Remote Sensing · doi:10.3390/rs16183374 · Cited by 42
Explainable artificial intelligence to improve the resilience of perishable product supply chains by leveraging customer characteristics
Jauhar, S. K., Harinath, S., Krishnaswamy, V., & Paul, S. K. (2024) · Annals of Operations Research · doi:10.1007/s10479-024-06348-z · Cited by 58
Reliable Autism Spectrum Disorder Diagnosis for Pediatrics Using Machine Learning and Explainable AI
Jeon, I., Kim, M., So, D., Kim, E. Y., Nam, Y., Kim, S., Shim, S.-H., Kim, J., & Moon, J. (2024) · Diagnostics · doi:10.3390/diagnostics14222504 · Cited by 40
Decoding China's new-type industrialization: Insights from an XGBoost-SHAP analysis
Lai, Y., Wan, G., & Qin, X. (2024) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2024.143927 · Cited by 43
Towards Cognitive AI Systems: Workload and Characterization of Neuro-Symbolic AI
Wan, Z., Liu, C.-K., Yang, H., Raj, R., Li, C., You, H., Fu, Y., Wan, C., Samajdar, A., Lin, Y. C., et al. (2024) · 2024 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS) · doi:10.1109/ispass61541.2024.00033 · Cited by 39
Using artificial intelligence in sustainability teaching and learning
Filho, W. L., Kim, E., Borsatto, J. M. L. S., & Marcolin, C. B. (2025) · Environmental Sciences Europe · doi:10.1186/s12302-025-01159-w · Cited by 70
Holding AI-Based Systems Accountable in the Public Sector: A Systematic Review
Yuan, Q., & Chen, T. (2025) · Public Performance & Management Review · doi:10.1080/15309576.2025.2469784 · Cited by 50
Machine learning optimized adsorption heat transformations from adsorbents screening to enhanced performances
Han, B., & Chakraborty, A. (2025) · Energy Conversion and Management · doi:10.1016/j.enconman.2025.120272 · Cited by 39
Innovations in Education
Fatima, S., Reddy, C. K. K., Sunerah, A., & Doss, S. (2024) · Advances in educational technologies and instructional design book series · doi:10.4018/979-8-3693-8151-9.ch002 · Cited by 37
Integrating permutation feature importance with conformal prediction for robust Explainable Artificial Intelligence in predictive process monitoring
Mehdiyev, N., Majlatow, M., & Fettke, P. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.110363 · Cited by 43
Ethical Challenges in the Integration of Artificial Intelligence in Palliative Care
Adegbesan, A., Akingbola, A., Ojo, O., Jessica, O. U., Alao, U. H., Shagaya, U. N., Adewole, O. A., & Abdullahi, O. (2024) · Journal of Medicine Surgery and Public Health · doi:10.1016/j.glmedi.2024.100158 · Cited by 46
Recent advances in wind turbine condition monitoring using SCADA data: A state-of-the-art review
Wang, S., Vidal, Y., & Pozo, F. (2025) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2025.111838 · Cited by 52
An Explainable CNN and Vision Transformer-Based Approach for Real-Time Food Recognition
Nfor, K. A., Armand, T. P. T., Ismaylovna, K. P., Joo, M.-I., & Kim, H. (2025) · Nutrients · doi:10.3390/nu17020362 · Cited by 38
Explainable Artificial Intelligence: Importance, Use Domains, Stages, Output Shapes, and Challenges
Ullah, N., Khan, J. A., Falco, I. D., & Sannino, G. (2024) · ACM Computing Surveys · doi:10.1145/3705724 · Cited by 45
Explanations Considered Harmful: The Impact of Misleading Explanations on Accuracy in Hybrid Human-AI Decision Making
Cabitza, F., Fregosi, C., Campagner, A., & Natali, C. (2024) · Communications in computer and information science · doi:10.1007/978-3-031-63803-9_14 · Cited by 41
Explainable AI for Text Classification: Lessons from a Comprehensive Evaluation of Post Hoc Methods
Cesarini, M., Malandri, L., Pallucchini, F., Seveso, A., & Xing, F. (2024) · Cognitive Computation · doi:10.1007/s12559-024-10325-w · Cited by 37
Please Tell Me More: Privacy Impact of Explainability through the Lens of Membership Inference Attack
Liu, H., Wu, Y., Yu, Z., & Zhang, N. (2024) · IEEE Symposium on Security and Privacy (S&P) · doi:10.1109/sp54263.2024.00120 · Cited by 46
Enhancing advanced driver assistance systems through explainable artificial intelligence for driver anomaly detection
Chengula, T. J., Mwakalonge, J., Comert, G., Sulle, M., Siuhi, S., & Osei, E. (2024) · Machine Learning with Applications · doi:10.1016/j.mlwa.2024.100580 · Cited by 35
A cognitive approach to human–AI complementarity in dynamic decision-making
González, C., & Heidari, H. (2025) · Nature Reviews Psychology · doi:10.1038/s44159-025-00499-x · Cited by 73
Overview: Machine Learning for Segmentation and Classification of Complex Steel Microstructures
Müller, M. J., Stiefel, M., Bachmann, B.-I., Britz, D., & Mücklich, F. (2024) · Metals · doi:10.3390/met14050553 · Cited by 44
Optimizing brain tumor classification with hybrid CNN architecture: Balancing accuracy and efficiency through oneAPI optimization
Ramakrishnan, A. B., Srıdevı, M., Vasudevan, S. K., et al. (2023) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2023.101436 · Cited by 33
Concept-based Explainable Artificial Intelligence: A Survey
Poeta, E., Ciravegna, G., Pastor, E., Cerquitelli, T., & Baralis, E. (2025) · ACM Computing Surveys · doi:10.1145/3774643 · Cited by 36
Understanding the development of public data ecosystems: From a conceptual model to a six-generation model of the evolution of public data ecosystems
Lněnička, M., Nikiforova, A., Luterek, M., Milić, P., Rudmark, D., Neumaier, S., Kević, K., Zuiderwijk, A., & Bolívar, M. P. R. (2024) · Telematics and Informatics · doi:10.1016/j.tele.2024.102190 · Cited by 43
An interpretable machine learning framework for enhancing road transportation safety
Abdulrashid, I., Chiang, W., Sheu, J., et al. (2025) · Transportation Research Part E Logistics and Transportation Review · doi:10.1016/j.tre.2025.103969 · Cited by 31
Recent Advances in Deep Learning for Channel Coding: A Survey
Matsumine, T., & Ochiai, H. (2024) · IEEE Open Journal of the Communications Society · doi:10.1109/ojcoms.2024.3472094 · Cited by 39
Machine Learning for Advanced Emission Monitoring and Reduction Strategies in Fossil Fuel Power Plants
Zuo, Z., Niu, Y., Li, J., Fu, H., & Zhou, M. (2024) · Applied Sciences · doi:10.3390/app14188442 · Cited by 31
Why should I trust you? Influence of explanation design on consumer behavior in AI-based services
Nizette, F., Hammedi, W., van Riel, A. C. R., & Steils, N. (2024) · Journal of service management · doi:10.1108/josm-05-2024-0223 · Cited by 36
Acoustic Features Analysis for Explainable Machine Learning-Based Audio Spoofing Detection
Bisogni, C., Loia, V., Nappi, M., & Pero, C. (2024) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2024.104145 · Cited by 30
Artificial intelligence for diagnosis of mild–moderate COVID-19 using haematological markers
Chadaga, K., Prabhu, S., Vivekananda Bhat, K., Sampathila, N., Umakanth, S., & Chadaga, R. (2023) · Annals of Medicine · doi:10.1080/07853890.2023.2233541 · Cited by 31

Authors: Dwivedi, R., Dave, D., Naik, H., Singhal, S., Omer, R., Patel, P., Qian, B., Wen, Z., Shah, T., Morgan, G., & Ranjan, R.
Affiliations: Netaji Subhas University of Technology (IN)
Pandit Deendayal Energy University (IN)
Cardiff University, Cardiff, UK
University of South Carolina (US)
Newcastle University (GB)
ORCID: Dwivedi, Rudresh
Dave, Devam
Naik, Het
Singhal, Smiti
Omer, Rana
Patel, Pankesh
Qian, Bin
Wen, Zhenyu
Shah, Tejal
Morgan, Graham
Ranjan, Rajiv
Venue: ACM Computing Surveys
Publisher: Association for Computing Machinery (ACM)
Published: 2023-01-16
Volume: 55
Issue: 9
Pages: 1-33
ISSN: 0360-0300 (ISSN-L)
1557-7341
DOI: 10.1145/3561048
OpenAlex: W4294559022
Semantic Scholar: 3b8abd466697998c6e17df2cd30f48a7594d795b
Cited by: 1,386
References: 53
Citations by year: 2026: 479 · 2025: 554 · 2024: 284 · 2023: 68
FWCI: 74.65
Type: journal article
Language: en
Open access: green
orca.cardiff.ac.uk
Licence: www.acm.org/publications/policies/copyright_policy#Background (vor)
Locations: ACM Computing Surveys (publishedVersion)
ORCA Online Research @Cardiff (Cardiff University) (submittedVersion)
Topics: Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
Physical Sciences › Computer Science › Software › Software Testing and Debugging Techniques
Physical Sciences › Computer Science › Artificial Intelligence › Adversarial Robustness in Machine Learning
Keywords: explainable AI, model interpretability, trust in AI

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

A language model drew the claims from the paper on 2026-10-06, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).

A second model labelled each claim on 2026-10-06 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Explainable AI (XAI): Core Ideas, Techniques, and Solutions

Abstract

Overview

AI snapshot

TL;DR

Key findings

Methods

Limitations

Integrity signals

Verification

Claims the paper makes

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks