Toward Trustworthy Artificial Intelligence (TAI) in the Context of Explainability and Robustness
Abstract
From the innovation, Artificial Intelligence (AI) materialized as one of the noticeable research areas in various technologies and has almost expanded into every aspect of modern human life. However, nowadays, the development of AI is unpredictable with the stated values of those developing them; hence, the risk of misbehaving AI increases continuously. Therefore, there are uncertainties about endorsing that the development and deploying of AI are favorable and not unfavorable to humankind. In addition, AI holds a black-box pattern, which results in a lack of understanding of how systems can work based on the raised concerns. From the above discussion, trustworthy AI is vital for the extensive adoption of AI in many applications, with strong attention to humankind and the need to focus on AI systems developing into the system outline at the time of system design. In this survey, we discuss compound materials on trustworthy AI and the present state-of-the-art of trustworthy AI technologies, revealing new perspectives, bridging knowledge gaps, and paving the way for potential advances of robustness, and explainability rules which play a proactive role in designing AI systems. Systems that are reliable and secure and mimic human behavior significantly impact the technological AI ecosystem. We provide various contemporary technologies to build explainability and robustness for AI-based solutions, so AI works in a safer and more trustworthy manner. Finally, we conclude our survey article with high-end opportunities, challenges, and future research directions for trustworthy AI to investigate in the future.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Current XAI research predominantly focuses on ex-ante trust formation or calibration.
“However, a limitation in current XAI research is its predominant focus on ex-ante trust formation or calibration.”
From Explainability in AI: Comparing Human-Like and System-Like Trust Repair Strategies (2026) · cites this paper as Chander et al., 2025
Technical explanations might be perceived as irrelevant or even obstructive if they do not directly facilitate problem resolution or provide the reassurance users seek after interaction errors.
“In such scenarios, technical explanations might be perceived as irrelevant or even obstructive if they do not directly facilitate problem resolution or provide the reassurance users seek after interaction errors (Ali et al., 2023;Chander et al., 2025).”
From Explainability in AI: Comparing Human-Like and System-Like Trust Repair Strategies (2026) · cites this paper as Chander et al., 2025
Cites
The Nature of Statistical Learning Theory
Vapnik, V. (2000) · Statistics for Engineering and Information Science · doi:10.1007/978-1-4757-3264-1 · Cited by 46,387 · see citing works
Hosmer, D. W., & Lemeshow, S. (2000) · doi:10.1002/0471722146 · Cited by 20,302 · see citing works
Visualizing and understanding convolutional networks
Zeiler, M. D., & Fergus, R. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10590-1_53 · Cited by 15,839 · see citing works
Proceedings of IEEE Conference on Computer Vision and Pattern Recognition
(1993) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.1993.341010 · Cited by 14,474 · see citing works
Rudin, C. (2019) · Nature Machine Intelligence · doi:10.1038/s42256-019-0048-x · Cited by 10,561 · see citing works
Towards Evaluating the Robustness of Neural Networks
Carlini, N., & Wagner, D. (2017) · 2017 IEEE Symposium on Security and Privacy (SP) · doi:10.1109/sp.2017.49 · Cited by 8,859 · see citing works
Intriguing properties of neural networks
Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., & Fergus, R. (2013) · arXiv · doi:10.48550/arxiv.1312.6199 · Cited by 8,143 · see citing works
Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization
Selvaraju, R. R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., & Batra, D. (2020) · International Journal of Computer Vision · doi:10.1007/s11263-019-01228-7 · Cited by 6,909 · see citing works
Explanation in artificial intelligence: Insights from the social sciences
Miller, T. (2019) · Artificial Intelligence · doi:10.1016/j.artint.2018.07.007 · Cited by 5,302 · see citing works
CNN Features Off-the-Shelf: An Astounding Baseline for Recognition
Razavian, A. S., Azizpour, H., Sullivan, J., & Carlsson, S. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops · doi:10.1109/cvprw.2014.131 · Cited by 4,451 · see citing works
Distillation as a Defense to Adversarial Perturbations Against Deep Neural Networks
Papernot, N., McDaniel, P. D., Wu, X., Jha, S., & Swami, A. (2016) · IEEE Symposium on Security and Privacy (S&P) · doi:10.1109/sp.2016.41 · Cited by 3,123 · see citing works
The future of digital health with federated learning
Rieke, N., Hancox, J., Li, W., Milletarì, F., Roth, H. R., Albarqouni, S., Bakas, S., Galtier, M. N., Landman, B. A., Maier-Hein, K., et al. (2020) · npj Digital Medicine · doi:10.1038/s41746-020-00323-1 · Cited by 3,119 · cited 1× · see citing works
“The robustness of the technical methods, transparency, and fairness are all taken into account in these scenarios [145].”
Methods for interpreting and understanding deep neural networks
Montavon, G., Samek, W., & Müller, K.-R. (2018) · Digital Signal Processing · doi:10.1016/j.dsp.2017.10.011 · Cited by 2,665 · see citing works
Explaining Explanations: An Overview of Interpretability of Machine Learning
Gilpin, L. H., Bau, D., Yuan, B. Z., Bajwa, A., Specter, M., & Kagal, L. (2018) · 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA) · doi:10.1109/dsaa.2018.00018 · Cited by 2,507 · see citing works
Definitions, methods, and applications in interpretable machine learning
Murdoch, W. J., Singh, C., Kumbier, K., Abbasi-Asl, R., & Yu, B. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1900654116 · Cited by 2,167 · see citing works
Anchors: High-Precision Model-Agnostic Explanations
Ribeiro, M. T., Singh, S., & Guestrin, C. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11491 · Cited by 2,162 · cited 3× · see citing works
“…of counterfactual explanations always keeps their efficiency at stake and there have many approaches followed for surpassing it, Ustun et al. [47] have proposed an integer programming approach for generating actionable recourses, and a gradient-based approach was used for searching for the…”
BackgroundMethodResultXAI—Explainable artificial intelligence
Gunning, D., Stefik, M., Choi, J., Miller, T., Stumpf, S., & Yang, G.-Z. (2019) · Science Robotics · doi:10.1126/scirobotics.aay7120 · Cited by 2,053 · see citing works
Machine Learning Interpretability: A Survey on Methods and Metrics
Carvalho, D. V., Pereira, E. M., & Cardoso, J. S. (2019) · Electronics · doi:10.3390/electronics8080832 · Cited by 1,842 · see citing works
Ali, S., Abuhmed, T., El-Sappagh, S., Muhammad, K., Alonso-Moral, J. M., Confalonieri, R., Guidotti, R., Del Ser, J., Díaz-Rodríguez, N., & Herrera, F. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.101805 · Cited by 1,762 · see citing works
Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks
Katz, G., Barrett, C., Dill, D. L., Julian, K., & Kochenderfer, M. J. (2017) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-63387-9_5 · Cited by 1,706 · see citing works
Artificial intelligence in drug discovery and development
Paul, D., Sanap, G., Shenoy, S., Kalyane, D., Kalia, K., & Tekade, R. K. (2021) · Drug Discovery Today · doi:10.1016/j.drudis.2020.10.010 · Cited by 1,695 · see citing works
That BLUP is a Good Thing: The Estimation of Random Effects
Robinson, G. K. (1991) · Statistical Science · doi:10.1214/ss/1177011926 · Cited by 1,694 · see citing works
Score-CAM: Score-Weighted Visual Explanations for Convolutional Neural Networks
Wang, H., Wang, Z., Du, M., Yang, F., Zhang, Z., Ding, S., Mardziel, P., & Hu, X. (2020) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw50498.2020.00020 · Cited by 1,553 · see citing works
Network Dissection: Quantifying Interpretability of Deep Visual Representations
Bau, D., Zhou, B., Khosla, A., Oliva, A., & Torralba, A. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.354 · Cited by 1,389 · see citing works
Explaining machine learning classifiers through diverse counterfactual explanations
Mothilal, R. K., Sharma, A., & Tan, C. (2020) · Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency · doi:10.1145/3351095.3372850 · Cited by 1,233 · see citing works
Explainable artificial intelligence: A survey
Dosilovic, F. K., Brcic, M., & Hlupic, N. (2018) · 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO) · doi:10.23919/mipro.2018.8400040 · Cited by 1,147 · see citing works
Explainable artificial intelligence: an analytical review
Angelov, P. P., Soares, E. A., Jiang, R., Arnold, N. I., & Atkinson, P. M. (2021) · WIREs Data Mining and Knowledge Discovery · doi:10.1002/widm.1424 · Cited by 843 · see citing works
Trustworthy AI: From Principles to Practices
Li, B., Qi, P., Liu, B., Di, S., Liu, J., Pei, J., Yi, J., & Zhou, B. (2023) · ACM Computing Surveys · doi:10.1145/3555803 · Cited by 612 · cited 3× · see citing works
“Proving malicious data into an AI system during training and upda ting may alter the system‟s behavior.”
Trustworthy artificial intelligence
Thiebes, S., Lins, S., & Sunyaev, A. (2021) · Electronic Markets · doi:10.1007/s12525-020-00441-4 · Cited by 595 · see citing works
Eigen-CAM: Class Activation Map using Principal Components
Muhammad, M. B., & Yeasin, M. (2020) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn48605.2020.9206626 · Cited by 584 · see citing works
Artificial intelligence applications in supply chain management
Pournader, M., Ghaderi, ., Hassanzadegan, A., & Fahimnia, B. (2021) · International Journal of Production Economics · doi:10.1016/j.ijpe.2021.108250 · Cited by 583 · see citing works
Advances, challenges and opportunities in creating data for trustworthy AI
Liang, W., Tadesse, G. A., Ho, D. E., Fei-Fei, L., Zaharia, M., Zhang, C., & Zou, J. (2022) · Nature Machine Intelligence · doi:10.1038/s42256-022-00516-1 · Cited by 582 · see citing works
Actionable Recourse in Linear Classification
Ustun, B., Spangher, A., & Liu, Y. (2019) · Proceedings of the Conference on Fairness, Accountability, and Transparency · doi:10.1145/3287560.3287566 · Cited by 501 · see citing works
Recent Advances in Adversarial Training for Adversarial Robustness
Bai, T., Luo, J., Zhao, J., Wen, B., & Wang, Q. (2021) · Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2021/591 · Cited by 490 · see citing works
Recent Advances in Adversarial Training for Adversarial Robustness
Bai, T., Luo, J., Zhao, J., Wen, B., & Wang, Q. (2021) · arXiv · doi:10.48550/arxiv.2102.01356 · Cited by 490 · cited 2× · see citing works
“In the context of adaptability, robustness implies that the AI system can learn from new data and adjust its algorithms to keep up with changing market conditions [115].”
BackgroundLayer-Wise Relevance Propagation for Neural Networks with Local Renormalization Layers
Binder, A., Montavon, G., Lapuschkin, S., Müller, K., & Samek, W. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-44781-0_8 · Cited by 455 · see citing works
Alozie, E. (2015) · BENTHAM SCIENCE PUBLISHERS eBooks · doi:10.2174/9781608059768115010014 · Cited by 427 · see citing works
Sayres, R., Taly, A., Rahimy, E., Blumer, K., Coz, D., Hammel, N., Krause, J., Narayanaswamy, A., Rastegar, Z., Wu, D., et al. (2019) · Ophthalmology · doi:10.1016/j.ophtha.2018.11.016 · Cited by 418 · see citing works
A Review of Taxonomies of Explainable Artificial Intelligence (XAI) Methods
Speith, T. (2022) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3531146.3534639 · Cited by 377 · see citing works
Transfer Learning for Drug Discovery
Cai, C., Wang, S., Xu, Y., Zhang, W., Tang, K., Ouyang, Q., Lai, L., & Pei, J. (2020) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.9b02147 · Cited by 364 · see citing works
Deep learning in drug discovery: opportunities, challenges and future prospects
Lavecchia, A. (2019) · Drug Discovery Today · doi:10.1016/j.drudis.2019.07.006 · Cited by 353 · see citing works
Mahbooba, B., Timilsina, M., Sahal, R., & Serrano, M. (2021) · Complexity · doi:10.1155/2021/6634811 · Cited by 333 · see citing works
Cyber security meets artificial intelligence: a survey
Li, J. (2018) · Frontiers of Information Technology & Electronic Engineering · doi:10.1631/fitee.1800573 · Cited by 331 · see citing works
The impact of artificial intelligence on human society and bioethics
Tai, M. C.-T. (2020) · Tzu Chi Medical Journal · doi:10.4103/tcmj.tcmj_71_20 · Cited by 326 · see citing works
FACE: Feasible and Actionable Counterfactual Explanations
Poyiadzi, R., Sokol, K., Santos-Rodriguez, R., De Bie, T., & Flach, P. (2020) · Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society · doi:10.1145/3375627.3375850 · Cited by 317 · see citing works
Multi-Objective Counterfactual Explanations
Dandl, S., Molnar, C., Binder, M., & Bischl, B. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58112-1_31 · Cited by 271 · see citing works
Pathologies of Neural Models Make Interpretations Difficult
Feng, S., Wallace, E., Grissom, A., Iyyer, M., Rodríguez, P., & Boyd-Graber, J. L. (2018) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d18-1407 · Cited by 260 · see citing works
Prototype selection for interpretable classification
Bien, J., & Tibshirani, R. J. (2011) · The Annals of Applied Statistics · doi:10.1214/11-aoas495 · Cited by 252 · see citing works
Neural Additive Models: Interpretable Machine Learning with Neural NetsPreprint
Agarwal, R., Melnick, L., Frosst, N., Zhang, X., Lengerich, B. J., Caruana, R., & Hinton, G. E. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2004.13912 · Cited by 246 · see citing works
Dhieb, N., Ghazzai, H., Besbes, H., & Massoud, Y. (2020) · IEEE Access · doi:10.1109/access.2020.2983300 · Cited by 206 · see citing works
A Review on Explainability in Multimodal Deep Neural Nets
Joshi, G., Walambe, R., & Kotecha, K. (2021) · IEEE Access · doi:10.1109/access.2021.3070212 · Cited by 204 · see citing works
SAFE Artificial Intelligence in finance
Giudici, P., & Raffinetti, E. (2023) · Finance research letters · doi:10.1016/j.frl.2023.104088 · Cited by 182 · see citing works
Vesnic-Alujevic, L., Nascimento, S., & Pólvora, A. (2020) · Telecommunications Policy · doi:10.1016/j.telpol.2020.101961 · Cited by 180 · see citing works
Steps Toward Robust Artificial Intelligence
Dietterich, T. G. (2017) · AI Magazine · doi:10.1609/aimag.v38i3.2756 · Cited by 139 · see citing works
Designing Futuristic Telemedicine Using Artificial Intelligence and Robotics in the COVID-19 Era
Bhaskar, S., Bradley, S., Sakhamuri, S., Moguilner, S., Chattu, V. K., Pandya, S., Schroeder, S., Ray, D., & Banach, M. (2020) · Frontiers in Public Health · doi:10.3389/fpubh.2020.556789 · Cited by 137 · see citing works
Explainable Artificial Intelligence for Tabular Data: A Survey
Sahakyan, M., Aung, Z., & Rahwan, T. (2021) · IEEE Access · doi:10.1109/access.2021.3116481 · Cited by 135 · cited 4× · see citing works
“Unlike prototypes which are actual instances from training data, counterfactuals can be new combinations of feature values [50] .”
BackgroundMethodResultInfluentialXAIR: 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 · see citing works
Choung, H., David, P., & Ross, A. A. (2022) · AI & Society · doi:10.1007/s00146-022-01473-4 · Cited by 119 · see citing works
AllenNLP Interpret: A Framework for Explaining Predictions of NLP Models
Wallace, E., Tuyls, J., Wang, J., Subramanian, S., Gardner, M., & Singh, S. (2019) · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): System Demonstrations · doi:10.18653/v1/d19-3002 · Cited by 116 · see citing works
How to achieve trustworthy artificial intelligence for health.
Bærøe, K., Miyata-Sturm, A., & Henden, E. (2020) · Bulletin of the World Health Organization · doi:10.2471/blt.19.237289 · Cited by 103 · see citing works
Kortylewski, A., Liu, Q., Wang, A., Sun, Y., & Yuille, A. (2021) · International Journal of Computer Vision · doi:10.1007/s11263-020-01401-3 · Cited by 98 · see citing works
Predictive analytics with ensemble modeling in laparoscopic surgery: A technical note
Zhang, Z., Chen, L., Xu, P., & Hong, Y. (2022) · Laparoscopic Endoscopic and Robotic Surgery · doi:10.1016/j.lers.2021.12.003 · Cited by 94 · see citing works
From AlphaGo to Power System AI: What Engineers Can Learn from Solving the Most Complex Board Game
Li, F. F., & Du, Y. (2018) · IEEE Power and Energy Magazine · doi:10.1109/mpe.2017.2779554 · Cited by 93 · see citing works
Interactive Visualization and Manipulation of Attention-based Neural Machine Translation
Lee, J., Shin, J.-H., & Kim, J. (2017) · Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d17-2021 · Cited by 87 · see citing works
XAI Evaluation: Evaluating Black-Box Model Explanations for Prediction
Zhang, Y., Xu, F., Zou, J., Petrosian, O. L., & Krinkin, K. V. (2021) · 2021 II International Conference on Neural Networks and Neurotechnologies (NeuroNT) · doi:10.1109/neuront53022.2021.9472817 · Cited by 76 · see citing works
Trusting autonomous vehicles: An interdisciplinary approach
Raats, K., Fors, V., & Pink, S. (2020) · Transportation Research Interdisciplinary Perspectives · doi:10.1016/j.trip.2020.100201 · Cited by 68 · see citing works
Data Cleaning for Accurate, Fair, and Robust Models: A Big Data - AI Integration Approach
Tae, K. H., Roh, Y., Oh, Y. H., Kim, H., & Whang, S. E. (2019) · Proceedings of the 3rd International Workshop on Data Management for End-to-End Machine Learning · doi:10.1145/3329486.3329493 · Cited by 62 · cited 1× · see citing works
“The integration testing outcomes have a huge impact on the production and release of the AI models.”
Requirements for Trustworthy Artificial Intelligence – A Review
Kaur, D., Uslu, S., & Arjan Durresi, A. (2020) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-57811-4_11 · Cited by 56 · see citing works
Liu, H., & Tan, S. T. (1995) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/icsmc.1995.538006 · Cited by 54 · see citing works
How to Explain Individual Classification Decisions
(2010) · Journal of Machine Learning Research · doi:10.5555/1756006.1859912 · Cited by 54 · see citing works
Feature Engineering (FE) Tools and Techniques for Better Classification Performance
(2017) · International Journal of Innovations in Engineering and Technology · doi:10.21172/ijiet.82.024 · Cited by 50 · see citing works
Tan, S., Soloviev, M., Hooker, G., & Wells, M. T. (2020) · ACM-IMS Foundations of Data Science Conference (FODS) · doi:10.1145/3412815.3416893 · Cited by 49 · see citing works
Principles of Explainable Artificial Intelligence
Guidotti, R., Monreale, A., Pedreschi, D., & Giannotti, F. (2021) · Springer eBooks · doi:10.1007/978-3-030-76409-8_2 · Cited by 40 · see citing works
Augmented Score-CAM: High resolution visual interpretations for deep neural networks
Ibrahim, R., & Shafiq, M. O. (2022) · Knowledge-Based Systems · doi:10.1016/j.knosys.2022.109287 · Cited by 39 · see citing works
Protecting JPEG Images Against Adversarial Attacks
Prakash, A., Moran, N., Garber, S., DiLillo, A., & Storer, J. A. (2018) · DCC · doi:10.1109/dcc.2018.00022 · Cited by 34 · see citing works
Robustness of AI-based prognostic and systems health management
Khan, S., Tsutsumi, S., Yairi, T., & Nakasuka, S. (2021) · Annual Reviews in Control · doi:10.1016/j.arcontrol.2021.04.001 · Cited by 33 · see citing works
Bayer, M., Frey, T., & Reuter, C. (2023) · Computers & Security · doi:10.1016/j.cose.2023.103430 · Cited by 33 · see citing works
Trustworthiness of Artificial Intelligence
Jain, S., Luthra, M., Sharma, S., & Fatima, M. (2020) · International Conference on Advanced Computing and Communication Systems (ICACCS) · doi:10.1109/icaccs48705.2020.9074237 · Cited by 28 · see citing works
Evaluating Robustness of AI Models against Adversarial Attacks
Chang, C.-L., Hung, J.-L., Tien, C., Tien, C., & Kuo, S. (2020) · doi:10.1145/3385003.3410920 · Cited by 27 · see citing works
Adversarial robustness assessment: Why in evaluation both L0 and L∞ attacks are necessary
Kotyan, S., & Vargas, D. V. (2022) · PLoS ONE · doi:10.1371/journal.pone.0265723 · Cited by 26 · see citing works
Trustworthy Artificial Intelligence in Education: Pitfalls and PathwaysPreprint
Smuha, N. A. (2020) · SSRN Electronic Journal · doi:10.2139/ssrn.3742421 · Cited by 25 · see citing works
Franco, D., Oneto, L., Navarin, N., & Anguita, D. (2021) · Entropy · doi:10.3390/e23081047 · Cited by 25 · see citing works
Pitfalls and pathways for Trustworthy Artificial Intelligence in education
Smuha, N. A. (2022) · Routledge eBooks · doi:10.4324/9780429329067-7 · Cited by 25 · see citing works
DeepCert: Verification of Contextually Relevant Robustness for Neural Network Image Classifiers
Paterson, C., Wu, H., Grese, J., Călinescu, R., Păsăreanu, C. S., & Barrett, C. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-83903-1_5 · Cited by 20 · see citing works
New Frontiers in Explainable AI: Understanding the GI to Interpret the GO
Cabitza, F., Campagner, A., & Ciucci, D. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-29726-8_3 · Cited by 19 · see citing works
McGovern, A., Bostrom, A., Davis, P., Demuth, J. L., Ebert‐Uphoff, I., He, R., Hickey, J. J., Gagne, D. J., Snook, N., Stewart, J. Q., et al. (2022) · Bulletin of the American Meteorological Society · doi:10.1175/bams-d-21-0020.1 · Cited by 19 · see citing works
Improving Automated Evaluation of Formative Assessments with Text Data Augmentation
Cochran, K., Cohn, C., Hutchins, N. M., Biswas, G., & Hastings, P. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-11644-5_32 · Cited by 17 · see citing works
Adversarial Robustness for Machine Learning
(2022) · Elsevier eBooks · doi:10.1016/c2020-0-01078-9 · Cited by 16 · see citing works
Machine Learning Explainability and Robustness
Datta, A., Fredrikson, M., Leino, K. R. M., Lu, K., Sen, S., & Wang, Z. (2021) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3447548.3470806 · Cited by 15 · see citing works
Singh, R., Mayank Vatsa, A., & Ratha, N. (2020) · Joint International Conference on Data Science & Management of Data (CODS-COMAD) · doi:10.1145/3430984.3431966 · Cited by 14 · see citing works
A Survey on Distributed Reinforcement Learning
Useng, M., & Abdulrahman, S. (2022) · Mesopotamian Journal of Big Data · doi:10.58496/mjbd/2022/006 · Cited by 13 · see citing works
CLoPAR: Classification based on Predictive Association Rules
Dehkordi, M. N., & Shenassa, M. H. (2006) · International IEEE Conference on Intelligent Systems (IS) · doi:10.1109/is.2006.348467 · Cited by 11 · see citing works
Robustness Testing of AI Systems: A Case Study for Traffic Sign Recognition
Berghoff, C., Bielik, P., Neu, M., Tsankov, P., & von Twickel, A. (2021) · IFIP advances in information and communication technology · doi:10.1007/978-3-030-79150-6_21 · Cited by 11 · see citing works
Adversarial Robustness in Graph-Based Neural Architecture Search for Edge AI Transportation Systems
Xu, P., Wang, K., Hassan, M. M., Chen, C., Lin, W., Hassan, M. R., & Fortino, G. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2022.3197713 · Cited by 11 · see citing works
Developing trustworthy AI for weather and climate
McGovern, A., Tissot, P. E., & Bostrom, A. (2024) · Physics Today · doi:10.1063/pt.3.5379 · Cited by 9 · see citing works
Face Recognition Algorithm based on Traditional and Artificial Intelligence: A Systematic Review
Kumar, M. M., & Hussaini, T. (2021) · International Conference on Intelligent Technologies (CONIT) · doi:10.1109/conit51480.2021.9498476 · Cited by 2 · see citing works
Flagged first, then by citations
3 of the 100 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Empowering Edge Intelligence: A Comprehensive Survey on On-Device AI Models
Wang, X., Tang, Z., Guo, J., Meng, T., Wang, C., Wang, T., & Weijia Jia, B. (2025) · ACM Computing Surveys · doi:10.1145/3724420 · Cited by 230 · see citing works
Interpretability research of deep learning: A literature survey
Xu, B., & Yang, G. (2024) · Information Fusion · doi:10.1016/j.inffus.2024.102721 · Cited by 159 · see citing works
Wang, X., Wang, B., Wu, Y., Ning, Z., Guo, S., & Yu, F. R. (2024) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2024.3446585 · Cited by 93 · see citing works
Evaluating Trustworthiness in AI: Risks, Metrics, and Applications Across Industries
Nastoska, A., Jancheska, B., Rizinski, M., & Trajanov, D. (2025) · Electronics · doi:10.3390/electronics14132717 · Cited by 110 · see citing works
A multifaceted vision of the human-AI collaboration: a comprehensive review
Puerta-Beldarrain, M., Gómez-Carmona, O., Sánchez-Corcuera, R., Casado-Mansilla, D., López-de-Ipiña, D., & Chen, L. (2025) · IEEE Access · doi:10.1109/access.2025.3536095 · Cited by 73 · see citing works
Mehdiyev, N., Majlatow, M., & Fettke, P. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.110363 · Cited by 43 · see citing works
Agentic AI Frameworks in SMMEs: A Systematic Literature Review of Ecosystemic Interconnected Agents
Olujimi, P. A., Owolawi, P. A., Mogase, R. C., & Wyk, E. V. (2025) · doi:10.20944/preprints202504.1797.v1 · Cited by 36 · see citing works
Agentic AI Frameworks in SMMEs: A Systematic Literature Review of Ecosystemic Interconnected Agents
Olujimi, P. A., Owolawi, P. A., Mogase, R. C., & Wyk, E. V. (2025) · AI · doi:10.3390/ai6060123 · Cited by 42 · see citing works
Artificial intelligence in urban science: why does it matter?
Ye, X., Yiğitcanlar, T., Goodchild, M. F., Huang, X., Li, W., Shaw, S., Fu, Y., Gong, W., & Newman, G. (2025) · Annals of GIS · doi:10.1080/19475683.2025.2469110 · Cited by 34 · see citing works
Cui, Y., & Hai, Z. (2025) · Education and Information Technologies · doi:10.1007/s10639-025-13448-1 · Cited by 36 · see citing works
Interpretable Clustering: A Survey
Hu, L., Jiang, M., Dong, J., Liu, X., & He, Z. (2026) · ACM Computing Surveys · doi:10.1145/3789495 · Cited by 30 · see citing works
Alsadie, D. (2025) · IET Information Security · doi:10.1049/ise2/2046868 · Cited by 29 · see citing works
The Evolution of Generative AI: Trends and Applications
Τρίγκα, Μ., & Dritsas, E. (2025) · IEEE Access · doi:10.1109/access.2025.3574660 · Cited by 36 · see citing works
Ray, P. P. (2026) · Expert Systems · doi:10.1111/exsy.70213 · Cited by 29 · see citing works
Alshehhi, K., Cheaitou, A., & Rashid, H. (2024) · Sustainability · doi:10.3390/su16177724 · Cited by 19 · see citing works
Securing the future: AI-driven cybersecurity in the age of autonomous IoT
Ogenyi, F. C., Ugwu, C. N., & Ugwu, O. P.-C. (2025) · Frontiers in the Internet of Things · doi:10.3389/friot.2025.1658273 · Cited by 19 · see citing works
Generative Artificial Intelligence in Marketing: The Invisible Danger of AI Hallucinations
Yaprak, B. (2024) · Journal of Economy Business and Management · doi:10.7596/jebm.1588897 · Cited by 18 · see citing works
Iqbal, A. B., Masoodi, T., Bhat, A. A., Macha, M. A., Assad, A., & Shah, S. Z. A. (2025) · Molecular Diversity · doi:10.1007/s11030-025-11133-6 · Cited by 15 · see citing works
Suffian, M., Kuhl, U., Bogliolo, A., & Alonso-Moral, J. M. (2025) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2025.103484 · Cited by 14 · see citing works
Khan, F. M., Zeb, A., RAHMAN, T., Al-Khasawneh, M. A., Daradkeh, Y. I., Siddiqui, I. F., Bashir, A. K., & Ullah, I. (2026) · Cognitive Computation · doi:10.1007/s12559-026-10559-w · Cited by 13 · see citing works
A Comprehensive Survey on Self-Interpretable Neural Networks
Ji, Y., Sun, Y., Zhang, Y., Wang, Z., Zhuang, Y., Gong, Z., Shen, D., Qin, C., Zhu, H., & Xiong, H. (2025) · Proceedings of the IEEE · doi:10.1109/jproc.2025.3635153 · Cited by 13 · see citing works
Sharma, D. P., Xue, L., Sun, X., Lin, X., & Xiong, P. (2025) · IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) · doi:10.1109/trustcom66490.2025.00125 · Cited by 11 · see citing works
Singh, A. K., Sharma, K. K., Bajpai, M. K., & Cabezuelo, A. S. (2024) · Applied Soft Computing · doi:10.1016/j.asoc.2024.112374 · Cited by 10 · see citing works
Zafar, I., Unar, A., Khan, N. U., Abalkhail, A., & Jamal, A. (2025) · Computational Biology and Chemistry · doi:10.1016/j.compbiolchem.2025.108535 · Cited by 9 · see citing works
A Systematic Review of Artistic Human-AI Co-Creative Systems and Interaction Design
Singh, S., Hindriks, K. V., Heylen, D., & Baraka, K. (2026) · ACM Transactions on Interactive Intelligent Systems · doi:10.1145/3834857 · Cited by 24 · see citing works
Data-Driven Innovation for Trustworthy AI
Siddharth, L., & Luo, J. (2025) · She ji · doi:10.1016/j.sheji.2025.06.002 · Cited by 7 · see citing works
Chotia, V., Sharma, P., Alshaghdali, N. O., Galgotia, D., & Sahore, N. (2025) · European Journal of Innovation Management · doi:10.1108/ejim-03-2025-0397 · Cited by 12 · see citing works
A Survey of the Application of Explainable Artificial Intelligence in Biomedical Informatics
Eshkiki, H., Tanhaei, F., Caraffini, F., & Mora, B. (2025) · Applied Sciences · doi:10.3390/app152412934 · Cited by 7 · see citing works
Risks of Discrimination Violence and Unlawful Actions in LLM-Driven Robots
Ren, Z. (2024) · Jisuanji shenghuojia. · doi:10.54097/taqbjh83 · Cited by 7 · see citing works
Smart hospitality: What drives consumers to accept AI services?
Azhar, M., Akhtar, M. J., Siddiqui, M. S., Khan, Z., & Khan, F. A. (2025) · Journal of Hospitality and Tourism Insights · doi:10.1108/jhti-04-2025-0492 · Cited by 6 · see citing works
AI-Guided Cancer Therapy for Patients with Coexisting Migraines
Olawade, D. B., Teke, J., Adeleye, K. K., Egbon, E., Weerasinghe, K., Ovsepian, S. V., & Boussios, S. (2024) · Cancers · doi:10.3390/cancers16213690 · Cited by 6 · see citing works
An Explainable Method for Malware Detection Through Convolutional Neural Networks
Mercaldo, F., Tavolato, P., Santone, A., & Martinelli, F. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-00630-1_18 · Cited by 5 · see citing works
Building Trustworthy AI in Healthcare
Amadi, C., & Ojo, A. (2025) · IEEE Access · doi:10.1109/access.2025.3648410 · Cited by 9 · see citing works
Artificial intelligence (AI) safety system for safe & trustworthy autonomy
Thakur, R., Sajid, Z., & Khan, F. I. (2026) · Digital Chemical Engineering · doi:10.1016/j.dche.2026.100308 · Cited by 5 · see citing works
Crash Scene to Resolution: LLM-based Agents Driven for Efficient Traffic Accident Handling
Huo, S., Rong, H., Zhou, Y., Zuo, H., Liu, D., Li, Z., Wang, M., & Jiang, H. (2025) · ACM Transactions on Internet of Things · doi:10.1145/3766899 · Cited by 4 · see citing works
A survey on design choices for self-supervised learning in computer vision
Wittscher, L. (2026) · Artificial Intelligence Review · doi:10.1007/s10462-026-11506-9 · Cited by 4 · see citing works
AI-Generated Videos and Deepfakes: A Technical PrimerPreprint
Sufian, A. (2024) · doi:10.36227/techrxiv.172348990.01007128/v1 · Cited by 5 · see citing works
Concept Disentangled Transformer for Hyperspectral Image Classification
Ahmad, M. I., Mazzara, M., Vivone, G., & Hong, D. (2026) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2026.3715319 · Cited by 3 · see citing works
Defending Against AI Threats with a User-Centric Trustworthiness Assessment Framework
Kafali, E., Preuveneers, D., Semertzidis, T., & Daras, P. (2024) · Big Data and Cognitive Computing · doi:10.3390/bdcc8110142 · Cited by 3 · see citing works
Integrating Shapley Value and Least Core Attribution for Robust Explainable AI in Rent Prediction
Wang, X., & Kee, T. (2025) · Buildings · doi:10.3390/buildings15173133 · Cited by 3 · see citing works
Musanga, V., Chibaya, C., & Viriri, S. (2026) · Frontiers in Computer Science · doi:10.3389/fcomp.2025.1639421 · Cited by 2 · see citing works
Uncertainty-aware human–machine collaboration in Camouflaged Object Detection
Yang, Z., Wang, K., Ming, Y., Yang, H., Chen, Q., Peng, Y., & Kong, W. (2026) · Cognitive Neurodynamics · doi:10.1007/s11571-025-10407-x · Cited by 2 · see citing works
The trusted evolution framework: governing cognitive stability in self-evolving foundation models
Chen, Y., & Chu, S. (2026) · Pattern Analysis and Applications · doi:10.1007/s10044-026-01615-0 · Cited by 2 · see citing works
Reliable IoT and Edge Device Using Trustworthy AI
Topazal, S. M., Islam, S., & Pandey, B. (2026) · Springer eBooks · doi:10.1007/978-3-032-15606-8_17 · Cited by 2 · see citing works
Chitrapu, P., Morampudi, M. K., & Kalluri, H. K. (2026) · Scientific Reports · doi:10.1038/s41598-026-43252-x · Cited by 2 · see citing works
Advancing adversarial and LLM robustness in trustworthy AI: a comprehensive survey
Tang, Q., Qian, J., Du, X., Wang, S., & Liu, H. (2026) · Artificial Intelligence Review · doi:10.1007/s10462-026-11558-x · Cited by 2 · see citing works
Explainable AI for Deep Visual Recognition: Evaluation, Methods, and Open Challenges
Alharbi, K. N. (2026) · Electronics · doi:10.3390/electronics15143222 · Cited by 2 · see citing works
Towards the Optimization of Hardware Efficiency through Machine Learning
Khdr, H., Sikal, M. B., Dietrich, B., & Henkel, J. (2025) · IEEE Computer Society Annual Symposium on VLSI · doi:10.1109/isvlsi65124.2025.11130243 · Cited by 2 · see citing works
Trust or distrust? AIGC trustworthiness and an extended analysis within nomology framework
Li, M., Du, R., Ai, S., Hunt, R. A., & Xie, J. (2025) · Data Science and Management · doi:10.1016/j.dsm.2025.09.005 · Cited by 2 · see citing works
Human-Centric AI for Enhancing Security in Smart City Anomaly Detection
Misra, S., Barik, K., Kvalvik, P., & Chockalingam, S. (2025) · IFIP advances in information and communication technology · doi:10.1007/978-3-031-96522-7_6 · Cited by 1 · see citing works
From Insights to Trust: A Review of AI-Driven Business Analytics Literature
Luchian, A., Strat, V. A., Jianu, O., & Drăgoicea, M. (2025) · Proceedings of the ... International Conference on Business Excellence · doi:10.2478/picbe-2025-0017 · Cited by 3 · see citing works
SafeGen: safeguarding privacy and fairness through a genetic method
Cinquini, M., Manerba, M. M., Mazzoni, F., Pratesi, F., & Guidotti, R. (2025) · Machine Learning · doi:10.1007/s10994-025-06835-9 · Cited by 1 · see citing works
Feature attribution in 5G intrusion detection: A statistical vs. logic-based comparison
Uccello, F., & Nadjm‐Tehrani, S. (2026) · Journal of Information Security and Applications · doi:10.1016/j.jisa.2026.104448 · Cited by 1 · see citing works
Fizan, M., Abdellatif, A. A., Asad, M., Zubair, M., Ni, B., Xu, B., Liu, X., Xiong, J., & Mehmood, M. Q. (2025) · Cluster Computing · doi:10.1007/s10586-025-05695-1 · Cited by 1 · see citing works
Trustworthy and Explainable AI: Bridging Algorithms and Ethics
Burla, S., & Priya, S. S. (2025) · International Conference on Applied Artificial Intelligence and Computing (ICAAIC) · doi:10.1109/icaaic64647.2025.11330323 · Cited by 1 · see citing works
Amin, M. A. S., Kim, S., & Kim, D. J. (2026) · ACM SIGMIS Database the DATABASE for Advances in Information Systems · doi:10.1145/3796972.3796975 · Cited by 1 · see citing works
Jang, M. (2026) · Information · doi:10.3390/info17020212 · Cited by 2 · see citing works
Din, F. U., Subhan, A., Rehman, A. U., Sayyed, A., Abbas, T., Abbas, Z., & Lee, S. W. (2026) · IEEE Internet of Things Journal · doi:10.1109/jiot.2026.3686391 · Cited by 1 · see citing works
Artificial intelligence and radiomics in precision oncology
Shafeeque, M. A., Fatima, N., Khan, M. A., Rashid, G., Singh, T., Zaidi, N., & Siddiqui, Z. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-45004-4.00015-3 · Cited by 1 · see citing works
A Conceptual Crosswalk Between Trustworthy Records and Trustworthy AI
Carrasco, L. B., Engvall, T. S., & Dobslaw, F. (2026) · Philosophy & Technology · doi:10.1007/s13347-026-01112-6 · Cited by 1 · see citing works
Presta, R., Simone, F. D., Bacchiani, L., & Girau, R. (2026) · Technologies · doi:10.3390/technologies14070386 · Cited by 1 · see citing works
Explainable AI in Biometrics: A Novel Framework for Facial Recognition Interpretation
Agrawal, A., Kaur, K., & Kaur, H. (2024) · International Conference on Modeling, Simulation & Intelligent Computing (MoSICom) · doi:10.1109/mosicom63082.2024.10881703 · Cited by 1 · see citing works
Kabir, M. A., Islam, M. M. M., Chakraborty, N. R., & Noori, S. R. H. (2025) · Springer series in advanced manufacturing · doi:10.1007/978-3-031-80154-9_9 · Cited by 1 · see citing works
Deepfake Manipulation and Ethical Dilemmas: A Comprehensive Risk Assessment
Kubanek, M., & Szymoniak, S. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-01429-0_46 · Cited by 2 · see citing works
Explainability in AI: Comparing Human-Like and System-Like Trust Repair Strategies
Konopka, B., & Wiesche, M. (2026) · Information Systems Frontiers · doi:10.1007/s10796-026-10751-1 · Cited by 1 · see citing works
Explainable and Trustworthy AI with Neuro-Symbolic Approaches
Bhuyan, B. P., Ramdane-Chérif, A., Singh, T. P., & Tomar, R. (2024) · Studies in computational intelligence · doi:10.1007/978-981-97-8171-3_16
Sindiramutty, S. R., Jhanjhi, N. Z., & Khan, N. A. (2025) · Advances in information security, privacy, and ethics book series · doi:10.4018/979-8-3693-6135-1.ch009
Princípios Internacionais de Inteligência Artificial
da Silva, C. B. F. (2024) · Revista da Escola Superior do Ministério Público da União · doi:10.63601/resmpu.2024.v1.n2.e-2107
Mitra, U., & Rehman, S. U. (2025) · IGI Global eBooks · doi:10.4018/979-8-3373-2200-1.ch001
“The Trust in AI-Mix: Technical, Legal and Ethical Requirements as well as Psychology”
Spilski, J., Spilski, A., & Lachmann, T. (2025) · IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops) · doi:10.1109/percomworkshops65533.2025.00068
A Federated Causal Gnn Approach for Fault Diagnosis of Metro Apu with Privacy PreservationPreprint
Liu, J., & Pan, K. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5353434
Credibility Assessment of Human–Generative AI Interaction
Zhao, Y., Song, S., & Jing, Y. (2025) · Cambridge University Press eBooks · doi:10.1017/9781009587877.005
Response grid plots for model-agnostic machine learning insight
Barton, R. R. (2025) · IMA Journal of Management Mathematics · doi:10.1093/imaman/dpaf036
Ersöz, B., & Bülbül, H. İ. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-7574-8.ch002 · Cited by 1 · see citing works
Akalın, N., & Riveiro, M. (2025) · IEEE RO-MAN · doi:10.1109/ro-man63969.2025.11217728
Zhang, X., Chen, H., & Wu, L. (2025) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3746252.3760829
How LLMs Handle Cultural Bias: Reactions to Asian Minority Historical Narratives
Shujaa, S., Dorleon, G., & Tang, A. (2025) · Lecture notes in computer science · doi:10.1007/978-981-95-4861-3_3
Trustworthy AI in Spatial Crowdsourcing: Mitigating Bias and Detecting Outliers for Robust Models
Rahman, M. M., Hasan, M. M., Mamun, Q., Wu, J., Pan, Z., & Shimamoto, S. (2025) · Asia-Pacific Conference on Communications (APCC) · doi:10.23919/apcc64555.2025.11279827
Vichare, S. C. (2025) · International Journal of Computational and Experimental Science and Engineering · doi:10.22399/ijcesen.3757
Artificial Intelligence and Human Resource Management: A Counterfactual Analysis of Productivity
Ditta, G. (2026) · Academicus : International Scientific Journal · doi:10.7336/academicus.2026.33.01
The Impact of Prompt Formats on the Robustness of LLMs
Lee, S., & Lee, Y. (2025) · The Journal of Korean Association of Computer Education · doi:10.32431/kace.2025.28.12.001
A dual-process neuro-symbolic framework for cognition-driven language understanding
Zhu, D. (2026) · Applied Soft Computing · doi:10.1016/j.asoc.2026.114887
Explainability-Guided Defense: Attribution-Aware Model Refinement Against Adversarial Data Attacks
Wang, L., Nayyem, M. N., Rakin, A. A., Santosh, K., Zhang, C., & Zhou, Y. (2025) · Proceedings · doi:10.1109/icdm65498.2025.00171
GAI & cybersecurity: Bi-visions
Taher, A. B., Fourati, L. C., & Ayed, S. (2026) · Computer Science Review · doi:10.1016/j.cosrev.2026.100940
Albahri, A. S., Hamid, R. A., Al-Qaysi, Z. T., Chyad, M. A., Aljanabi, M., Ali, A. H., Albahri, O. S., Alamoodi, A. H., Duhaim, A. M., Garfan, S., et al. (2026) · International Journal of Data Science and Analytics · doi:10.1007/s41060-025-00919-4
Explainable AI for Secure and Trustworthy Autonomous Network Management
Kandi, Y., Srivastava, D., Logeshwaran, J., Dhanasekaran, S., Sama, M., & Garg, A. (2025) · IEEE International Conference on Smart Innovations in Systems, Infrastructure, Mechanical, Power, AI and Computing Technologies (SISIMPACT) · doi:10.1109/sisimpact67725.2025.11439829
QuAC: Quality-aware chinese pre-training proactive defense framework
Deng, Z., Chen, C. L. P., Chen, Y., & Zhang, T. (2026) · Pattern Recognition · doi:10.1016/j.patcog.2026.113574
Colegado, S., Jin, J., & Hou, Y. (2026) · IEEE International Conference on Semantic Computing (ICSC) · doi:10.1109/icsc67292.2026.00018
SGExplainer: Balanced Path-based Signed Graph Neural Network Explanation for Link Sign Prediction
Gao, J., Hu, J. W., Min, G., & Hao, F. (2026) · International World Wide Web Conference (WWW) · doi:10.1145/3774904.3792146
SPATA: Systematic Pattern Analysis for Detailed and Transparent Data Cards
Vitorino, J., Maia, E., Praça, I., & Soares, C. (2026) · Communications in computer and information science · doi:10.1007/978-3-032-19102-1_22
Trustworthy Multi-Agent Framework for Video Thumbnail Selection in Data Asset Ecosystems
Zhang, B., Xu, C., & Huang, G. (2025) · International Conference on Big Data Economy and Information Management (BDEIM) · doi:10.1145/3800000.3800224
Mercaldo, F., Schrittwieser, S., Kochberger, P., Petyx, M., Agnello, P., Santone, A., Martinelli, F., & Tavolato, P. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-23883-2_24
Towards Continuous Explainability of Complex AI Systems: Challenges and Requirements
Müller, A. W., Recker, J., Keuper, A., Schuh, G., & Legat, C. (2026) · Studies in computational intelligence · doi:10.1007/978-3-032-18786-4_36
SemuFuzz: An efficient fuzzing approach for deep learning libraries based on semantic representation
Chen, Z., Chen, J., Cai, S., Chen, J., & Gu, W. (2026) · Journal of Systems and Software · doi:10.1016/j.jss.2026.112999
Xing, H., Rehman, A., Hussain, M., Mastoi, M. S., Gulzar, N., & Khan, M. A. (2026) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-026-10699-0
Kim, E., & Chiu, J. L. (2026) · Libri · doi:10.1515/libri-2025-0143
A Unified Framework for Trustworthy Machine Learning for Intrusion Detection Systems
Fawaz, H., Ayoub, O., & Giordano, S. (2026) · IFIP Networking Conference (IFIP Networking) · doi:10.23919/ifipnetworking70592.2026.11579230
Efficient and Interpretable Tabular Learning Via Visual Transformation
Hajjari, A., Seyedmohammadi, S. J., Sadeghi, M. T., Abouei, J., Ng, P. C., & Mohammadi, A. (2026) · IEEE International Conference on Human-Machine Systems (ICHMS) · doi:10.1109/ichms69701.2026.11602339
Explainability in Autonomous Cyber Decision-Making
Kadyan, P. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-2600-0413-5.ch007
Mercaldo, F., Schrittwieser, S., Kochberger, P., Petyx, M., Agnello, P., Santone, A., Martinelli, F., & Tavolato, P. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-32636-2_35
- Authors
- chander, B., John, C., Warrier, L., & Gopalakrishnan, K.
- Affiliations
- Indian Institute of Information Technology Kottayam (IN)
- Pondicherry University (IN)
- Venue
- ACM Computing Surveys
- Published
- 2025-02-10
- Volume
- 57
- Issue
- 6
- Pages
- 1-49
- ISSN
- 0360-0300 (ISSN-L)
- 1557-7341
- OpenAlex
- W4400123667
- Cited by
- 108
- References
- 100
- Citations by year
- 2026: 48 · 2025: 48 · 2024: 12
- FWCI
- 16.00
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- ACM Computing Surveys (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Artificial Intelligence › Adversarial Robustness in Machine Learning
- Keywords
- trustworthy AI, explainable AI, robustness, AI safety, black-box systems, AI system design, AI ethics, AI security, human-AI interaction
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.
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
