The DOI matches its Crossref record; 24 of the 25 references it cites were found in a registry or an index, and 1 are not checked yet.
The Mythos of Model Interpretability: In machine learning, the concept of interpretability is both important and slippery.
Open accessAbstract
Supervised machine-learning models boast remarkable predictive capabilities. But can you trust your model? Will it work in deployment? What else can it tell you about the world?
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
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?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Newer methodologies have the danger of becoming more complex and less interpretable, even when sophisticated interpretation technique are used11.”
From Minimum information about clinical artificial intelligence modeling: the MI-CLAIM checklist. (2020) · cites this paper as [11]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“As users of these systems have grown beyond ML experts and the research community, the need to better interpret and understand them has grown as well, particularly in the context of high-stakes decisions that affect individuals ' health or wellbeing (Lakkaraju et al., 2016; Lipton, 2018; Rudin, 2019).”
From Explainable machine learning for public policy: Use cases, gaps, and research directions (2023) · cites this paper as Lipton, 2018
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“While this expansion of the field has yielded a rich body of methodological work, recently, the community has begun to highlight the shortfalls such as the lack of consistent language and definitions, the lack of clearly defined explainability goals, and desiderata; and the lack of consensus on metrics and methods of evaluating the quality of explanations (Doshi-Velez and Kim, 2017; Lipton, 2018;”
From Explainable machine learning for public policy: Use cases, gaps, and research directions (2023) · cites this paper as Lipton, 2018
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“For her, as for other people who are not AI specialists, it may be better to employ natural language, narrative, explanation by example, visualisations, and so forth (Barredo Arrieta et al., 2020;Borrego-Díaz & Galán-Páez, 2022;Lipton, 2018).”
From Hidden Risks: Artificial Intelligence and Hermeneutic Harm (2025) · cites this paper as Lipton, 2018
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“According to Lipton [121], a model is transparent if its intrinsic structure satisfies at least one of the following three requirements:”
From A Survey on Explainable Anomaly Detection (2024) · cites this paper as [121]
The cited source says what the claim attributes to it.
In the citing paper
“Unfortunately, scarce contributions have attempted to define such goals from a conceptual perspective [5, 13, 24, 30].”
From Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI (2020) · cites this paper as [5]
The cited source says what the claim attributes to it.
In the citing paper
“However, it misses to consider other purposes motivating the need for interpretable AI models, such as causality, transferability, informativeness, fairness and confidence [5, 24, 25, 26].”
From Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI (2020) · cites this paper as [5]
The cited source says what the claim attributes to it.
In the citing paper
“Therefore, post-hoc explanation techniques are employed to explain existing models (Došilović et al., 2018; Lipton, 2018).”
From Effects of Explainable Artificial Intelligence on trust and human behavior in a high-risk decision task (2023) · cites this paper as Lipton, 2018
The cited source says what the claim attributes to it.
In the citing paper
“As explainability is not a monolithic concept and can play different roles in different applications (Lipton, 2018; Molnar, 2019), this process requires extensive domain/application-specific efforts.”
From Explainable machine learning for public policy: Use cases, gaps, and research directions (2023) · cites this paper as Lipton, 2018
The cited source says what the claim attributes to it.
In the citing paper
“Too great an emphasis on making explanations psychologically fitting risks making it easier for bad actors to deceive and manipulate end-users (Baron, 2023;Lipton, 2018, pp. 21-22).”
From Hidden Risks: Artificial Intelligence and Hermeneutic Harm (2025) · cites this paper as Lipton, 2018
The cited source says what the claim attributes to it.
In the citing paper
“While providing individuals with grounds to contest individual decisions is often cited as a central motivation of the field of explainable artificial intelligence (XAI) (Ribeiro, Singh, and Guestrin 2016;Lipton 2018;Adadi and Berrada 2018;Molnar 2020), only very few technical works address contestability in greater detail (Mansi, Karusala, and Riedl 2025).”
From Explainable AI Isn't Enough! Rethinking Algorithmic Contestability (2026) · cites this paper as Lipton 2018
The cited source says what the claim attributes to it.
In the citing paper
“Interpretability and explainability methods promoted by popular tools vary widely in their goals [120] and, like many 'snake oil' AI products [18, 59, 91], may encourage false confidence in their users [118].”
From Towards AI Accountability Infrastructure: Gaps and Opportunities in AI Audit Tooling (2025) · cites this paper as [120]
The cited source says what the claim attributes to it.
In the citing paper
“In interpretable AI research, the motivations and methods employed are often described as”
From Open Problems in Mechanistic Interpretability (2025) · cites this paper as Lipton, 2018
The check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“With the availability of more datasets, there is an increased benefit that allows more complex functions to be approximated for future developments in XAI [21,167].”
From Interpreting Black-Box Models: A Review on Explainable Artificial Intelligence (2024) · cites this paper as [167]
Experimental — accuracy not yet measuredIn the citing paper
“Clinical tasks are often complex and it is impossible to incorporate and evaluate all desirable properties (e.g. legality, ethicality, and robustness) in a model using standard predictive performance evaluation metrics (e.g. area under the receiver-operating curve) [12].”
In the citing paper
“Researchers do not specify what terms mean, use the same term for different meanings, or refer to the same notion by different terms.”
In the citing paper
“Lipton [12] points out that these terms are often ill-defined in the existing literature.”
In the citing paper
“These explanations have the potential to present plausible but misleading explanations [12, 75].”
In the citing paper
“However, it is important to note that ML models are not designed to identify causal relationships but rather association relationships 116 .”
From Machine learning in concrete science: applications, challenges, and best practices (2022) · cites this paper as [116]
In the citing paper
“Hence, trust in ML models is strongly related to their interpretability, which has thus become a focus of active research in the ML community 116,132 .”
From Machine learning in concrete science: applications, challenges, and best practices (2022) · cites this paper as [116]
In the citing paper
“As the field is still in its infancy, there is no consensus on the definition of the terms interpretability and explainability in this context, and recently various definitions have been proposed for these terms [9,88,110].”
From Transparency of deep neural networks for medical image analysis: A review of interpretability methods (2022) · cites this paper as [88]
In the citing paper
“Lipton [147], however, notes that even white-box models might not be interpretable anymore when their size exceed the limited capacity of human cognition”
From From Anecdotal Evidence to Quantitative Evaluation Methods: A Systematic Review on Evaluating Explainable AI (2023) · cites this paper as [147]
In the citing paper
“We also considered the ethical implications of AI in preserving privacy and the necessity for transparency, oversight, and continuous evaluation of AI systems [52][53][54].”
From Fairness and Bias in Artificial Intelligence: A Brief Survey of Sources, Impacts, and Mitigation Strategies (2023) · cites this paper as [52]
In the citing paper
“XAI can be used to help us evaluate if predictions are biased and defend algorithmic decisions as being fair and ethical [18,19,46,66].”
From Current Challenges and Future Opportunities for XAI in Machine Learning-Based Clinical Decision Support Systems: A Systematic Review (2021) · cites this paper as [46]
In the citing paper
“Although, giving explanations can increase understanding of and trust in a system [19,46,64], simple explanations can hide undesirable attributes of the system and may mislead users into coming to dangerous or unfounded conclusions that could ultimately be unethical [21].”
From Current Challenges and Future Opportunities for XAI in Machine Learning-Based Clinical Decision Support Systems: A Systematic Review (2021) · cites this paper as [46]
In the citing paper
“Additionally, XAI can help to shed some light onto causality [18,46,67] although, there is a recognized need to go beyond causality/correlation to true “causability” [3].”
From Current Challenges and Future Opportunities for XAI in Machine Learning-Based Clinical Decision Support Systems: A Systematic Review (2021) · cites this paper as [46]
In the citing paper
“However, linear models, for example, are not strictly more interpretable than, for example, neural networks, especially when high-dimensional or heavily engineered features are used.”
From Current Challenges and Future Opportunities for XAI in Machine Learning-Based Clinical Decision Support Systems: A Systematic Review (2021) · cites this paper as [46]
In the citing paper
“On the other hand, there are some studies supporting that XAI is not always necessary [46,134,135].”
From Current Challenges and Future Opportunities for XAI in Machine Learning-Based Clinical Decision Support Systems: A Systematic Review (2021) · cites this paper as [46]
In the citing paper
“Lipton [46] argues that the short-term goal of building trust with doctors by developing transparent models might clash with the longer-term goal of improving health care.”
From Current Challenges and Future Opportunities for XAI in Machine Learning-Based Clinical Decision Support Systems: A Systematic Review (2021) · cites this paper as [46]
In the citing paper
“Lipton (2018) [31] and Gunning et al. (2019) [32] highlight that for AI to be effectively integrated into clinical practice, it must provide clear and interpretable explanations of its outputs.”
From Leading with AI in critical care nursing: challenges, opportunities, and the human factor (2024) · cites this paper as Lipton (2018) [31]
In the citing paper
“However, at this level of abstraction, explainability may be difficult to reach even to computer scientists and engineers (Lipton 2018).”
From Transparency as design publicity: explaining and justifying inscrutable algorithms (2021) · cites this paper as Lipton 2018
In the citing paper
“The proposed approach to algorithmic transparency deviates from the existing body of literature on explainable artificial intelligence (xAI), where the concept of transparency focuses on the explanation of the inner workings of algorithms or the interpretability of their individual outcomes (Lipton 2018; Ribeiro et al. 2016).”
From Transparency as design publicity: explaining and justifying inscrutable algorithms (2021) · cites this paper as Lipton 2018
In the citing paper
“This suggests a disconnect between organizational realities and current research, which undermines advancements [197,287].”
From Fairness in machine learning: A survey (2024) · cites this paper as [197]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Regression shrinkage and selection via the lasso
Tibshirani, R. (1996) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1996.tb02080.x · Cited by 51,482
(2008) · Journal of Machine Learning Research · Cited by 33,051
"Why Should I Trust You?": Explaining the Predictions of Any Classifier
Ribeiro, M. T., Singh, S., & Guestrin, C. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939778 · Cited by 17,555
Causality: Models, Reasoning, and Inference
Pearl, J. (2009) · doi:10.1017/cbo9780511803161 · Cited by 11,653
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
Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps
Simonyan, K., Vedaldi, A., & Zisserman, A. (2013) · arXiv · doi:10.48550/arxiv.1312.6034 · Cited by 5,370
Dueling Network Architectures for Deep Reinforcement Learning
Wang, Z., Schaul, T., Hessel, M., van Hasselt, H., Lanctot, M., & de Freitas, N. (2015) · arXiv · doi:10.48550/arxiv.1511.06581 · Cited by 3,300
Fair prediction with disparate impact: a study of bias in recidivism prediction instruments
Chouldechova, A. (2017) · Big Data · doi:10.1089/big.2016.0047 · Cited by 2,159
European Union Regulations on Algorithmic Decision Making and a “Right to Explanation”
Goodman, B., & Flaxman, S. (2017) · AI Magazine · doi:10.1609/aimag.v38i3.2741 · Cited by 2,131 · cited 2×
“Few would suggest that a human could contemplate a thousand parameters at once, but most could simulate a linear model with 20 parameters.”
Background(2009) · Cited by 1,900
Flagged first, then by citations
Research works printed but not linked: 9
- Athey S. Imbens G. W. 2015 Machine-learning methods https://arxiv.org/abs/1504.01132v1 (see also ref. 7). Athey S. Imbens G. W. 2015 Machine-learning methods https://arxiv.org/abs/1504.01132v1 (see also ref. 7). · arXiv
- Caruana R., 1999, Proceedings of the American Medical Informatics Association (AMIA) Symposium: 212-215
- Chang J., 2009, Proceedings of the 22nd International Conference on Neural Information Processing Systems (NIPS), 288-296
- Doshi-Velez F., 2015, Proceedings of the 29th Association for the Advancement of Artificial Intelligence (AAAI) Conference, 2575-2581
- Kim B., 2014, Proceedings of the 27th International Conference on Neural Information Processing Systems (NIPS), 2, 1952
- Lipton Z. C., 2016, Proceedings of Machine Learning for Healthcare.
- Mikolov T., 2013, Proceedings of the 26th International Conference on Neural Information Processing Systems (NIPS), 2
- Ridgeway G., 1998, Proceedings of the 4th International Conference on Knowledge Discovery and Data Mining: 101-104
- 10.5555/3045390.3045601 · DOI
Cited by
Sorted by citations
Items 901–1,000 of 1,051
Large language models: an overview of foundational architectures, recent trends, and a new taxonomy
Mienye, I. D., Jere, N., Obaido, G., Ogunruku, O. O., Esenogho, E., & Modisane, C. (2025) · Discover Applied Sciences · doi:10.1007/s42452-025-07668-w · Cited by 46
Semantic Web Technologies for Explainable Machine Learning Models: A Literature Review.
Seeliger, A., Pfaff, M., & Krcmar, H. (2019) · Cited by 45
TeleGam: Combining Visualization and Verbalization for Interpretable Machine Learning
Hohman, F., Srinivasan, A., & Drucker, S. M. (2019) · Visualization · doi:10.1109/visual.2019.8933695 · Cited by 46
A Comprehensive Explanation Framework for Biomedical Time Series Classification
Ivaturi, P., Gadaleta, M., Pandey, A. C., Pazzani, M. J., Steinhubl, S. R., & Quer, G. (2021) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2021.3060997 · Cited by 46
Kenny, E. M., & Keane, M. T. (2021) · Knowledge-Based Systems · doi:10.1016/j.knosys.2021.107530 · Cited by 49
A Human-Grounded Evaluation Benchmark for Local Explanations of Machine LearningPreprint
Mohseni, S., Block, J. E., & Ragan, E. D. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1801.05075 · Cited by 80
Regularizing Black-box Models for Improved InterpretabilityPreprint
Plumb, G., Al-Shedivat, M., Cabrera, Á. A., Perer, A., Xing, E. P., & Talwalkar, A. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1902.06787 · Cited by 53
Machine learning and power relations
Maas, J. (2022) · Ai & Society · doi:10.1007/s00146-022-01400-7 · Cited by 31
Human-centered explainability for life sciences, healthcare, and medical informatics
Dey, S., Chakraborty, P., Kwon, B. C., Dhurandhar, A., Ghalwash, M. F., Suárez, F., Ng, K., Sow, D., Varshney, K. R., & Meyer, P. (2022) · Patterns · doi:10.1016/j.patter.2022.100493 · Cited by 56
Opening the black box: interpretable machine learning for predictor finding of metabolic syndrome
Zhang, Y., Zhang, X., Razbek, J., Li, D., Xia, W., Bao, L., Mao, H., Daken, M., & Cao, M. (2022) · BMC Endocrine Disorders · doi:10.1186/s12902-022-01121-4 · Cited by 47
Reliability and Interpretability in Science and Deep Learning
Scorzato, L. (2024) · Minds and Machines · doi:10.1007/s11023-024-09682-0 · Cited by 51
Towards AI driven surface roughness evaluation in manufacturing: a prospective study
Ghosh, S., Knoblauch, R., Mansori, M. E., & Corleto, C. (2024) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-024-02493-1 · Cited by 55
From Tools to Agents: Meta-Analytic Insights into Human Acceptance of AI
Li, B., Lai, E. Y., & Wang, X. (. (2025) · Journal of Marketing · doi:10.1177/00222429251355266 · Cited by 59
Raghavan, M. (2023) · ACM eBooks · doi:10.1145/3603195.3603207 · Cited by 49
An overview and a benchmark of active learning for outlier detection with one-class classifiers
Trittenbach, H., Englhardt, A., & Böhm, K. (2020) · Expert Systems with Applications · doi:10.1016/j.eswa.2020.114372 · Cited by 48
Sanchis‐Segura, C., Aguirre, N., Cruz-Gómez, Á. J., Félix, S., & Forn, C. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119343 · Cited by 48
A Human-Grounded Evaluation of SHAP for Alert ProcessingPreprint
Weerts, H., van Ipenburg, W., & Pechenizkiy, M. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1907.03324 · Cited by 45
Model Interpretability through the Lens of Computational ComplexityPreprint
Barceló, P., Monet, M., Pérez, J. A., & Subercaseaux, B. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.12265 · Cited by 45
Sattarzadeh, S., Sudhakar, M., Plataniotis, K. N., Jang, J., Jeong, Y., & Kim, H. (2021) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp39728.2021.9415064 · Cited by 55
Wang, S., Dong, H., Shen, W., Yang, Y., Li, Z., Liu, Y., Wang, C., Gu, B., & Zhang, L. (2021) · RSC Advances · doi:10.1039/d1ra05778b · Cited by 44
Using MaxSAT for Efficient Explanations of Tree Ensembles
Ignatiev, A., Izza, Y., Stuckey, P. J., & Marques-Silva, J. P. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i4.20292 · Cited by 44
Hong, G.-S., Jang, M., Kyung, S., Cho, K., Jeong, J., Lee, G. Y., Shin, K., Kim, K., Ryu, S. M., Seo, J. B., et al. (2023) · Korean Journal of Radiology · doi:10.3348/kjr.2023.0393 · Cited by 46
Hauptman, A. I., Schelble, B. G., Duan, W., Flathmann, C., & McNeese, N. J. (2024) · Cognition Technology & Work · doi:10.1007/s10111-024-00765-7 · Cited by 56
A survey of methods and tools used for interpreting Random Forest
Haddouchi, M., & Berrado, A. (2019) · International Conference on Smart Systems and Data Science (ICSSD) · doi:10.1109/icssd47982.2019.9002770 · Cited by 44
QED: A Framework and Dataset for Explanations in Question Answering
Lamm, M., Palomaki, J., Alberti, C., Andor, D., Choi, E., Soares, L. B., & Collins, M. (2021) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00398 · Cited by 59
Explainable AI for Deep Learning Based Disease Detection
Kinger, S., & Kulkarni, V. Y. (2021) · International Conference on Contemporary Computing · doi:10.1145/3474124.3474154 · Cited by 49
On the explanatory power of Boolean decision trees
Audemard, G., Bellart, S., Bounia, L., Koriche, F., Lagniez, J.-M., & Marquis, P. (2022) · Data & Knowledge Engineering · doi:10.1016/j.datak.2022.102088 · Cited by 44
TreeView: Peeking into Deep Neural Networks Via Feature-Space PartitioningPreprint
Thiagarajan, J. J., Kailkhura, B., Sattigeri, P., & Ramamurthy, K. N. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1611.07429 · Cited by 44
Explaining Question Answering Models through Text GenerationPreprint
Latcinnik, V., & Berant, J. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2004.05569 · Cited by 60
Bell, A., Nov, O., & Stoyanovich, J. (2023) · Data & Policy · doi:10.1017/dap.2023.8 · Cited by 44
Ethics and Good Practice in Computational Paralinguistics
Batliner, A., Hantke, S., & Schuller, B. (2022) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2020.3021015 · Cited by 30
Malgieri, G., & Pasquale, F. (2023) · Computer law & security review · doi:10.1016/j.clsr.2023.105899 · Cited by 66
A Machine Learning Explainability Tutorial for Atmospheric Sciences
Flora, M. L., Potvin, C. K., McGovern, A., & Handler, S. L. (2023) · Artificial Intelligence for the Earth Systems · doi:10.1175/aies-d-23-0018.1 · Cited by 49
Explainable Artificial Intelligence for Resilient Security Applications in the Internet of Things
Masud, M. T., Keshk, M., Moustafa, N., Linkov, I., & Emge, D. K. (2024) · IEEE Open Journal of the Communications Society · doi:10.1109/ojcoms.2024.3413790 · Cited by 50
Explanation, semantics, and ontology
Guizzardi, G., & Guarino, N. (2024) · Data & Knowledge Engineering · doi:10.1016/j.datak.2024.102325 · Cited by 76
You, X., Huang, Y., Zhang, C. L., Wang, J., Yin, H., & Wu, H. (2024) · Science China Information Sciences · doi:10.1007/s11432-024-4257-6 · Cited by 43
Multi-stakeholder perspective on responsible artificial intelligence and acceptability in education
Karran, A. J., Charland, P., Martineau, J. T., de Guinea, A. O., Lesage, A., Sénécal, S., & Léger, P. (2025) · npj Science of Learning · doi:10.1038/s41539-025-00333-2 · Cited by 76
Explicit Document Modeling through Weighted Multiple-Instance Learning
Pappas, N., & Popescu-Belis, A. (2017) · Journal of Artificial Intelligence Research · doi:10.1613/jair.5240 · Cited by 45
Understanding Deep Neural Networks for Regression in Leaf Counting
Dobrescu, A., Giuffrida, M. V., & Tsaftaris, S. A. (2019) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2019.00316 · Cited by 44
Karlos, S., Kostopoulos, G., & Kotsiantis, S. B. (2020) · Applied Sciences · doi:10.3390/app10238413 · Cited by 43
Is explainable artificial intelligence intrinsically valuable?
Colaner, N. R. (2021) · AI & Society · doi:10.1007/s00146-021-01184-2 · Cited by 43
Understanding and Interpreting the Impact of User Context in Hate Speech Detection
Mosca, E., Wich, M., & Groh, G. (2021) · International Workshop on Natural Language Processing for Social Media (SocialNLP) · doi:10.18653/v1/2021.socialnlp-1.8 · Cited by 43
Real-Time Text Classification of User-Generated Content on Social Media: Systematic Review
Rogers, D., Preece, A. D., Innes, M., & Spasić, . (2021) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2021.3120138 · Cited by 48
Bueno, I., Carrasco, R. A., Ureña, R., & Herrera‐Viedma, E. (2021) · Information Sciences · doi:10.1016/j.ins.2021.12.080 · Cited by 44
Interpretable Random Forests via Rule ExtractionPreprint
Bénard, C., Gérard Biau, A., da Veiga, S., & Scornet, E. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2004.14841 · Cited by 43
Learning a Formula of Interpretability to Learn Interpretable Formulas
Virgolin, M., Lorenzo, A. D., Medvet, E., & Randone, F. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58115-2_6 · Cited by 46
SAT-Based Rigorous Explanations for Decision Lists
Ignatiev, A., & Marques-Silva, J. P. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-80223-3_18 · Cited by 42
Explainable Artificial Intelligence in Cybersecurity: A Brief Review
Hariharan, S., Velicheti, A., Anagha, A. S., Thomas, C., & Balakrishnan, N. (2021) · doi:10.1109/isea-isap54304.2021.9689765 · Cited by 45
Towards explainable AI for hyperspectral image classification in Edge Computing environments
Lucia, G. D., Lapegna, M., & Romano, D. (2022) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2022.108381 · Cited by 45
Ebermann, C., Selisky, M., & Weibelzahl, S. (2022) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2022.2126812 · Cited by 53
Fuzzy Rule-Based Local Surrogate Models for Black-Box Model Explanation
Zhu, X., Wang, D., Pedrycz, W., & Li, Z. (2022) · IEEE Transactions on Fuzzy Systems · doi:10.1109/tfuzz.2022.3218426 · Cited by 42
Revisiting the Performance-Explainability Trade-Off in Explainable Artificial Intelligence (XAI)
Crook, B., Schlüter, M., & Speith, T. (2023) · IEEE International Requirements Engineering Conference Workshops (REW) · doi:10.1109/rew57809.2023.00060 · Cited by 47
Gao, Y., Liu, J., Li, W., Hou, M. L., Li, Y., & Zhao, H. (2023) · Electronics · doi:10.3390/electronics12234846 · Cited by 46
Evaluating ChatGPT-4V in chest CT diagnostics: a critical image interpretation assessment
Dehdab, R., Brendlin, A., Werner, S., Almansour, H., Gassenmaier, S., Brendel, J. M., Nikolaou, K., & Afat, S. (2024) · Japanese Journal of Radiology · doi:10.1007/s11604-024-01606-3 · Cited by 43
On the interpretability of quantum neural networks
Pira, L., & Ferrie, C. (2024) · Quantum Machine Intelligence · doi:10.1007/s42484-024-00191-y · Cited by 57
Wang, E., Fu, Y., Guo, T., & Li, M. (2024) · Fuel · doi:10.1016/j.fuel.2024.132958 · Cited by 42
Darvishvand, L., Kamkari, B., Huang, M. J., & Hewitt, N. J. (2025) · Sustainable Cities and Society · doi:10.1016/j.scs.2025.106492 · Cited by 42
Naser, M. Z. (2025) · Computers & Industrial Engineering · doi:10.1016/j.cie.2025.111704 · Cited by 42
Moore, J. (2019) · Frontiers in Big Data · doi:10.3389/fdata.2019.00032 · Cited by 42
The grammar of interactive explanatory model analysis
Baniecki, H., Parzych, D., & Biecek, P. (2023) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-023-00924-w · Cited by 50
Shedding Light on the Black Box: Explaining Deep Neural Network Prediction of Clinical Outcomes
Shao, Y., Cheng, Y., Shah, R. U., Weir, C. R., Bray, B. E., & Zeng‐Treitler, Q. (2021) · Journal of Medical Systems · doi:10.1007/s10916-020-01701-8 · Cited by 45
Directive Explanations for Actionable Explainability in Machine Learning Applications
Singh, R., Miller, T., Lyons, H., Sonenberg, L., Velloso, E., Vetere, F., Howe, P. D. L., & Dourish, P. (2023) · ACM Transactions on Interactive Intelligent Systems · doi:10.1145/3579363 · Cited by 57
Hamon, R., Junklewitz, H., Malgieri, G., Hert, P. D., Beslay, L., & Sanchez, I. (2021) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3442188.3445917 · Cited by 42
Data Science for Economics and Finance
Consoli, S., Recupero, D. R., & Saisana, M. (2021) · Springer eBooks · doi:10.1007/978-3-030-66891-4 · Cited by 53
Interpretable modeling of genotype–phenotype landscapes with state-of-the-art predictive power
Tonner, P. D., Pressman, A., & Ross, D. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2114021119 · Cited by 41
On Explaining Decision TreesPreprint
Izza, Y., Ignatiev, A., & Marques-Silva, J. P. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.11034 · Cited by 107
Robust and Stable Black Box ExplanationsPreprint
Lakkaraju, H., Arsov, N., & Bastani, O. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2011.06169 · Cited by 45
The Explanation Game: A Formal Framework for Interpretable Machine Learning
Watson, D., & Floridi, L. (2021) · Philosophical studies series · doi:10.1007/978-3-030-81907-1_11 · Cited by 42
Deep Transfer Learning With Self-Attention for Industry Sensor Fusion Tasks
Zhang, Z., Farnsworth, M., Song, B., Tiwari, D., & Tiwari, A. (2022) · IEEE Sensors Journal · doi:10.1109/jsen.2022.3186505 · Cited by 41
Wang, T., & Li, Y. (2023) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2023.169164 · Cited by 42
Explainability as the key ingredient for AI adoption in Industry 5.0 settings
Agostinho, C., Dikopoulou, Z., Lavasa, E., Perakis, Κ., Pitsios, S., Branco, R., Reji, S., Hetterich, J., Biliri, E., Lampathaki, F., et al. (2023) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2023.1264372 · Cited by 41
Mohammed, S., & Malhotra, N. (2025) · BenchCouncil Transactions on Benchmarks Standards and Evaluations · doi:10.1016/j.tbench.2025.100215 · Cited by 53
Ferrone, L., & Zanzotto, F. M. (2020) · Frontiers in Robotics and AI · doi:10.3389/frobt.2019.00153 · Cited by 55
Isotope identification using deep learning: An explanation
Gomez-Fernandez, M., Wong, W., Tokuhiro, A., Welter, K. B., Alhawsawi, A. M., Yang, H., & Higley, K. A. (2020) · Nuclear Instruments and Methods in Physics Research Section A Accelerators Spectrometers Detectors and Associated Equipment · doi:10.1016/j.nima.2020.164925 · Cited by 44
SELFEXPLAIN: A Self-Explaining Architecture for Neural Text Classifiers
Rajagopal, D., Balachandran, V., Hovy, E. H., & Tsvetkov, Y. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.64 · Cited by 48
On the Tractability of Explaining Decisions of Classifiers
Cooper, M., & Marques-Silva, J. P. (2021) · Leibniz international proceedings in informatics · doi:10.4230/lipics.cp.2021.21 · Cited by 51
Evaluating Saliency Map Explanations for Convolutional Neural Networks: A User StudyPreprint
Alqaraawi, A., Schuessler, M., Weiß, P., Costanza, E., & Bianchi‐Berthouze, N. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.00772 · Cited by 41
Knowledge Graph Embeddings and Explainable AIPreprint
Bianchi, F., Rossiello, G., Costabello, L., Palmonari, M., & Minervini, P. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2004.14843 · Cited by 41
Machine Learning Explainability for External Stakeholders
Bhatt, U., Andrus, M., Weller, A., & Xiang, A. (2020) · arXiv · doi:10.48550/arxiv.2007.05408 · Cited by 41
Abduction-Based Explanations for Machine Learning Models
Ignatiev, A., Narodytska, N., & Marques-Silva, J. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33011511 · Cited by 40
Evaluating the Interpretability of Generative Models by Interactive Reconstruction
Ross, A., Chen, N., Hang, E. Z., Glassman, E. L., & Doshi-Velez, F. (2021) · Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445296 · Cited by 40
Fifty Years of Prolog and Beyond
KÖRNER, P., LEUSCHEL, M., BARBOSA, J., COSTA, V. S., DAHL, V., HERMENEGILDO, M. V., MORALES, J. F., WIELEMAKER, J., DIAZ, D., ABREU, S., et al. (2022) · Theory and Practice of Logic Programming · doi:10.1017/s1471068422000102 · Cited by 40
Hopkins, A., & Booth, S. (2021) · Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society · doi:10.1145/3461702.3462527 · Cited by 40
Behind the scenes: A medical natural language processing project
Wu, J. T.-Y., Dernoncourt, F., Gehrmann, S., Tyler, P. D., Moseley, E. T., Carlson, E. T., Grant, D. W., Li, Y., Welt, J. A., & Celi, L. A. (2017) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2017.12.003 · Cited by 40
Daniele, A., & Song, Y.-Z. (2019) · Proceedings of the AAAI/ACM Conference on AI Ethics and Society · doi:10.1145/3306618.3314233 · Cited by 56
Model Explanations with Differential Privacy
Patel, N., Shokri, R., & Zick, Y. (2022) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3531146.3533235 · Cited by 51
Black box algorithms and the rights of individuals: no easy solution to the “explainability” problem
Gryz, J., & Rojszczak, M. (2021) · Internet Policy Review · doi:10.14763/2021.2.1564 · Cited by 40
Many hands make many fingers to point: challenges in creating accountable AI
Slota, S. C., Fleischmann, K. R., Greenberg, S. R., Verma, N., Cummings, B., Li, L., & Shenefiel, C. (2021) · AI & Society · doi:10.1007/s00146-021-01302-0 · Cited by 50
Chan, M.-C., Pai, K.-C., Su, S.-A., Wang, M.-S., Wu, C., & Chao, W. (2022) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-022-01817-6 · Cited by 43
Leichtmann, B., Hinterreiter, A., Humer, C., Streit, M., & Mara, M. (2023) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2023.2221605 · Cited by 47
Accurate, interpretable predictions of materials properties within transformer language models
Korolev, V., & Protsenko, P. V. (2023) · Patterns · doi:10.1016/j.patter.2023.100803 · Cited by 44
Survey on ontology-based explainable AI in manufacturing
Naqvi, M. R., Elmhadhbi, L., Sarkar, A., Archimède, B., & Karray, M. H. (2024) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-023-02304-z · Cited by 45
Fuck, J. V. R., Cechinel, M. A. P., Neves, J., de Andrade, R. C., Tristão, R., Spogis, N., Riella, H. G., Soares, C., & Padoin, N. (2024) · Chemosphere · doi:10.1016/j.chemosphere.2024.141472 · Cited by 40
Alhussein, G., Ziogas, I., Saleem, S., & Hadjileontiadis, L. J. (2025) · Artificial Intelligence Review · doi:10.1007/s10462-025-11197-8 · Cited by 48
Dark Patterns of Explainability, Transparency, and User Control for Intelligent Systems.
Chromik, M., Eiband, M., Völkel, S. T., & Buschek, D. (2019) · IUI Workshops · Cited by 42
Fergus, P., Montañez, C. A. C., Abdulaimma, B., Lisboa, P. J. G., Chalmers, C., & Pineles, B. L. (2018) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2018.2868667 · Cited by 42
Aguasca-Colomo, R., Castellanos–Nieves, D., & Méndez, M. (2019) · Applied Sciences · doi:10.3390/app9224931 · Cited by 44
Explainable Student Agency Analytics
Saarela, M., Heilala, V., Jääskelä, P., Rantakaulio, A., & Kärkkäinen, T. (2021) · IEEE Access · doi:10.1109/access.2021.3116664 · Cited by 48
Machine Learning Based Approaches to Accelerate Energy Materials Discovery and Optimization
Krishnamurthy, D., Weiland, H., Farimani, A. B., Antono, E., Green, J., & Viswanathan, V. (2018) · ACS Energy Letters · doi:10.1021/acsenergylett.8b02278 · Cited by 41
Solanke, A. A. (2022) · Forensic Science International: Digital Investigation · doi:10.1016/j.fsidi.2022.301403 · Cited by 52
Metadata
- Authors
- Lipton, Z. C.
- Affiliations
- Carnegie Mellon University (US)
- Corresponding author
- Lipton, Zachary C.
- ORCID
- Lipton, Zachary C.
- Venue
- Queue
- Published
- 2018-06
- Volume
- 16
- Issue
- 3
- Pages
- 31-57
- ISSN
- 0001-0782 (ISSN-L)
- 1542-7730
- 1542-7749
- OpenAlex
- W2439568532
- Cited by
- 5,427
- References
- 25
- Citations by year
- 2026: 803 · 2025: 918 · 2024: 633 · 2023: 657 · 2022: 575 · 2021: 664 · 2020: 521 · 2019: 395 · 2018: 190
- FWCI
- 249.23
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Communications of the ACM (publishedVersion)
- Queue (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Artificial Intelligence › Adversarial Robustness in Machine Learning
- Keywords
- model interpretability, explainable AI, machine learning models
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
