Cited by 10,551Explainable Artificial Intelligence (XAI)Artificial Intelligence in HealthcareData Mining Algorithms and Applications

The DOI matches its Crossref record; 45 of the 45 references it cites were found in a registry or an index.

From local explanations to global understanding with explainable AI for trees

Open access

Lundberg, Scott M., Erion, Gabriel, Chen, Hugh, DeGrave, Alex, Prutkin, Jordan M., Nair, Bala, Katz, Ronit, Himmelfarb, Jonathan, Bansal, Nisha, Lee, Su-In·2020·Nature Machine Intelligence

DOI 10.1038/s42256-019-0138-9PMC PMC7326367

Open this paper in CoChat — free account

Abstract

Tree-based machine learning models such as random forests, decision trees, and gradient boosted trees are popular non-linear predictive models, yet comparatively little attention has been paid to explaining their predictions. Here, we improve the interpretability of tree-based models through three main contributions: 1) The first polynomial time algorithm to compute optimal explanations based on game theory. 2) A new type of explanation that directly measures local feature interaction effects. 3) A new set of tools for understanding global model structure based on combining many local explanations of each prediction. We apply these tools to three medical machine learning problems and show how combining many high-quality local explanations allows us to represent global structure while retaining local faithfulness to the original model. These tools enable us to i) identify high magnitude but low frequency non-linear mortality risk factors in the US population, ii) highlight distinct population sub-groups with shared risk characteristics, iii) identify non-linear interaction effects among risk factors for chronic kidney disease, and iv) monitor a machine learning model deployed in a hospital by identifying which features are degrading the model's performance over time. Given the popularity of tree-based machine learning models, these improvements to their interpretability have implications across a broad set of domains.

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

Crossref · 10.1038/s42256-019-0138-9

Cites

45 of 45 references verified0 citation errors · 0 not checkable

Statistics

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?

Self-check

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

The self-check needs the paper’s full text.

Random Forests
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
XGBoost: A Scalable Tree Boosting System
Chen, T., & Guestrin, C. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939785 · Cited by 54,569
A Unified Approach to Interpreting Model Predictions
Lundberg, S., & Lee, S.-I. (2017) · arXiv · doi:10.48550/arxiv.1705.07874 · Cited by 23,707
Self-citation
"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
On a Method to Measure Supervised Multiclass Model’s Interpretability: Application to Degradation Diagnosis (Short Paper)
Gauriat, C.-M., Pencolé, Y., Ribot, P., et al. (2024) · HAL · doi:10.4230/oasics.dx.2024.27 · Cited by 12,623
A Value for n-person Games
Shapley, L. (1988) · Cambridge University Press eBooks · doi:10.1017/cbo9780511528446.003 · Cited by 10,698
Heart Disease and Stroke Statistics—2016 Update: A Report From the American Heart Association
Mozaffarian, D., Benjamin, E. J., Go, A. S., Arnett, D. K., Blaha, M. J., Cushman, M., Das, S. R., de Ferranti, S., Després, J.-P., Fullerton, H. J., et al. (2016) · Circulation · doi:10.1161/cir.0000000000000350 · Cited by 6,596
A Randomized Trial of Intensive versus Standard Blood-Pressure Control
The SPRINT Research Group (2015) · New England Journal of Medicine · doi:10.1056/nejmoa1511939 · Cited by 6,325
Heart Disease and Stroke Statistics—2015 Update: A Report From the American Heart Association
Mozaffarian, D., Benjamin, E. J., Go, A. S., Arnett, D. K., Blaha, M. J., Cushman, M., de Ferranti, S., Després, J.-P., Fullerton, H. J., Howard, V. J., et al. (2015) · Circulation · doi:10.1161/cir.0000000000000152 · Cited by 5,797
Repeated observation of breast tumor subtypes in independent gene expression data sets
Sørlie, T., Tibshirani, R. J., Parker, J. S., Hastie, T. J., Marron, J. S., Nobel, A. B., Deng, S., Johnsen, H., Pesich, R., Geisler, S. B., et al. (2003) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0932692100 · Cited by 5,508

Show all 38 references

Sorted by citations

Items 301–400 of 1,032

Development and Validation of a Machine Learning Model Using Administrative Health Data to Predict Onset of Type 2 Diabetes
Ravaut, M., Harish, V., Sadeghi, H., Leung, K. K., Volkovs, M., Kornas, K., Watson, T., Poutanen, T., & Rosella, L. C. (2021) · JAMA Network Open · doi:10.1001/jamanetworkopen.2021.11315 · Cited by 97
Using explainable machine learning to understand how urban form shapes sustainable mobility
Wagner, F., Milojevic-Dupont, N., Franken, L., Zekar, A., Thies, B., Koch, N., & Creutzig, F. (2022) · Transportation Research Part D Transport and Environment · doi:10.1016/j.trd.2022.103442 · Cited by 89
Quod erat demonstrandum? - Towards a typology of the concept of explanation for the design of explainable AI
Cabitza, F., Campagner, A., Malgieri, G., Natali, C., Schneeberger, D., Stoeger, K., & Holzinger, A. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.118888 · Cited by 89
Compressive strength prediction of one-part alkali activated material enabled by interpretable machine learning
Shah, S. F. A., Chen, B., Zahid, M., & Ahmad, M. R. (2022) · Construction and Building Materials · doi:10.1016/j.conbuildmat.2022.129534 · Cited by 95
Exposing and understanding synergistic effects in co-pyrolysis of biomass and plastic waste via machine learning
Prasertpong, P., Onsree, T., Khuenkaeo, N., Tippayawong, N., & Lauterbach, J. A. (2022) · Bioresource Technology · doi:10.1016/j.biortech.2022.128419 · Cited by 89
Privacy-preserving edge federated learning for intelligent mobile-health systems
Aminifar, A., Shokri, M., & Aminifar, A. (2024) · Future Generation Computer Systems · doi:10.1016/j.future.2024.07.035 · Cited by 106
An explainable machine learning model for predicting in‐hospital amputation rate of patients with diabetic foot ulcer
Xie, P., Li, Y., Deng, B., Du, C., Rui, S., Deng, W., Wang, M., Boey, J., Armstrong, D. G., Ma, Y., et al. (2021) · International Wound Journal · doi:10.1111/iwj.13691 · Cited by 89
Generation of a more efficient prime editor 2 by addition of the Rad51 DNA-binding domain
Song, M., Lim, J. M., Min, S., Oh, J., Kim, D. Y., Woo, J., Nishimasu, H., Cho, S., Yoon, S., & Kim, S. (2021) · Nature Communications · doi:10.1038/s41467-021-25928-2 · Cited by 88
Machine learning based brain signal decoding for intelligent adaptive deep brain stimulation
Merk, T., Peterson, V., Köhler, R. M., Haufe, S., Richardson, R. M., & Neumann, W. (2022) · Experimental Neurology · doi:10.1016/j.expneurol.2022.113993 · Cited by 87
Metal–Organic Framework Stability in Water and Harsh Environments from Data-Driven Models Trained on the Diverse WS24 Data Set
Terrones, G., Huang, S., Rivera, M. P., Yue, S., Hernandez, A., & Kulik, H. J. (2024) · Journal of the American Chemical Society · doi:10.1021/jacs.4c05879 · Cited by 91
Quantifying the main and interactive effects of the dominant factors on the diurnal cycles of land surface temperature in typical urban functional zones
Chen, J., Wang, K., Du, P., Zang, Y., Zhang, P., Xia, J., Chen, C., & Yu, Z. (2024) · Sustainable Cities and Society · doi:10.1016/j.scs.2024.105727 · Cited by 90
Opening the Black Box: A systematic review on explainable artificial intelligence in remote sensing
Höhl, A., Obadić, I., Fernández‐Torres, M., Najjar, H., Oliveira, D. A. B., Akata, Z., Dengel, A. R., & Zhu, X. X. (2024) · IEEE Geoscience and Remote Sensing Magazine · doi:10.1109/mgrs.2024.3467001 · Cited by 116
Explanation of Machine-Learning Solutions in Air-Traffic Management
Xie, Y., Pongsakornsathien, N., Gardi, A., & Sabatini, R. (2021) · Aerospace · doi:10.3390/aerospace8080224 · Cited by 89
iAFPs-EnC-GA: Identifying antifungal peptides using sequential and evolutionary descriptors based multi-information fusion and ensemble learning approach
Ahmad, A., Akbar, S., Tahir, M., Hayat, M., & Ali, F. (2022) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/j.chemolab.2022.104516 · Cited by 87
Converting Nanotoxicity Data to Information Using Artificial Intelligence and Simulation
Yan, X., Yue, T., Winkler, D. A., Yin, Y., Zhu, H., Jiang, G., & Yan, B. (2023) · Chemical Reviews · doi:10.1021/acs.chemrev.3c00070 · Cited by 89
Targeted design of synthetic enhancers for selected tissues in the Drosophila embryo
de Almeida, B. P., Schaub, C., Pagani, M., Secchia, S., Furlong, E. E. M., & Stark, A. (2024) · Nature · doi:10.1038/s41586-023-06905-9 · Cited by 87
Making Sense of Machine Learning: A Review of Interpretation Techniques and Their Applications
Tursunalieva, A., Alexander, D. L., Dunne, R. A., Li, J., Riera, L., & Zhao, Y. (2024) · Applied Sciences · doi:10.3390/app14020496 · Cited by 102
Nanofiltration Membranes for Efficient Lithium Extraction from Salt-Lake Brine: A Critical Review
Yong, M. S., Yang, Y., Sun, L., Tang, M., Wang, Z., Xing, C., Hou, J., Zheng, M., Chui, T. F. M., Li, Z., et al. (2024) · ACS Environmental Au · doi:10.1021/acsenvironau.4c00061 · Cited by 96
Predicting intergranular stress corrosion cracking of stainless steels in high-temperature water by incorporating crystallographic factor
Zhou, Y., Liu, J., Chen, K., Shi, H., Shang, X., Que, Z., Shen, Z., Zhang, L., & Zeng, X. (2025) · Acta Materialia · doi:10.1016/j.actamat.2025.121322 · Cited by 87
Explainable Machine Learning for Property Predictions in Compound Optimization
Rodríguez-Pérez, R., & Bajorath, J. (2021) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.1c01789 · Cited by 88
Gut microbiome modulates the effects of a personalised postprandial-targeting (PPT) diet on cardiometabolic markers: a diet intervention in pre-diabetes
Ben-Yacov, O., Godneva, A., Rein, M., Shilo, S., Lotan‐Pompan, M., Weinberger, A., & Segal, E. (2023) · Gut · doi:10.1136/gutjnl-2022-329201 · Cited by 93
Universal machine learning aided synthesis approach of two-dimensional perovskites in a typical laboratory
Wu, Y., Wang, C., Ju, M., Jia, Q., Zhou, Q., Lu, S., Gao, X., Zhang, Y., & Wang, J. (2024) · Nature Communications · doi:10.1038/s41467-023-44236-5 · Cited by 94
A Clinically Interpretable Computer-Vision Based Method for Quantifying Gait in Parkinson’s Disease
Rupprechter, S., Morinan, G., Peng, Y., Foltynie, T., Sibley, K. G., Weil, R. S., Leyland, L., Baig, F., Morgante, F., Gilron, R., et al. (2021) · Sensors · doi:10.3390/s21165437 · Cited by 85
On the interpretability of machine learning methods in crash frequency modeling and crash modification factor development
Wen, X., Xie, Y., Jiang, L., Li, Y., & Ge, T. (2022) · Accident Analysis & Prevention · doi:10.1016/j.aap.2022.106617 · Cited by 85
Features predisposing forest to bark beetle outbreaks and their dynamics during drought
Müller, M., Olsson, P.-O., Eklundh, L., Jamali, S., & Ardö, J. (2022) · Forest Ecology and Management · doi:10.1016/j.foreco.2022.120480 · Cited by 93
Shapley Values for Explaining the Black Box Nature of Machine Learning Model Clustering
Louhichi, M., Nesmaoui, R., Marwan, M., & Lazaar, M. (2023) · Procedia Computer Science · doi:10.1016/j.procs.2023.03.107 · Cited by 96
Evidence of a causal and modifiable relationship between kidney function and circulating trimethylamine N-oxide
Andrikopoulos, P., Aron‐Wisnewsky, J., Chakaroun, R., Myridakis, A., Forslund, S. K., Nielsen, T., Adriouch, S., Holmes, B., Chilloux, J., Vieira‐Silva, S., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-39824-4 · Cited by 88
Explainable artificial intelligence for microbiome data analysis in colorectal cancer biomarker identification
Novielli, P., Romano, D., Magarelli, M., Bitonto, P. D., Diacono, D., Chiatante, A., Lopalco, G., Sabella, D. V. A., Venerito, V., Filannino, P., et al. (2024) · Frontiers in Microbiology · doi:10.3389/fmicb.2024.1348974 · Cited by 94
Agricultural practices influence soil microbiome assembly and interactions at different depths identified by machine learning
Mo, Y., Bier, R. L., Li, X., Daniels, M. D., Smith, A., Yu, L., & Kan, J. (2024) · Communications Biology · doi:10.1038/s42003-024-07059-8 · Cited by 97
Probing out-of-distribution generalization in machine learning for materials
Li, K., Rubungo, A. N., Lei, X., Persaud, D., Choudhary, K. K., DeCost, B., Dieng, A. B., & Hattrick-Simpers, J. R. (2025) · Communications Materials · doi:10.1038/s43246-024-00731-w · Cited by 110
FairLens: Auditing black-box clinical decision support systems
Panigutti, C., Perotti, A., Panisson, A., Bajardi, P., & Pedreschi, D. (2021) · Information Processing & Management · doi:10.1016/j.ipm.2021.102657 · Cited by 86
Machine Learning Approach to Predicting COVID-19 Disease Severity Based on Clinical Blood Test Data: Statistical Analysis and Model Development
Aktar, S., Ahamad, M. M., Rashed-Al-Mahfuz, M., Azad, A., Uddin, S., Kamal, A. H. M., Alyami, S. A., Lin, P., Islam, S. M. S., Quinn, J. M. W., et al. (2021) · JMIR Medical Informatics · doi:10.2196/25884 · Cited by 84
An explainable machine learning model to predict and elucidate the compressive behavior of high-performance concrete
Chakraborty, D., Awolusi, I., & Gutierrez, L. (2021) · Results in Engineering · doi:10.1016/j.rineng.2021.100245 · Cited by 84
Advancing material property prediction: using physics-informed machine learning models for viscosity
Chew, A. K., Sender, M., Kaplan, Z., Chandrasekaran, A. R., Elk, J. C., Browning, A. R., Kwak, H. S., Halls, M. D., & Afzal, M. A. F. (2024) · Journal of Cheminformatics · doi:10.1186/s13321-024-00820-5 · Cited by 84
Leveraging machine learning to streamline the development of liposomal drug delivery systems
Eugster, R., Orsi, M., Buttitta, G., Serafini, N., Tiboni, M., Casettari, L., Reymond, J., Aleandri, S., & Luciani, P. (2024) · Journal of Controlled Release · doi:10.1016/j.jconrel.2024.10.065 · Cited by 85
Understanding the relationship between 2D/3D variables and land surface temperature in plain and mountainous cities: Relative importance and interaction effects
Luo, P., Yu, B., Li, P., Liang, P., Zhang, Q., & Yang, L. (2023) · Building and Environment · doi:10.1016/j.buildenv.2023.110959 · Cited by 87
Artificial intelligence and food flavor: How AI models are shaping the future and revolutionary technologies for flavor food development
Cui, Z., Qi, C., Zhou, T., Yu, Y., Wang, Y., Zhang, Z., Zhang, Y., Wang, W., & Liu, Y. (2025) · Comprehensive Reviews in Food Science and Food Safety · doi:10.1111/1541-4337.70068 · Cited by 89
Explainable Machine Learning Approach as a Tool to Understand Factors Used to Select the Refractive Surgery Technique on the Expert Level
Yoo, T. K., Ryu, I. H., Choi, H., Kim, J. K., Lee, I. S., Kim, J. S., Lee, G., & Rim, T. H. (2020) · Translational Vision Science & Technology · doi:10.1167/tvst.9.2.8 · Cited by 89
Integrating satellite imagery and environmental data to predict field-level cane and sugar yields in Australia using machine learning
Shendryk, Y., Davy, R. J., & Thorburn, P. J. (2020) · Field Crops Research · doi:10.1016/j.fcr.2020.107984 · Cited by 82
Validity evaluation of a machine-learning model for chlorophyll a retrieval using Sentinel-2 from inland and coastal waters
Kim, Y. W., Kim, T., Shin, J., Lee, D., Park, Y., Kim, Y., & Cha, Y. (2022) · Ecological Indicators · doi:10.1016/j.ecolind.2022.108737 · Cited by 85
Toward the explainability, transparency, and universality of machine learning for behavioral classification in neuroscience
Goodwin, N. L., Nilsson, S. R. O., Choong, J. J., & Golden, S. A. (2022) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2022.102544 · Cited by 85
Measuring the model risk-adjusted performance of machine learning algorithms in credit default prediction
Alonso-Robisco, A., & Carbó, J. M. (2022) · Financial Innovation · doi:10.1186/s40854-022-00366-1 · Cited by 107
Mood ratings and digital biomarkers from smartphone and wearable data differentiates and predicts depression status: A longitudinal data analysis
Asare, K. O., Moshe, I., Terhorst, Y., Vega, J., Hosio, S. J., Baumeister, H., Pulkki-Råbäck, L., & Ferreira, D. (2022) · Pervasive and Mobile Computing · doi:10.1016/j.pmcj.2022.101621 · Cited by 86
Explainable artificial intelligence modeling to forecast bitcoin prices
Goodell, J. W., Jabeur, S. B., Saâdaoui, F., & Nasir, M. A. (2023) · International Review of Financial Analysis · doi:10.1016/j.irfa.2023.102702 · Cited by 93
A Survey of Explainable Artificial Intelligence (XAI) in Financial Time Series Forecasting
Arsenault, P.-D., Wang, S., & Patenaude, J.-M. (2025) · ACM Computing Surveys · doi:10.1145/3729531 · Cited by 120
“That's (not) the output I expected!” On the role of end user expectations in creating explanations of AI systems
Riveiro, M., & Thill, S. (2021) · Artificial Intelligence · doi:10.1016/j.artint.2021.103507 · Cited by 93
Implementing local-explainability in Gradient Boosting Trees: Feature Contribution
Delgado-Panadero, Á., Hernández-Lorca, B., García-Ordás, M. T., & Benítez‐Andrades, J. A. (2022) · Information Sciences · doi:10.1016/j.ins.2021.12.111 · Cited by 81
Prediction and diagnosis of depression using machine learning with electronic health records data: a systematic review
Nickson, D., Meyer, C., Walasek, L., & Toro, C. T. (2023) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-023-02341-x · Cited by 99
Machine learning-based prediction and experimental validation of heavy metal adsorption capacity of bentonite
Guo, L., Xu, X., Niu, C., Wang, Q., Park, J., Zhou, L., Lei, H., Wang, X., & Yuan, X. (2024) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2024.171986 · Cited by 81
A Survey on Trustworthy Edge Intelligence: From Security and Reliability to Transparency and Sustainability
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 101
An Enhanced Ensemble Learning-Based Fault Detection and Diagnosis for Grid-Connected PV Systems
Dhibi, K., Mansouri, M. M., Bouzrara, K., Nounou, H. N., & Nounou, M. N. (2021) · IEEE Access · doi:10.1109/access.2021.3128749 · Cited by 81
The Pandemic's Toll on Young Adolescents: Prevention and Intervention Targets to Preserve Their Mental Health
Kiss, O., Alzueta, E., Yüksel, D., Pohl, K. M., de Zambotti, M., Müller‐Oehring, E. M., Prouty, D., Durley, I., Pelham, W. E., McCabe, C. J., et al. (2022) · Journal of Adolescent Health · doi:10.1016/j.jadohealth.2021.11.023 · Cited by 88
Grading by AI makes me feel fairer? How different evaluators affect college students’ perception of fairness
Chai, F., Ma, J., Wang, Y., Zhu, J., & Han, T. (2024) · Frontiers in Psychology · doi:10.3389/fpsyg.2024.1221177 · Cited by 103
HergSPred: Accurate Classification of hERG Blockers/Nonblockers with Machine-Learning Models
Zhang, X., Mao, J., Wei, M., Qi, Y., & Zhang, J. Z. H. (2022) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.2c00256 · Cited by 77
Deciphering the influence of TOD on metro ridership: An integrated approach of extended node-place model and interpretable machine learning with planning implications
Su, S., Wang, Z., Li, B., & Kang, M. (2022) · Journal of Transport Geography · doi:10.1016/j.jtrangeo.2022.103455 · Cited by 79
Features from the photoplethysmogram and the electrocardiogram for estimating changes in blood pressure
Finnegan, E., Davidson, S. M., Harford, M., Watkinson, P., Tarassenko, L., & Villarroel, M. (2023) · Scientific Reports · doi:10.1038/s41598-022-27170-2 · Cited by 87
Machine learning reveals the complex ecological interplay of microbiome in a full-scale membrane bioreactor wastewater treatment plant
Wijaya, J., & Oh, S. (2023) · Environmental Research · doi:10.1016/j.envres.2023.115366 · Cited by 79
An interpretable machine learning approach to multimodal stress detection in a simulated office environment
Naegelin, M., Weibel, R. P., Kerr, J. I., Schinazi, V. R., Marca, R. L., von Wangenheim, F., Hölscher, C., & Ferrario, A. (2023) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2023.104299 · Cited by 83
Identification and attribution analysis of integrated ecological zones based on the XGBoost-SHAP model: A case study of Chengdu, China
Huang, X., Liu, X., Jin, Y., Gao, X., & Chen, Y. (2025) · Ecological Indicators · doi:10.1016/j.ecolind.2025.113787 · Cited by 79
NBDT: Neural-Backed Decision TreesPreprint
Wan, A., Dunlap, L., Ho, D., Yin, J., Lee, S., Jin, H., Petryk, S., Bargal, S. A., & Gonzalez, J. E. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2004.00221 · Cited by 137
Self-citation
Data Analytics for Non-Life Insurance PricingPreprint
Wüthrich, M. V., & Buser, C. (2017) · SSRN Electronic Journal · doi:10.2139/ssrn.2870308 · Cited by 87
Artificial Intelligence for Quantitative Modeling in Drug Discovery and Development: An Innovation and Quality Consortium Perspective on Use Cases and Best Practices
Terranova, N., Renard, D., Shahin, M. H., Menon, S., Cao, Y., Hop, C. E. C. A., Hayes, S., Madrasi, K., Stodtmann, S., Tensfeldt, T., et al. (2024) · Clinical Pharmacology & Therapeutics · doi:10.1002/cpt.3053 · Cited by 78
Alibi Explain: Algorithms for Explaining Machine Learning Models
Klaise, J., Looveren, A. V., Vacanti, G., & Coca, A. (2021) · Journal of Machine Learning Research · Cited by 78
Provenance documentation to enable explainable and trustworthy AI: A literature review
Kale, A., Nguyen, T., Harris, F. C., Li, C., Zhang, J., & Ma, X. (2022) · Data Intelligence · doi:10.1162/dint_a_00119 · Cited by 96
Direct Prediction of Bioaccumulation of Organic Contaminants in Plant Roots from Soils with Machine Learning Models Based on Molecular Structures
Gao, F., Shen, Y., Sallach, J. B., Li, H., Liu, C., & Li, Y. (2021) · Environmental Science & Technology · doi:10.1021/acs.est.1c02376 · Cited by 78
Leveraging explainable AI for gut microbiome-based colorectal cancer classification
Rynazal, R., Fujisawa, K., Shiroma, H., Salim, F., Mizutani, S., Shiba, S., Yachida, S., & Yamada, T. (2023) · Genome biology · doi:10.1186/s13059-023-02858-4 · Cited by 78
Snow avalanche susceptibility mapping using novel tree-based machine learning algorithms (XGBoost, NGBoost, and LightGBM) with eXplainable Artificial Intelligence (XAI) approach
İban, M. C., & Bilgilioğlu, S. S. (2023) · Stochastic Environmental Research and Risk Assessment · doi:10.1007/s00477-023-02392-6 · Cited by 78
ExplaiNAble BioLogical Age (ENABL Age): an artificial intelligence framework for interpretable biological age
Qiu, W., Chen, H., Kaeberlein, M. R., & Lee, S. (2023) · The Lancet Healthy Longevity · doi:10.1016/s2666-7568(23)00189-7 · Cited by 90
Self-citation
Revealing drivers and risks for power grid frequency stability with explainable AIPreprint
Kruse, J., Sch\"afer, B., & Witthaut, D. (2021) · arXiv (Cornell University) · doi:10.1016/j.patter.2021.100365 · Cited by 78
Analysis of Updated Literature Data up to 2019 on the Oxidative Coupling of Methane Using an Extrapolative Machine‐Learning Method to Identify Novel Catalysts
Mine, S., Takao, M., Yamaguchi, T., Toyao, T., Maeno, Z., Siddiki, S. M. A. H., Takakusagi, S., Shimizu, K., & Takigawa, I. (2021) · ChemCatChem · doi:10.1002/cctc.202100495 · Cited by 78
Overview of Explainable Artificial Intelligence for Prognostic and Health Management of Industrial Assets Based on Preferred Reporting Items for Systematic Reviews and Meta-Analyses
Nor, A. K. M., Pedapati, S. R., Muhammad, M. B., & Leiva, V. (2021) · Sensors · doi:10.3390/s21238020 · Cited by 86
Interpretable machine-learning model with a collaborative game approach to predict yields and higher heating value of torrefied biomass
Onsree, T., Tippayawong, N., Phithakkitnukoon, S., & Lauterbach, J. A. (2022) · Energy · doi:10.1016/j.energy.2022.123676 · Cited by 77
Hybrid gradient boosting with meta-heuristic algorithms prediction of unconfined compressive strength of stabilized soil based on initial soil properties, mix design and effective compaction
Tran, V. Q. (2022) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2022.131683 · Cited by 77
Explainable machine learning for the prediction and assessment of complex drought impacts
Zhang, B., Salem, F. K. A., Hayes, M. J., Smith, K. H., Tadesse, T., & Wardlow, B. D. (2023) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2023.165509 · Cited by 77
Complimentary Computational Cues for Water Electrocatalysis: A DFT and ML Perspective
Badreldin, A., Bouhali, O., & Abdel‐Wahab, A. (2023) · Advanced Functional Materials · doi:10.1002/adfm.202312425 · Cited by 78
Explainable machine learning methods for predicting water treatment plant features under varying weather conditions
Saleem, M. A., Harrou, F., & Sun, Y. (2024) · Results in Engineering · doi:10.1016/j.rineng.2024.101930 · Cited by 79
Robust network anomaly detection using ensemble learning approach and explainable artificial intelligence (XAI)
Hooshmand, M. K., Manjaiah, D. H., Alzighaibi, A. R., Hashim, H. A., Atlam, E.-S., & Gad, I. (2024) · Alexandria Engineering Journal · doi:10.1016/j.aej.2024.03.041 · Cited by 77
Explaining Explanations: Axiomatic Feature Interactions for Deep NetworksPreprint
Janizek, J. D., Sturmfels, P., & Lee, S. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.04138 · Cited by 95
Self-citation
Modeling and interpreting hydrological responses of sustainable urban drainage systems with explainable machine learning methods
Yang, Y., & Chui, T. F. M. (2021) · Hydrology and earth system sciences · doi:10.5194/hess-25-5839-2021 · Cited by 81
Forecasting the Olympic medal distribution – A socioeconomic machine learning model
Schlembach, C., Schmidt, S. L., Schreyer, D., & Wunderlich, L. (2021) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2021.121314 · Cited by 76
Conceptualising fairness: three pillars for medical algorithms and health equity
Sikstrom, L., Maslej, M. M., Hui, K., Findlay, Z., Buchman, D. Z., & Hill, S. L. (2022) · BMJ Health & Care Informatics · doi:10.1136/bmjhci-2021-100459 · Cited by 76
Interpretable machine learning for accelerating the discovery of metal-organic frameworks for ethane/ethylene separation
Wang, Z., Zhou, T., & Sundmacher, K. (2022) · Chemical Engineering Journal · doi:10.1016/j.cej.2022.136651 · Cited by 76
Transfer learning for non-image data in clinical research: A scoping review
Ebbehøj, A. L., Thunbo, M. Ø., Andersen, O. E., Glindtvad, M. V., & Hulmán, Á. (2022) · PLOS Digital Health · doi:10.1371/journal.pdig.0000014 · Cited by 83
Improving preeclampsia risk prediction by modeling pregnancy trajectories from routinely collected electronic medical record data
Li, S., Wang, Z., Vieira, L., Zheutlin, A. B., Ru, B., Schadt, E., Wang, P., Copperman, A. B., Stone, J. L., Gross, S. J., et al. (2022) · npj Digital Medicine · doi:10.1038/s41746-022-00612-x · Cited by 73
Active learning streamlines development of high performance catalysts for higher alcohol synthesis
Suvarna, M., Zou, T., Chong, S. H., Ge, Y., Martín, A. J., & Pérez‐Ramírez, J. (2024) · Nature Communications · doi:10.1038/s41467-024-50215-1 · Cited by 93
Predicting breast cancer risk using interacting genetic and demographic factors and machine learning
Behravan, H., Hartikainen, J. M., Tengström, M., Kosma, V., & Mannermaa, A. (2020) · Scientific Reports · doi:10.1038/s41598-020-66907-9 · Cited by 77
Predicting Driver Takeover Time in Conditionally Automated Driving
Ayoub, J., Du, N., Yang, X. J., & Zhou, F. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2022.3154329 · Cited by 75
The Role of Explainability in Assuring Safety of Machine Learning in Healthcare
Jia, Y., McDermid, J. A., Lawton, T., & Habli, I. (2022) · IEEE Transactions on Emerging Topics in Computing · doi:10.1109/tetc.2022.3171314 · Cited by 93
State-of-health estimation of Li-ion batteries in the early phases of qualification tests: An interpretable machine learning approach
Lee, G., Kim, J., & Lee, C. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.116817 · Cited by 75
Conceptual challenges for interpretable machine learning
Watson, D. S. (2022) · Synthese · doi:10.1007/s11229-022-03485-5 · Cited by 87
Unveiling “Sabatier principle” for electrocatalytic nitric oxide reduction on single cluster catalysts: A DFT and machine learning guideline
Yang, L., Fan, J., Xiao, B., & Zhu, W. (2023) · Chemical Engineering Journal · doi:10.1016/j.cej.2023.143823 · Cited by 75
Does institutional quality affect CO2 emissions? Evidence from explainable artificial intelligence models
Stef, N., Başağaoğlu, H., Chakraborty, D., & Jabeur, S. B. (2023) · Energy Economics · doi:10.1016/j.eneco.2023.106822 · Cited by 76
Counterfactual Shapley Additive Explanations
Albini, E., Long, J., Dervovic, D., & Magazzeni, D. (2022) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3531146.3533168 · Cited by 86
Using an Explainable Machine Learning Approach to Characterize Earth System Model Errors: Application of SHAP Analysis to Modeling Lightning Flash Occurrence
Silva, S. J., Keller, C. A., & Hardin, J. C. (2022) · Journal of Advances in Modeling Earth Systems · doi:10.1029/2021ms002881 · Cited by 74
A robust framework to investigate the reliability and stability of explainable artificial intelligence markers of Mild Cognitive Impairment and Alzheimer’s Disease
Lombardi, A., Diacono, D., Amoroso, N., Biecek, P., Monaco, A., Bellantuono, L., Pantaleo, E., Logroscino, G., Blasi, R. D., Tangaro, S., et al. (2022) · Brain Informatics · doi:10.1186/s40708-022-00165-5 · Cited by 82
GlassNet: A multitask deep neural network for predicting many glass properties
Cassar, D. R. (2023) · Ceramics International · doi:10.1016/j.ceramint.2023.08.281 · Cited by 77
A k-additive Choquet integral-based approach to approximate the SHAP values for local interpretability in machine learning
Pelegrina, G. D., Duarte, L. T., & Michel Grabisch, B. (2023) · Artificial Intelligence · doi:10.1016/j.artint.2023.104014 · Cited by 82
A machine-learning-derived online prediction model for depression risk in COPD patients: A retrospective cohort study from CHARLS
Zhao, X., Wang, Y., Li, J., Liu, W., Yang, Y., Qiao, Y., Liao, J., Chen, M., Li, D., Wu, B., et al. (2025) · Journal of Affective Disorders · doi:10.1016/j.jad.2025.02.063 · Cited by 79
True to the Model or True to the Data?Preprint
Chen, H., Janizek, J. D., Lundberg, S., & Lee, S. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.16234 · Cited by 111
Self-citation
Man vs. Machine: Quantitative and Discretionary Equity ManagementPreprint
Abis, S. (2020) · SSRN Electronic Journal · doi:10.2139/ssrn.3717371 · Cited by 74

Authors: Lundberg, S. M., Erion, G., Chen, H., DeGrave, A., Prutkin, J. M., Nair, B., Katz, R., Himmelfarb, J., Bansal, N., & Lee, S.-I.
Affiliations: Microsoft (United States) (US)
University of Washington (US)
Corresponding author: Lee, Su-In
ORCID: Lundberg, Scott M.
Erion, Gabriel
Chen, Hugh
DeGrave, Alex
Prutkin, Jordan M.
Nair, Bala
Katz, Ronit
Himmelfarb, Jonathan
Bansal, Nisha
Lee, Su-In
Venue: Nature Machine Intelligence
Publisher: Springer Science and Business Media LLC
Published: 2020-01-17
Volume: 2
Issue: 1
Pages: 56-67
ISSN: 2522-5839 (ISSN-L)
DOI: 10.1038/s42256-019-0138-9
PMC: PMC7326367
OpenAlex: W2999615587
Cited by: 10,551
References: 38
Citations by year: 2026: 3607 · 2025: 2330 · 2024: 1650 · 2023: 1197 · 2022: 959 · 2021: 614 · 2020: 187
FWCI: 291.51
Type: journal article
Language: en
Open access: green
www.ncbi.nlm.nih.gov
Licence: www.springernature.com/gp/researchers/text-and-data-mining (tdm)
www.springernature.com/gp/researchers/text-and-data-mining (vor)
Locations: Nature Machine Intelligence (publishedVersion)
PubMed (publishedVersion)
PubMed Central (acceptedVersion)
Funders: National Science Foundation (DGE-1256082, DBI-1759487, DBI-1552309, DBI-1355899)
American Cancer Society (127332-RSG-15-097-01-TBG)
U.S. Department of Health & Human Services | National Institutes of Health (R35 GM 128638, R01 NIA AG 061132)
Topics: Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
Physical Sciences › Computer Science › Information Systems › Data Mining Algorithms and Applications
Keywords: explainable AI, tree-based models, local explanations, mortality risk, chronic kidney disease, model monitoring, machine learning in medicine

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

From local explanations to global understanding with explainable AI for trees

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks