{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,19]],"date-time":"2026-08-19T17:58:43Z","timestamp":1787162323207,"version":"build-2736575974"},"reference-count":46,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2025,10,4]],"date-time":"2025-10-04T00:00:00Z","timestamp":1759536000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,10,4]],"date-time":"2025-10-04T00:00:00Z","timestamp":1759536000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Dyn Games Appl"],"published-print":{"date-parts":[[2026,7]]},"DOI":"10.1007\/s13235-025-00670-2","type":"journal-article","created":{"date-parts":[[2025,10,4]],"date-time":"2025-10-04T09:21:14Z","timestamp":1759569674000},"page":"1013-1050","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["The Shapley Value Contribution to Explainable Artificial Intelligence: A Comprehensive Survey"],"prefix":"10.1007","volume":"16","author":[{"given":"Chi","family":"Zhao","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jing","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Elena","family":"Parilina","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,10,4]]},"reference":[{"issue":"3","key":"670_CR1","doi-asserted-by":"publisher","first-page":"362","DOI":"10.1002\/rob.21918","volume":"37","author":"S Grigorescu","year":"2020","unstructured":"Grigorescu S, Trasnea B, Cocias T, Macesanu G (2020) A survey of deep learning techniques for autonomous driving. J Field Robot 37(3):362\u2013386","journal-title":"J Field Robot"},{"issue":"1","key":"670_CR2","doi-asserted-by":"publisher","first-page":"6","DOI":"10.1186\/s13037-019-0188-2","volume":"13","author":"LA Lynn","year":"2019","unstructured":"Lynn LA (2019) Artificial intelligence systems for complex decision-making in acute care medicine: a review. Patient Saf Surg 13(1):6","journal-title":"Patient Saf Surg"},{"key":"670_CR3","doi-asserted-by":"publisher","first-page":"491","DOI":"10.1109\/TEM.2021.3117884","volume":"71","author":"S Malodia","year":"2021","unstructured":"Malodia S, Islam N, Kaur P, Dhir A (2021) Why do people use Artificial Intelligence (AI)-enabled voice assistants? IEEE Trans Eng Manage 71:491\u2013505","journal-title":"IEEE Trans Eng Manage"},{"issue":"3","key":"670_CR4","doi-asserted-by":"publisher","first-page":"402","DOI":"10.3390\/smartcities2030025","volume":"2","author":"X Guo","year":"2019","unstructured":"Guo X, Shen Z, Zhang Y, Wu T (2019) Review on the application of artificial intelligence in smart homes. Smart Cities 2(3):402\u2013420","journal-title":"Smart Cities"},{"key":"670_CR5","doi-asserted-by":"publisher","first-page":"46","DOI":"10.1016\/j.futures.2017.03.006","volume":"90","author":"S Makridakis","year":"2017","unstructured":"Makridakis S (2017) The forthcoming Artificial Intelligence (AI) revolution: Its impact on society and firms. Futures 90:46\u201360","journal-title":"Futures"},{"key":"670_CR6","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1016\/j.inffus.2019.12.012","volume":"58","author":"AB Arrieta","year":"2020","unstructured":"Arrieta AB, D\u00edaz-Rodr\u00edguez N, Ser J, Bennetot A, Tabik S, Barbado A, Garc\u00eda S, Gil-L\u00f3pez S, Molina D, Benjamins R et al (2020) Explainable Artificial Intelligence (XAI): concepts, taxonomies, opportunities and challenges toward responsible AI. Inf Fusion 58:82\u2013115","journal-title":"Inf Fusion"},{"key":"670_CR7","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman L (2001) Random forests. Mach Learn 45:5\u201332","journal-title":"Mach Learn"},{"issue":"10","key":"670_CR8","doi-asserted-by":"publisher","first-page":"1340","DOI":"10.1093\/bioinformatics\/btq134","volume":"26","author":"A Altmann","year":"2010","unstructured":"Altmann A, Tolo\u015fi L, Sander O, Lengauer T (2010) Permutation importance: a corrected feature importance measure. Bioinformatics 26(10):1340\u20131347","journal-title":"Bioinformatics"},{"key":"670_CR9","doi-asserted-by":"crossref","unstructured":"Ribeiro MT, Singh S, Guestrin C (2016) \u201cWhy should I trust you?\" Explaining the predictions of any classifier. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp 1135\u20131144","DOI":"10.1145\/2939672.2939778"},{"key":"670_CR10","doi-asserted-by":"crossref","unstructured":"Datta A, Sen S, Zick Y (2016) Algorithmic transparency via quantitative input influence: theory and experiments with learning systems. In: 2016 IEEE symposium on security and privacy (SP), pp 598\u2013617","DOI":"10.1109\/SP.2016.42"},{"key":"670_CR11","unstructured":"Lundberg SM, Lee S-I (2017) A unified approach to interpreting model predictions. In: Advances in neural information processing systems, vol 30. Curran Associates"},{"key":"670_CR12","first-page":"1","volume":"11","author":"E Strumbelj","year":"2010","unstructured":"Strumbelj E, Kononenko I (2010) An efficient explanation of individual classifications using game theory. J Mach Learn Res 11:1\u201318","journal-title":"J Mach Learn Res"},{"key":"670_CR13","unstructured":"Jethani N, Sudarshan M, Covert IC, Lee S-I, Ranganath R (2021) Fastshap: real-time Shapley value estimation. In: International conference on learning representations"},{"key":"670_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2025.110409","volume":"148","author":"C Zhao","year":"2025","unstructured":"Zhao C, Liu J, Parilina E (2025) Shapg: New feature importance method based on the Shapley value. Eng Appl Artif Intell 148:110409","journal-title":"Eng Appl Artif Intell"},{"issue":"5","key":"670_CR15","doi-asserted-by":"publisher","first-page":"687","DOI":"10.3390\/e24050687","volume":"24","author":"AI Adler","year":"2022","unstructured":"Adler AI, Painsky A (2022) Feature importance in gradient boosting trees with cross-validation feature selection. Entropy 24(5):687","journal-title":"Entropy"},{"key":"670_CR16","unstructured":"Pawar U, O\u2019Shea D, Rea S, O\u2019Reilly R (2020) Incorporating explainable artificial intelligence (XAI) to aid the understanding of machine in the healthcare domain. In: AICS, pp 169\u2013180"},{"issue":"10","key":"670_CR17","doi-asserted-by":"publisher","first-page":"767","DOI":"10.3390\/en9100767","volume":"9","author":"N Huang","year":"2016","unstructured":"Huang N, Lu G, Xu D (2016) A permutation importance-based feature selection method for short-term electricity load forecasting using random forest. Energies 9(10):767","journal-title":"Energies"},{"issue":"2","key":"670_CR18","doi-asserted-by":"publisher","first-page":"916","DOI":"10.31466\/kfbd.1174591","volume":"12","author":"M\u0130 G\u00fcrsoy","year":"2022","unstructured":"G\u00fcrsoy M\u0130, Alkan A (2022) Investigation of diabetes data with Permutation Feature Importance based Deep Learning methods. Karadeniz Fen Bilimleri Dergisi 12(2):916\u2013930","journal-title":"Karadeniz Fen Bilimleri Dergisi"},{"key":"670_CR19","doi-asserted-by":"crossref","unstructured":"Kaushik S, Birok R (2021) Heart failure prediction using XGBOOST algorithm and feature selection using feature permutation. In: 2021 4th international conference on electrical, computer and communication technologies (ICECCT), pp 1\u20136","DOI":"10.1109\/ICECCT52121.2021.9616626"},{"key":"670_CR20","doi-asserted-by":"crossref","unstructured":"Nagaraj P, Muneeswaran V, Dharanidharan A, Balananthanan K, Arunkumar M, Rajkumar C (2022) A prediction and recommendation system for diabetes mellitus using XAI-based lime explainer. In: 2022 International conference on sustainable computing and data communication systems (ICSCDS), pp 1472\u20131478","DOI":"10.1109\/ICSCDS53736.2022.9760847"},{"key":"670_CR21","doi-asserted-by":"crossref","unstructured":"Chowdhury KR, Sil A, Shukla SR (2021) Explaining a black-box sentiment analysis model with local interpretable model diagnostics explanation (LIME). In: Advances in computing and data sciences: 5th international conference, ICACDS 2021, Nashik, India, 23\u201324 April 2021, revised selected papers, part I 5. Springer, pp 90\u2013101","DOI":"10.1007\/978-3-030-81462-5_9"},{"key":"670_CR22","unstructured":"Ji Y (2021) Explainable AI methods for credit card fraud detection: evaluation of LIME and SHAP through a user study. Data Science, Master\u2019s Programme"},{"key":"670_CR23","unstructured":"Das A, Rad P (2020) Opportunities and challenges in explainable artificial intelligence (XAI): a survey. arXiv preprint. arXiv:2006.11371"},{"issue":"3","key":"670_CR24","doi-asserted-by":"publisher","first-page":"1154","DOI":"10.3390\/s22031154","volume":"22","author":"T-T-H Le","year":"2022","unstructured":"Le T-T-H, Kim H, Kang H, Kim H (2022) Classification and explanation for intrusion detection system based on ensemble trees and SHAP method. Sensors 22(3):1154","journal-title":"Sensors"},{"issue":"1","key":"670_CR25","doi-asserted-by":"publisher","first-page":"8984","DOI":"10.1038\/s41598-023-35795-0","volume":"13","author":"RO Alabi","year":"2023","unstructured":"Alabi RO, Elmusrati M, Leivo I, Almangush A, M\u00e4kitie AA (2023) Machine learning explainability in nasopharyngeal cancer survival using LIME and SHAP. Sci Rep 13(1):8984","journal-title":"Sci Rep"},{"key":"670_CR26","doi-asserted-by":"publisher","DOI":"10.1016\/j.scs.2022.103677","volume":"79","author":"Y Kim","year":"2022","unstructured":"Kim Y, Kim Y (2022) Explainable heat-related mortality with random forest and SHapley Additive exPlanations (SHAP) models. Sustain Cities Soc 79:103677","journal-title":"Sustain Cities Soc"},{"key":"670_CR27","doi-asserted-by":"crossref","unstructured":"Roshan K, Zafar A (2022) Using kernel SHAP XAI method to optimize the network anomaly detection model. In: 2022 9th International conference on computing for sustainable global development (INDIACom), pp 74\u201380","DOI":"10.23919\/INDIACom54597.2022.9763241"},{"key":"670_CR28","doi-asserted-by":"crossref","unstructured":"Hogan M, Aouf N, Spencer P, Almond J (2022) Explainable object detection for uncrewed aerial vehicles using KernelSHAP. In: 2022 IEEE International conference on autonomous robot systems and competitions (ICARSC). IEEE, pp 136\u2013141","DOI":"10.1109\/ICARSC55462.2022.9784772"},{"key":"670_CR29","doi-asserted-by":"crossref","unstructured":"Chakrabarti K, Mehrotra S (1999) The hybrid tree: an index structure for high dimensional feature spaces. In: Proceedings 15th international conference on data engineering (Cat. No. 99CB36337), pp 440\u2013447","DOI":"10.1109\/ICDE.1999.754960"},{"issue":"2","key":"670_CR30","first-page":"130","volume":"27","author":"Y-Y Song","year":"2015","unstructured":"Song Y-Y, Ying L (2015) Decision tree methods: applications for classification and prediction. Shanghai Arch Psychiatry 27(2):130","journal-title":"Shanghai Arch Psychiatry"},{"key":"670_CR31","unstructured":"Ke G, Meng Q, Finley T, Wang T, Chen W, Ma W, Ye Q, Liu T-Y (2017) LightGBM: a highly efficient gradient boosting decision tree. In: Advances in neural information processing systems, vol 30, Long Beach"},{"key":"670_CR32","unstructured":"Lundberg SM, Erion GG, Lee S-I (2018) Consistent individualized feature attribution for tree ensembles. arXiv preprint. arXiv:1802.03888"},{"key":"670_CR33","doi-asserted-by":"publisher","DOI":"10.1016\/j.scitotenv.2022.155070","volume":"832","author":"J Park","year":"2022","unstructured":"Park J, Lee WH, Kim KT, Park CY, Lee S, Heo T-Y (2022) Interpretation of ensemble learning to predict water quality using explainable artificial intelligence. Sci Total Environ 832:155070","journal-title":"Sci Total Environ"},{"issue":"1","key":"670_CR34","doi-asserted-by":"publisher","first-page":"20630","DOI":"10.1038\/s41598-020-77296-4","volume":"10","author":"J G\u00f3mez-Ram\u00edrez","year":"2020","unstructured":"G\u00f3mez-Ram\u00edrez J, Avila-Villanueva M, Fern\u00e1ndez-Bl\u00e1zquez M\u00c1 (2020) Selecting the most important self-assessed features for predicting conversion to mild cognitive impairment with random forest and permutation-based methods. Sci Rep 10(1):20630","journal-title":"Sci Rep"},{"key":"670_CR35","unstructured":"Mishra S, Sturm BL, Dixon S (2017) Local interpretable model-agnostic explanations for music content analysis. In: ISMIR, vol 53, pp 537\u2013543"},{"key":"670_CR36","doi-asserted-by":"crossref","unstructured":"Palacio S, Lucieri A, Munir M, Ahmed S, Hees J, Dengel A (2021) XAI handbook: towards a unified framework for explainable AI. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 3766\u20133775","DOI":"10.1109\/ICCVW54120.2021.00420"},{"key":"670_CR37","doi-asserted-by":"crossref","unstructured":"Shapley L (1953) A value for n-person games. In: Contributions to the theory of games (28). Princeton University Press, Princeton, p 307","DOI":"10.1515\/9781400881970-018"},{"key":"670_CR38","first-page":"60","volume-title":"Shapley value and its extensions","author":"NI Naumova","year":"2017","unstructured":"Naumova NI (2017) Shapley value and its extensions. VVM Publishing House, Saint Petersburg, p 60"},{"key":"670_CR39","doi-asserted-by":"publisher","first-page":"647","DOI":"10.1007\/s10115-013-0679-x","volume":"41","author":"E \u0160trumbelj","year":"2014","unstructured":"\u0160trumbelj E, Kononenko I (2014) Explaining prediction models and individual predictions with feature contributions. Knowl Inf Syst 41:647\u2013665","journal-title":"Knowl Inf Syst"},{"issue":"5","key":"670_CR40","doi-asserted-by":"publisher","first-page":"1726","DOI":"10.1016\/j.cor.2008.04.004","volume":"36","author":"J Castro","year":"2009","unstructured":"Castro J, G\u00f3mez D, Tejada J (2009) Polynomial calculation of the Shapley value based on sampling. Comput Oper Res 36(5):1726\u20131730","journal-title":"Comput Oper Res"},{"key":"670_CR41","unstructured":"neiljethani\/fastshap\u2014FastSHAP-TensorFlow. https:\/\/github.com\/neiljethani\/fastshap. Accessed 25 Aug 2024"},{"key":"670_CR42","unstructured":"iancovert\/fastshap\u2014FastSHAP-PyTorch. https:\/\/github.com\/iancovert\/fastshap. Accessed 25 Aug 2024"},{"key":"670_CR43","unstructured":"iclr1814\/fastshap\u2014FastSHAP-codebase. https:\/\/github.com\/iclr1814\/fastshap. Accessed 25 Aug 2024"},{"key":"670_CR44","doi-asserted-by":"publisher","unstructured":"Zhao C, Parilina E (2025) Centrality measures and opinion dynamics in two-layer networks with replica nodes. Comput & Oper Res 107245. https:\/\/doi.org\/10.1016\/j.cor.2025.107245","DOI":"10.1016\/j.cor.2025.107245"},{"key":"670_CR45","doi-asserted-by":"publisher","first-page":"96","DOI":"10.1002\/zamm.19300100113","volume":"10","author":"G P\u00f3lya","year":"1930","unstructured":"P\u00f3lya G (1930) Eine Wahrscheinlichkeitsaufgabe in der Kundenwerbung. Z Angew Math Mech (ZAMM) 10:96\u201397","journal-title":"Z Angew Math Mech (ZAMM)"},{"issue":"4","key":"670_CR46","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1109\/45.329294","volume":"13","author":"G Bebis","year":"1994","unstructured":"Bebis G, Georgiopoulos M (1994) Feed-forward neural networks. IEEE Potentials 13(4):27\u201331","journal-title":"IEEE Potentials"}],"container-title":["Dynamic Games and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13235-025-00670-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13235-025-00670-2","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13235-025-00670-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T02:16:28Z","timestamp":1782785788000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13235-025-00670-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,4]]},"references-count":46,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2026,7]]}},"alternative-id":["670"],"URL":"https:\/\/doi.org\/10.1007\/s13235-025-00670-2","relation":{},"ISSN":["2153-0785","2153-0793"],"issn-type":[{"value":"2153-0785","type":"print"},{"value":"2153-0793","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,4]]},"assertion":[{"value":"7 May 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 August 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 October 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}