{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,23]],"date-time":"2026-02-23T22:51:00Z","timestamp":1771887060873,"version":"3.50.1"},"reference-count":67,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,5,9]],"date-time":"2025-05-09T00:00:00Z","timestamp":1746748800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,5,9]],"date-time":"2025-05-09T00:00:00Z","timestamp":1746748800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Discov Artif Intell"],"DOI":"10.1007\/s44163-025-00281-1","type":"journal-article","created":{"date-parts":[[2025,5,9]],"date-time":"2025-05-09T12:16:02Z","timestamp":1746792962000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Transformative impact of explainable artificial intelligence: bridging complexity and trust"],"prefix":"10.1007","volume":"5","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9114-7080","authenticated-orcid":false,"given":"Girish","family":"Paliwal","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5477-9553","authenticated-orcid":false,"given":"Ashish","family":"Kumar","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3976-5839","authenticated-orcid":false,"given":"Kanta Prasad","family":"Sharma","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7017-1372","authenticated-orcid":false,"given":"Deepshika","family":"Bhargava","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8303-8749","authenticated-orcid":false,"given":"Vijay Mohan","family":"Shrimal","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,5,9]]},"reference":[{"key":"281_CR1","doi-asserted-by":"publisher","DOI":"10.3389\/fpsyg.2021.696346","volume":"12","author":"AA Abonamah","year":"2021","unstructured":"Abonamah AA, Tariq MU, Shilbayeh S. On the commoditization of artificial intelligence. Front Psychol. 2021;12: 696346. https:\/\/doi.org\/10.3389\/fpsyg.2021.696346.","journal-title":"Front Psychol"},{"issue":"3","key":"281_CR2","doi-asserted-by":"publisher","first-page":"771","DOI":"10.3390\/biomedicines11030771","volume":"11","author":"B Allen","year":"2023","unstructured":"Allen B. Discovering themes in deep brain stimulation research using explainable artificial intelligence. Biomedicines. 2023;11(3):771. https:\/\/doi.org\/10.3390\/biomedicines11030771.","journal-title":"Biomedicines"},{"key":"281_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijmedinf.2023.105088","volume":"175","author":"MM Alsaleh","year":"2023","unstructured":"Alsaleh MM, Allery F, Choi JW, Hama T, McQuillin A, Wu H, Thygesen JH. Prediction of disease comorbidity using explainable artificial intelligence and machine learning techniques: a systematic review. Int J Med Inform. 2023;175:105088. https:\/\/doi.org\/10.1016\/j.ijmedinf.2023.105088.","journal-title":"Int J Med Inform"},{"key":"281_CR4","doi-asserted-by":"publisher","first-page":"205520762412726","DOI":"10.1177\/20552076241272657","volume":"10","author":"J Caterson","year":"2024","unstructured":"Caterson J, Lewin A, Williamson E. The application of explainable artificial intelligence (XAI) in electronic health record research: a scoping review. Digit Health. 2024;10:20552076241272656. https:\/\/doi.org\/10.1177\/20552076241272657.","journal-title":"Digit Health"},{"key":"281_CR5","doi-asserted-by":"publisher","DOI":"10.3389\/frai.2022.827584","volume":"5","author":"J \u010cernevi\u010dien\u0117","year":"2022","unstructured":"\u010cernevi\u010dien\u0117 J, Kaba\u0161inskas A. Review of multi-criteria decision-making methods in finance using explainable artificial intelligence. Front Artif Intell. 2022;5:827584. https:\/\/doi.org\/10.3389\/frai.2022.827584.","journal-title":"Front Artif Intell"},{"issue":"10","key":"281_CR6","doi-asserted-by":"publisher","first-page":"1500","DOI":"10.3390\/foods11101500","volume":"11","author":"A Adak","year":"2022","unstructured":"Adak A, Pradhan B, Shukla N. Sentiment analysis of customer reviews of food delivery services using deep learning and explainable artificial intelligence: systematic review. Foods. 2022;11(10):1500. https:\/\/doi.org\/10.3390\/foods11101500.","journal-title":"Foods"},{"key":"281_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2023.107555","volume":"166","author":"S Ali","year":"2023","unstructured":"Ali S, Akhlaq F, Imran AS, Kastrati Z, Daudpota SM, Moosa M. The enlightening role of explainable artificial intelligence in medical and healthcare domains: a systematic literature review. Comput Biol Med. 2023;166:107555. https:\/\/doi.org\/10.1016\/j.compbiomed.2023.107555.","journal-title":"Comput Biol Med"},{"key":"281_CR8","doi-asserted-by":"publisher","first-page":"1180773","DOI":"10.3389\/fmed.2023.1180773","volume":"10","author":"BM de Vries","year":"2023","unstructured":"de Vries BM, Zwezerijnen GJC, Burchell GL, van Velden FHP, der Houven M-V, van Oordt CW, Boellaard R. Explainable artificial intelligence (XAI) in radiology and nuclear medicine: a literature review. Front Med. 2023;10:1180773. https:\/\/doi.org\/10.3389\/fmed.2023.1180773.","journal-title":"Front Med"},{"issue":"5","key":"281_CR9","doi-asserted-by":"publisher","DOI":"10.1002\/cai2.136","volume":"3","author":"A Ghasemi","year":"2024","unstructured":"Ghasemi A, Hashtarkhani S, Schwartz DL, Shaban-Nejad A. Explainable artificial intelligence in breast cancer detection and risk prediction: a systematic scoping review. Cancer Innov. 2024;3(5):e136. https:\/\/doi.org\/10.1002\/cai2.136.","journal-title":"Cancer Innov"},{"issue":"5","key":"281_CR10","doi-asserted-by":"publisher","first-page":"834","DOI":"10.3348\/jksr.2024.0118","volume":"85","author":"S Do","year":"2024","unstructured":"Do S. Explainable & safe artificial intelligence in radiology. J Korean Soc Radiol. 2024;85(5):834\u201347. https:\/\/doi.org\/10.3348\/jksr.2024.0118.","journal-title":"J Korean Soc Radiol"},{"issue":"3","key":"281_CR11","doi-asserted-by":"publisher","first-page":"423","DOI":"10.1111\/bjd.18880","volume":"183","author":"X Du-Harpur","year":"2020","unstructured":"Du-Harpur X, Watt FM, Luscombe NM, Lynch MD. What is AI? Applications of artificial intelligence to dermatology. Br J Dermatol. 2020;183(3):423\u201330. https:\/\/doi.org\/10.1111\/bjd.18880.","journal-title":"Br J Dermatol"},{"issue":"11","key":"281_CR12","doi-asserted-by":"publisher","first-page":"e745","DOI":"10.1016\/S2589-7500(21)00208-9","volume":"3","author":"M Ghassemi","year":"2021","unstructured":"Ghassemi M, Oakden-Rayner L, Beam AL. The false hope of current approaches to explainable artificial intelligence in health care. Lancet Digit Health. 2021;3(11):e745\u201350. https:\/\/doi.org\/10.1016\/S2589-7500(21)00208-9.","journal-title":"Lancet Digit Health"},{"issue":"1","key":"281_CR13","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.cpet.2021.09.007","volume":"17","author":"N Hasani","year":"2022","unstructured":"Hasani N, Morris MA, Rhamim A, Summers RM, Jones E, Siegel E, Saboury B. Trustworthy artificial intelligence in medical imaging. PET Clinics. 2022;17(1):1\u201312. https:\/\/doi.org\/10.1016\/j.cpet.2021.09.007.","journal-title":"PET Clinics"},{"issue":"1","key":"281_CR14","doi-asserted-by":"publisher","first-page":"6","DOI":"10.1038\/s41746-023-00751-9","volume":"6","author":"DW Joyce","year":"2023","unstructured":"Joyce DW, Kormilitzin A, Smith KA, Cipriani A. Explainable artificial intelligence for mental health through transparency and interpretability for understandability. NPJ Digit Med. 2023;6(1):6. https:\/\/doi.org\/10.1038\/s41746-023-00751-9.","journal-title":"NPJ Digit Med"},{"key":"281_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2023.106668","volume":"156","author":"S Nazir","year":"2023","unstructured":"Nazir S, Dickson DM, Akram MU. Survey of explainable artificial intelligence techniques for biomedical imaging with deep neural networks. Comput Biol Med. 2023;156: 106668. https:\/\/doi.org\/10.1016\/j.compbiomed.2023.106668.","journal-title":"Comput Biol Med"},{"key":"281_CR16","doi-asserted-by":"publisher","unstructured":"Cambria E, Malandri L, Mercorio F, Nobani N, Seveso A. XAI meets LLMs: A survey of the relation between explainable AI and large language models. 2024. arXiv preprint arXiv:2407.15248. https:\/\/doi.org\/10.48550\/arXiv.2407.15248","DOI":"10.48550\/arXiv.2407.15248"},{"key":"281_CR17","doi-asserted-by":"publisher","unstructured":"Zytek A, Pid\u00f2 S, Veeramachaneni K. LLMs for XAI: future directions for explaining explanations. 2024. arXiv preprint arXiv:2405.06064. https:\/\/doi.org\/10.48550\/arXiv.2405.06064","DOI":"10.48550\/arXiv.2405.06064"},{"key":"281_CR18","doi-asserted-by":"publisher","unstructured":"Wu X, Zhao H, Zhu Y, Shi Y, Yang F, Liu T, Zhai X, Yao W, Li J, Du M, Liu N. Usable XAI: 10 strategies towards exploiting explainability in the LLM era. 2024. [13 March, 2024]. arXiv preprint arXiv:2403.08946. https:\/\/doi.org\/10.48550\/arXiv.2403.08946.","DOI":"10.48550\/arXiv.2403.08946"},{"key":"281_CR19","doi-asserted-by":"publisher","unstructured":"Zihni E, McGarry B, Kelleher J. Moving toward explainable decisions of artificial intelligence models for the prediction of functional outcomes of ischemic stroke patients. Brisbane: Exon Publications; 2022. p. 73\u201390.\u00a0https:\/\/doi.org\/10.36255\/exon-publications-digital-health-explainable-decisions.","DOI":"10.36255\/exon-publications-digital-health-explainable-decisions"},{"issue":"3","key":"281_CR20","doi-asserted-by":"publisher","first-page":"283","DOI":"10.3390\/e23030283","volume":"23","author":"A Hern\u00e1ndez","year":"2021","unstructured":"Hern\u00e1ndez A, Amig\u00f3 JM. Attention mechanisms and their applications to complex systems. Entropy. 2021;23(3):283. https:\/\/doi.org\/10.3390\/e23030283.","journal-title":"Entropy"},{"issue":"4","key":"281_CR21","doi-asserted-by":"publisher","first-page":"369","DOI":"10.3390\/bioengineering11040369","volume":"11","author":"C Metta","year":"2024","unstructured":"Metta C, Beretta A, Pellungrini R, Rinzivillo S, Giannotti F. Towards transparent healthcare: advancing local explanation methods in explainable artificial intelligence. Bioengineering. 2024;11(4):369. https:\/\/doi.org\/10.3390\/bioengineering11040369.","journal-title":"Bioengineering"},{"issue":"10","key":"281_CR22","doi-asserted-by":"publisher","first-page":"3853","DOI":"10.21037\/tcr-22-1626","volume":"11","author":"C Ladbury","year":"2022","unstructured":"Ladbury C, Zarinshenas R, Semwal H, Tam A, Vaidehi N, Rodin AS, Liu A, Glaser S, Salgia R, Amini A. Utilization of model-agnostic explainable artificial intelligence frameworks in oncology: a narrative review. Transl Cancer Res. 2022;11(10):3853\u201368. https:\/\/doi.org\/10.21037\/tcr-22-1626.","journal-title":"Transl Cancer Res"},{"issue":"10","key":"281_CR23","doi-asserted-by":"publisher","first-page":"1095","DOI":"10.1016\/j.ccell.2022.09.012","volume":"40","author":"J Lipkova","year":"2022","unstructured":"Lipkova J, Chen RJ, Chen B, Lu MY, Barbieri M, Shao D, Vaidya AJ, Chen C, Zhuang L, Williamson DF, Shaban M. Artificial intelligence for multimodal data integration in oncology. Cancer Cell. 2022;40(10):1095\u2013110. https:\/\/doi.org\/10.1016\/j.ccell.2022.09.012.","journal-title":"Cancer Cell"},{"issue":"8","key":"281_CR24","doi-asserted-by":"publisher","first-page":"1","DOI":"10.55041\/ijsrem37405","volume":"8","author":"G Anushree","year":"2024","unstructured":"Anushree G, Madagaonkar SB, Ravili CH. Unveiling the black box: a comprehensive review of explainable AI techniques. Indian Sci J Res Eng Manag. 2024;8(8):1\u20136. https:\/\/doi.org\/10.55041\/ijsrem37405.","journal-title":"Indian Sci J Res Eng Manag"},{"key":"281_CR25","doi-asserted-by":"publisher","unstructured":"Salih A, Boscolo Galazzo I, Raisi-Estabragh Z, Petersen SE, Menegaz G, Radeva P. Characterizing the contribution of dependent features in XAI methods. 2023;28 (11):6466\u201373.  https:\/\/doi.org\/10.1109\/JBHI.2024.3395289.","DOI":"10.1109\/JBHI.2024.3395289"},{"issue":"7","key":"281_CR26","doi-asserted-by":"publisher","first-page":"769","DOI":"10.1177\/0022034520915714","volume":"99","author":"F Schwendicke","year":"2020","unstructured":"Schwendicke F, Samek W, Krois J. Artificial intelligence in dentistry: chances and challenges. J Dent Res. 2020;99(7):769\u201374. https:\/\/doi.org\/10.1177\/0022034520915714.","journal-title":"J Dent Res"},{"issue":"2","key":"281_CR27","doi-asserted-by":"publisher","first-page":"86","DOI":"10.1007\/s10586-024-04804-w","volume":"28","author":"EH Houssein","year":"2025","unstructured":"Houssein EH, Mohsen S, Emam MM, Abdel Samee N, Alkanhel RI, Younis EMG. Leveraging explainable artificial intelligence for emotional label prediction through health sensor monitoring. Clust Comput. 2025;28(2):86. https:\/\/doi.org\/10.1007\/s10586-024-04804-w.","journal-title":"Clust Comput"},{"key":"281_CR28","doi-asserted-by":"publisher","DOI":"10.2196\/53207","volume":"3","author":"R Rosenbacke","year":"2024","unstructured":"Rosenbacke R, Melhus \u00c5, McKee M, Stuckler D. How explainable artificial intelligence can increase or decrease clinicians\u2019 trust in AI applications in health care: systematic review. JMIR AI. 2024;3:e53207. https:\/\/doi.org\/10.2196\/53207.","journal-title":"JMIR AI"},{"key":"281_CR29","doi-asserted-by":"publisher","unstructured":"Quan C, Wang W, Yu K, Ban D. Explainable artificial intelligence framework for plastic hinge length prediction of flexural-dominated steel-reinforced concrete composite shear walls. Eng Struct. 2025;324:119388.\u00a0https:\/\/doi.org\/10.1016\/j.engstruct.2024.119388.","DOI":"10.1016\/j.engstruct.2024.119388"},{"key":"281_CR30","doi-asserted-by":"publisher","first-page":"54","DOI":"10.1016\/j.ejca.2022.02.025","volume":"167","author":"K Hauser","year":"2022","unstructured":"Hauser K, Kurz A, Haggenm\u00fcller S, Maron RC, von Kalle C, Utikal JS, Meier F, Hobelsberger S, Gellrich FF, Sergon M, Hauschild A.\u00a0Explainable artificial intelligence in skin cancer recognition: a systematic review. Eur J Cancer. 2022;167:54\u201369. https:\/\/doi.org\/10.1016\/j.ejca.2022.02.025.","journal-title":"Eur J Cancer"},{"issue":"5","key":"281_CR31","doi-asserted-by":"publisher","first-page":"1125","DOI":"10.1007\/s00345-022-03930-7","volume":"40","author":"S O\u2019Sullivan","year":"2022","unstructured":"O\u2019sullivan S, Janssen M, Holzinger A, Nevejans N, Eminaga O, Meyer CP, Miernik A.\u00a0Explainable artificial intelligence (XAI): closing the gap between image analysis and navigation in complex invasive didagnostic procedures. World J Urol. 2022; 40(5):1125\u201334. https:\/\/doi.org\/10.1007\/s00345-022-03930-7.","journal-title":"World J Urol"},{"key":"281_CR32","doi-asserted-by":"publisher","unstructured":"Maiuri C, Karimshoushtari M, Tango F, Novara C. Application of reinforcement learning for intelligent support decision system: a paradigm towards safety and explainability.\u00a0In International Conference on Human-Computer Interaction 2023 Jul 9 (pp. 243-261). Cham: Springer Nature Switzerland.\u00a0Eds: Schmorrow DD, Fidopiastis CM. Lecture notes in computer science, vol. 14050.\u00a0p. 351\u201368. https:\/\/doi.org\/10.1007\/978-3-031-35891-3_15.","DOI":"10.1007\/978-3-031-35891-3_15"},{"issue":"10","key":"281_CR33","doi-asserted-by":"publisher","first-page":"1902","DOI":"10.1002\/mar.21706","volume":"39","author":"Z Xie","year":"2022","unstructured":"Xie Z, Yu Y, Zhang J, Chen M. The searching artificial intelligence: consumers show less aversion to algorithm-recommended search product. Psychol Mark. 2022;39(10):1902\u201319. https:\/\/doi.org\/10.1002\/mar.21706.","journal-title":"Psychol Mark"},{"issue":"5","key":"281_CR34","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2023.103440","volume":"60","author":"M Dongbo","year":"2023","unstructured":"Dongbo M, Miniaoui S, Fen L, Althubiti SA, Alsenani TR. Intelligent chatbot interaction system capable for sentimental analysis using hybrid machine learning algorithms. Inf Process Manage. 2023;60(5):103440. https:\/\/doi.org\/10.1016\/j.ipm.2023.103440.","journal-title":"Inf Process Manage"},{"key":"281_CR35","doi-asserted-by":"publisher","unstructured":"Walter S. AI impacts on supply chain performance: a manufacturing use case study. Discov Artif Intell. 2023;3(1):18. https:\/\/doi.org\/10.1007\/s44163-023-00061-9.","DOI":"10.1007\/s44163-023-00061-9"},{"key":"281_CR36","unstructured":"Chopal R, Garg U. Artificial-intelligence and recruitment: Shift towards automated HR practice. Journal of Emerging Technologies and Innovative Research. 2021;8(7): B726-31."},{"issue":"4","key":"281_CR37","doi-asserted-by":"publisher","first-page":"1607","DOI":"10.1007\/s13347-021-00477-0","volume":"34","author":"WJ Von Eschenbach","year":"2021","unstructured":"Von Eschenbach WJ.\u00a0Transparency and the black box problem: why we do not trust AI. Philos Technol. 2021;34(4):1607\u201322. https:\/\/doi.org\/10.1007\/s13347-021-00477-0.","journal-title":"Philos Technol"},{"key":"281_CR38","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2023.101882","volume":"99","author":"E Mariotti","year":"2023","unstructured":"Mariotti E, Moral JM, Gatt A. Exploring the balance between interpretability and performance with carefully designed constrainable neural additive models. Inf Fusion. 2023;99: 101882. https:\/\/doi.org\/10.1016\/j.inffus.2023.101882.","journal-title":"Inf Fusion"},{"issue":"6","key":"281_CR39","doi-asserted-by":"publisher","first-page":"3572","DOI":"10.3390\/app13063572","volume":"13","author":"O Lukashova-Sanz","year":"2023","unstructured":"Lukashova-Sanz O, Dechant M, Wahl S. The influence of disclosing the AI potential error to the user on the efficiency of user\u2013AI collaboration. Appl Sci. 2023;13(6):3572. https:\/\/doi.org\/10.3390\/app13063572.","journal-title":"Appl Sci"},{"key":"281_CR40","doi-asserted-by":"publisher","unstructured":"Deshpande RS, Ambatkar PV. Interpretable deep learning models: Enhancing transparency and trustworthiness in explainable AI. In: Proceedings of the International Conference on Science and Engineering. 2023;11(1):1352\u201363. https:\/\/doi.org\/10.52783\/cienceng.v11i1.286.","DOI":"10.52783\/cienceng.v11i1.286"},{"issue":"2","key":"281_CR41","doi-asserted-by":"publisher","first-page":"158","DOI":"10.1016\/j.tree.2021.09.007","volume":"37","author":"JA Catford","year":"2022","unstructured":"Catford JA, Wilson JR, Py\u0161ek P, Hulme PE, Duncan RP. Addressing context dependence in ecology. Trends in Ecology & Evolution. 2022 Feb 1;37(2):158-70.Addressing context dependence in ecology. Trends Ecol Evol. 2022;37(2):158\u201370. https:\/\/doi.org\/10.1016\/j.tree.2021.09.007.","journal-title":"Trends Ecol Evol"},{"key":"281_CR42","doi-asserted-by":"publisher","unstructured":"Clark B, Wilming R, Haufe S. XAI-TRIS: non-linear image benchmarks to quantify false positive post-hoc attribution of feature importance. Mach Learn 2024;113:6871\u2013910. https:\/\/doi.org\/10.1007\/s10994-024-06574-3.","DOI":"10.1007\/s10994-024-06574-3"},{"issue":"3","key":"281_CR43","doi-asserted-by":"publisher","first-page":"2247","DOI":"10.22214\/ijraset.2023.49990","volume":"11","author":"S Srivastava","year":"2023","unstructured":"Srivastava S, Sinha K. From bias to fairness: a review of ethical considerations and mitigation strategies in artificial intelligence. Int J Sci Technol Eng. 2023;11(3):2247\u201351. https:\/\/doi.org\/10.22214\/ijraset.2023.49990.","journal-title":"Int J Sci Technol Eng"},{"key":"281_CR44","doi-asserted-by":"publisher","unstructured":"Ogawa R, Shima S, Takemura T, Fukuzumi SI. A study on trust building in AI systems through user commitment. In: Lecture notes in computer science, vol. 12776. Cham: Springer; 2023. p. 557\u201367. https:\/\/doi.org\/10.1007\/978-3-031-35132-7_42.","DOI":"10.1007\/978-3-031-35132-7_42"},{"issue":"3","key":"281_CR45","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3531532","volume":"14","author":"P Hacker","year":"2022","unstructured":"Hacker P, Naumann F, Friedrich T, Grundmann S, Lehmann A, Zech H.\u00a0AI compliance \u2013 challenges of bridging data science and law. J Data Inf Qual. 2022;14(3):1\u20134. https:\/\/doi.org\/10.1145\/3531532.","journal-title":"J Data Inf Qual"},{"key":"281_CR46","doi-asserted-by":"publisher","unstructured":"Calegari R, Ciatto G, Omicini A. On the integration of symbolic and sub-symbolic techniques for XAI: a survey. Intell Agents. 2020;14(1):7\u201332. https:\/\/doi.org\/10.3233\/IA-190036.","DOI":"10.3233\/IA-190036"},{"key":"281_CR47","doi-asserted-by":"publisher","unstructured":"Singh T, Goel R, Baral SK. A study on big data privacy in cross-industrial challenges and legal implications. In Cross-Industry Applications of Cyber Security Frameworks (pp. 112\u2013123). IGI Global. 2022. https:\/\/doi.org\/10.4018\/978-1-6684-3448-2.ch006. Accessed on 13 Mar.","DOI":"10.4018\/978-1-6684-3448-2.ch006"},{"key":"281_CR48","doi-asserted-by":"publisher","unstructured":"Weber L. Beyond explaining: Opportunities and challenges of XAI-based model improvement. Typeset.io. 2022;92:154\u201376. https:\/\/doi.org\/10.48550\/arXiv.2203.08008.","DOI":"10.48550\/arXiv.2203.08008"},{"key":"281_CR49","doi-asserted-by":"publisher","unstructured":"Rong Y. Towards human-centered explainable AI: user studies for model explanations. In: IEEE Transactions on Pattern Analysis and Machine Intelligence. 2024;46(4)2104\u2013122.https:\/\/doi.org\/10.1109\/TPAMI.2023.3331846.","DOI":"10.1109\/TPAMI.2023.3331846"},{"key":"281_CR50","doi-asserted-by":"publisher","DOI":"10.3233\/faia230126","author":"Regina de Brito Duarte","year":"2023","unstructured":"Regina De Brito Duarte. Towards responsible AI: developing explanations to increase human-AI collaboration. Front Artif Intell Appl. 2023;368:470\u201382.\u00a0https:\/\/doi.org\/10.3233\/faia230126.","journal-title":"Front Artif Intell Appl"},{"issue":"CSCW1","key":"281_CR51","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3579467","volume":"7","author":"U Ehsan","year":"2023","unstructured":"Ehsan U, Saha K, De Choudhury M, Riedl MO. Charting the sociotechnical gap in explainable AI: a framework to address the gap in XAI. Proc ACM Hum-Comput Interact. 2023;7(CSCW1):1\u201332. https:\/\/doi.org\/10.1145\/3579467.","journal-title":"Proc ACM Hum-Comput Interact"},{"key":"281_CR52","doi-asserted-by":"publisher","DOI":"10.3389\/frai.2021.550030","volume":"4","author":"L Wells","year":"2021","unstructured":"Wells L, Bednarz T. Explainable AI and reinforcement learning\u2014a systematic review of current approaches and trends. Front Artif Intell. 2021;4:550030. https:\/\/doi.org\/10.3389\/frai.2021.550030.","journal-title":"Front Artif Intell"},{"issue":"1","key":"281_CR53","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3494454","volume":"14","author":"M Barhamgi","year":"2022","unstructured":"Barhamgi M, Bertino E. Editorial: Special issue on data transparency\u2014data quality, annotation, and provenance. J Data Inf Qual. 2022;14(1):1\u20133. https:\/\/doi.org\/10.1145\/3494454.","journal-title":"J Data Inf Qual"},{"issue":"3","key":"281_CR54","doi-asserted-by":"publisher","first-page":"189","DOI":"10.3390\/joitmc7030189","volume":"7","author":"R Shinde","year":"2021","unstructured":"Shinde R, Patil S, Kotecha K, Ruikar K. Blockchain for securing AI applications and open innovations. J Open Innov: Technol Mark Complex. 2021;7(3):189. https:\/\/doi.org\/10.3390\/joitmc7030189.","journal-title":"J Open Innov: Technol Mark Complex"},{"key":"281_CR55","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1016\/j.jclinepi.2022.06.004","volume":"150","author":"J.F. Meneses-Echavez","year":"2022","unstructured":"Meneses-Echavez JF, Bidonde J, Yepes-Nu\u00f1ez JJ, Peri\u010di\u0107 TP, Puljak L, Bala MM, Storman D, Swierz MJ, Zaj\u0105c J, Montesinos-Guevara C, Zhang Y. Evidence to decision frameworks enabled structured and explicit development of healthcare recommendations. J Clin Epidemiol. 2022;150:51\u201362. https:\/\/doi.org\/10.1016\/j.jclinepi.2022.06.004.","journal-title":"J Clin Epidemiol"},{"issue":"2","key":"281_CR56","doi-asserted-by":"publisher","first-page":"257","DOI":"10.1016\/j.clsr.2018.01.004","volume":"34","author":"M Butterworth","year":"2018","unstructured":"Butterworth M. The ICO and artificial intelligence: the role of fairness in the GDPR framework. Comput Law Secur Rev. 2018;34(2):257\u201368. https:\/\/doi.org\/10.1016\/j.clsr.2018.01.004.","journal-title":"Comput Law Secur Rev"},{"issue":"11","key":"281_CR57","doi-asserted-by":"publisher","first-page":"5310","DOI":"10.3390\/app12115310","volume":"12","author":"H Mankodiya","year":"2022","unstructured":"Mankodiya H, Jadav D, Gupta R, Tanwar S, Hong WC, Sharma R. OD-XAI: explainable AI-based semantic object detection for autonomous vehicles. Appl Sci. 2022;12(11):5310. https:\/\/doi.org\/10.3390\/app12115310.","journal-title":"Appl Sci"},{"issue":"4","key":"281_CR58","doi-asserted-by":"publisher","first-page":"1175","DOI":"10.22214\/ijraset.2023.50098","volume":"11","author":"S Mutsuddi","year":"2023","unstructured":"Mutsuddi\u00a0S. Machine learning for predictive maintenance in manufacturing industries. Int J Sci Technol Eng. 2023;11(4):1175\u201381. https:\/\/doi.org\/10.22214\/ijraset.2023.50098.","journal-title":"Int J Sci Technol Eng"},{"issue":"1","key":"281_CR59","doi-asserted-by":"publisher","first-page":"152","DOI":"10.26682\/hjuod.2023.26.1.11","volume":"26","author":"F.A.A. Abdulkareem","year":"2023","unstructured":"F. A. A. Abdulkareem. Using predictive justice algorithms for issuing court judgments with efficient prediction: development of legal-tech prospects in the judiciary system in Iraq and Kurdistan region. Humanit J Univ Duhok. 2023; 26(1):152\u20137.\u00a0https:\/\/doi.org\/10.26682\/hjuod.2023.26.1.11.","journal-title":"Humanit J Univ Duhok"},{"issue":"2","key":"281_CR60","doi-asserted-by":"publisher","first-page":"352","DOI":"10.1109\/TCSS.2020.2965198","volume":"7","author":"Y Tang","year":"2020","unstructured":"Tang Y, Liang J, Hare R, Wang FY. A personalized learning system for parallel intelligent education.\u00a0IEEE Trans Comput Soc Syst. 2020;7(2):352\u201361. https:\/\/doi.org\/10.1109\/TCSS.2020.2965198.","journal-title":"IEEE Trans Comput Soc Syst"},{"issue":"5","key":"281_CR61","doi-asserted-by":"publisher","first-page":"498","DOI":"10.1080\/09593969.2020.1768575","volume":"30","author":"S Wolpert","year":"2020","unstructured":"Wolpert S, Roth A. Development of a classification framework for technology based retail services: A retailers\u2019 perspective. Int Rev Retail Distrib Consum Res. 2020;30(5):498\u2013537.\u00a0https:\/\/doi.org\/10.1080\/09593969.2020.1768575.","journal-title":"Int Rev Retail Distrib Consum Res"},{"key":"281_CR62","doi-asserted-by":"publisher","unstructured":"Bergadano F, Giacinto G. Special Issue \u201cAI for Cybersecurity: Robust Models for Authentication, Threat and Anomaly Detection\u201d. Algorithms. 2023;16(7):327.\u00a0https:\/\/doi.org\/10.3390\/a16070327.","DOI":"10.3390\/a16070327"},{"key":"281_CR63","doi-asserted-by":"publisher","unstructured":"Stassin S, Englebert A, Nanfack G, Albert J, Versbraegen N, Peiffer G, Doh M, Riche N, Frenay B, De Vleeschouwer C. An Experimental Investigation into the Evaluation of Explainability Methods. arXiv 2023. arXiv preprint arXiv:2305.16361. https:\/\/doi.org\/10.48550\/arXiv.2305.16361","DOI":"10.48550\/arXiv.2305.16361"},{"key":"281_CR64","doi-asserted-by":"publisher","DOI":"10.1007\/s11704-023-2691-y","author":"Xumeng Wang","year":"2023","unstructured":"Wang X, Wu Z, Huang W, Wei Y, Huang Z, Xu M, Chen W. VIS+ AI: integrating visualization with artificial intelligence for efficient data analysis. Front Comput Sci. 2023;17(6):176709.\u00a0https:\/\/doi.org\/10.1007\/s11704-023-2691-y.","journal-title":"Front Comput Sci"},{"key":"281_CR65","doi-asserted-by":"publisher","unstructured":"Reddy GP, Kumar YP. Explainable AI (XAI): explained. In 2023 IEEE Open Conference of Electrical, Electronic and Information Sciences (eStream). 2023. pp. 1\u20136. IEEE.  https:\/\/doi.org\/10.1109\/eStream59056.2023.10134984.","DOI":"10.1109\/eStream59056.2023.10134984"},{"key":"281_CR66","doi-asserted-by":"publisher","unstructured":"Quakulinski L, Koumpis A, Beyan OD. Establishing transparency in artificial intelligence systems. In 2022 Fourth International Conference on Transdisciplinary AI (TransAI). 2022. pp. 116\u2013121 IEEE. https:\/\/doi.org\/10.1109\/TransAI54797.2022.00027.","DOI":"10.1109\/TransAI54797.2022.00027"},{"key":"281_CR67","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2023.101805","author":"S Sajid","year":"2023","unstructured":"Ali S, Abuhmed T, El-Sappagh S, Muhammad K, Alonso-Moral JM, Confalonieri R, Guidotti R, Del Ser J, D\u00edaz-Rodr\u00edguez N, Herrera F. Explainable artificial intelligence (XAI): What we know and what is left to attain trustworthy artificial intelligence. Inf Fusion. 2023;99:101805. https:\/\/doi.org\/10.1016\/j.inffus.2023.101805.","journal-title":"Inf Fusion"}],"container-title":["Discover Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44163-025-00281-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s44163-025-00281-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44163-025-00281-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,9]],"date-time":"2025-05-09T12:16:07Z","timestamp":1746792967000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s44163-025-00281-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,9]]},"references-count":67,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["281"],"URL":"https:\/\/doi.org\/10.1007\/s44163-025-00281-1","relation":{},"ISSN":["2731-0809"],"issn-type":[{"value":"2731-0809","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,5,9]]},"assertion":[{"value":"31 December 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 April 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 May 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":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to participate"}},{"value":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to publish"}},{"value":"The authors declare no competing interests.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"51"}}