{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T15:38:38Z","timestamp":1775230718277,"version":"3.50.1"},"publisher-location":"Cham","reference-count":67,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032083326","type":"print"},{"value":"9783032083333","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,10,19]],"date-time":"2025-10-19T00:00:00Z","timestamp":1760832000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,10,19]],"date-time":"2025-10-19T00:00:00Z","timestamp":1760832000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026]]},"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:p>Artificial Intelligence (AI) is rapidly embedded in critical decision-making systems, however their foundational \u201cblack-box\u201d models require eXplainable AI (XAI) solutions to enhance transparency, which are mostly oriented to experts, making no sense to non-experts. Alarming evidence about AI\u2019s unprecedented human values risks brings forward the imperative need for transparent human-centered XAI solutions. In this work, we introduce a domain-, model-, explanation-agnostic, generalizable and reproducible framework that ensures both transparency and human-centered explanations tailored to the needs of both experts and non-experts. The framework leverages Large Language Models (LLMs) and employs in-context learning to convey domain- and explainability-relevant contextual knowledge into LLMs. Through its structured prompt and system setting, our framework encapsulates in one response explanations understandable by non-experts and technical information to experts, all grounded in domain and explainability principles. To demonstrate the effectiveness of our framework, we establish a ground-truth contextual \u201cthesaurus\u201d through a rigorous benchmarking with over 40 data, model, and XAI combinations for an explainable clustering analysis of a well-being scenario. Through a comprehensive quality and human-friendliness evaluation of our framework\u2019s explanations, we prove high content quality through strong correlations with ground-truth explanations (Spearman rank correlation = 0.92) and improved interpretability and human-friendliness to non-experts through a user study (N = 56). Our overall evaluation confirms trust in LLMs as HCXAI enablers, as our framework bridges the above Gaps by delivering (i) high-quality technical explanations aligned with foundational XAI methods and (ii) clear, efficient, and interpretable human-centered explanations for non-experts.<\/jats:p>","DOI":"10.1007\/978-3-032-08333-3_6","type":"book-chapter","created":{"date-parts":[[2025,10,18]],"date-time":"2025-10-18T05:22:36Z","timestamp":1760764956000},"page":"113-137","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Mind the\u00a0XAI Gap: A Human-Centered LLM Framework for\u00a0Democratizing Explainable AI"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3561-6994","authenticated-orcid":false,"given":"Eva","family":"Paraschou","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4212-0890","authenticated-orcid":false,"given":"Ioannis","family":"Arapakis","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5629-3493","authenticated-orcid":false,"given":"Sofia","family":"Yfantidou","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4246-4976","authenticated-orcid":false,"given":"Sebastian","family":"Macaluso","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0666-6984","authenticated-orcid":false,"given":"Athena","family":"Vakali","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,10,19]]},"reference":[{"key":"6_CR1","doi-asserted-by":"crossref","unstructured":"Barua, A., Widmer, C., Hitzler, P.: Concept induction using LLMs: a user experiment for assessment. arXiv preprint arXiv:2404.11875 (2024)","DOI":"10.1007\/978-3-031-71170-1_13"},{"issue":"12","key":"6_CR2","doi-asserted-by":"publisher","first-page":"58","DOI":"10.1145\/3665322","volume":"67","author":"A Bellog\u00edn","year":"2024","unstructured":"Bellog\u00edn, A., Grau, O., Larsson, S., Schimpf, G., Sengupta, B., Solmaz, G.: The EU AI act and the wager on trustworthy AI. Commun. ACM 67(12), 58\u201365 (2024)","journal-title":"Commun. ACM"},{"issue":"2\u20133","key":"6_CR3","doi-asserted-by":"publisher","first-page":"191","DOI":"10.1016\/0098-3004(84)90020-7","volume":"10","author":"JC Bezdek","year":"1984","unstructured":"Bezdek, J.C., Ehrlich, R., Full, W.: FCM: the fuzzy c-means clustering algorithm. Comput. Geosci. 10(2\u20133), 191\u2013203 (1984)","journal-title":"Comput. Geosci."},{"key":"6_CR4","unstructured":"Bhattacharjee, A., Moraffah, R., Garland, J., Liu, H.: Towards LLM-guided causal explainability for black-box text classifiers (2024)"},{"key":"6_CR5","doi-asserted-by":"publisher","first-page":"101556","DOI":"10.1109\/ACCESS.2022.3208957","volume":"10","author":"S Bobek","year":"2022","unstructured":"Bobek, S., Kuk, M., Szel\u0105\u017cek, M., Nalepa, G.J.: Enhancing cluster analysis with explainable AI and multidimensional cluster prototypes. IEEE Access 10, 101556\u2013101574 (2022)","journal-title":"IEEE Access"},{"key":"6_CR6","first-page":"1877","volume":"33","author":"T Brown","year":"2020","unstructured":"Brown, T., et al.: Language models are few-shot learners. Adv. Neural. Inf. Process. Syst. 33, 1877\u20131901 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"6_CR7","doi-asserted-by":"crossref","unstructured":"Burkart, N., Brajovic, D., Huber, M.F.: Explainable AI: introducing trust and comprehensibility to AI engineering. at-Automatisierungstechnik 70(9), 787\u2013792 (2022)","DOI":"10.1515\/auto-2022-0013"},{"key":"6_CR8","doi-asserted-by":"crossref","unstructured":"Cahyawijaya, S., Lovenia, H., Fung, P.: LLMs are few-shot in-context low-resource language learners. arXiv preprint arXiv:2403.16512 (2024)","DOI":"10.18653\/v1\/2024.naacl-long.24"},{"issue":"1","key":"6_CR9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1080\/03610927408827101","volume":"3","author":"T Cali\u0144ski","year":"1974","unstructured":"Cali\u0144ski, T., Harabasz, J.: A dendrite method for cluster analysis. Commun. Stat. Theory Methods 3(1), 1\u201327 (1974)","journal-title":"Commun. Stat. Theory Methods"},{"key":"6_CR10","doi-asserted-by":"crossref","unstructured":"Campello, R.J., Moulavi, D., Sander, J.: Density-based clustering based on hierarchical density estimates. In: Pacific-Asia Conference on Knowledge Discovery and Data Mining, pp. 160\u2013172. Springer (2013)","DOI":"10.1007\/978-3-642-37456-2_14"},{"issue":"1","key":"6_CR11","doi-asserted-by":"publisher","first-page":"78","DOI":"10.3390\/make5010006","volume":"5","author":"T Clement","year":"2023","unstructured":"Clement, T., Kemmerzell, N., Abdelaal, M., Amberg, M.: XAIR: a systematic metareview of explainable AI (XAI) aligned to the software development process. Mach. Learn. Knowl. Extr. 5(1), 78\u2013108 (2023)","journal-title":"Mach. Learn. Knowl. Extr."},{"key":"6_CR12","doi-asserted-by":"publisher","first-page":"224","DOI":"10.1109\/TPAMI.1979.4766909","volume":"2","author":"DL Davies","year":"1979","unstructured":"Davies, D.L., Bouldin, D.W.: A cluster separation measure. IEEE Trans. Pattern Anal. Mach. Intell. 2, 224\u2013227 (1979)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"6_CR13","doi-asserted-by":"crossref","unstructured":"Dhanorkar, S., Wolf, C.T., Qian, K., Xu, A., Popa, L., Li, Y.: Who needs to know what, when?: Broadening the explainable AI (XAI) design space by looking at explanations across the AI lifecycle. In: Proceedings of the 2021 ACM Designing Interactive Systems Conference, pp. 1591\u20131602 (2021)","DOI":"10.1145\/3461778.3462131"},{"issue":"6","key":"6_CR14","doi-asserted-by":"publisher","first-page":"788","DOI":"10.1007\/s11633-020-1243-2","volume":"17","author":"PD Doma\u0144ski","year":"2020","unstructured":"Doma\u0144ski, P.D.: Study on statistical outlier detection and labelling. Int. J. Autom. Comput. 17(6), 788\u2013811 (2020)","journal-title":"Int. J. Autom. Comput."},{"key":"6_CR15","doi-asserted-by":"publisher","first-page":"375","DOI":"10.1016\/j.ins.2021.04.089","volume":"571","author":"M Du","year":"2021","unstructured":"Du, M., Wang, R., Ji, R., Wang, X., Dong, Y.: ROBP a robust border-peeling clustering using Cauchy kernel. Inf. Sci. 571, 375\u2013400 (2021)","journal-title":"Inf. Sci."},{"issue":"1","key":"6_CR16","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1080\/01969727408546059","volume":"4","author":"JC Dunn","year":"1974","unstructured":"Dunn, J.C.: Well-separated clusters and optimal fuzzy partitions. J. Cybern. 4(1), 95\u2013104 (1974)","journal-title":"J. Cybern."},{"key":"6_CR17","unstructured":"Ester, M., Kriegel, H.P., Sander, J., Xu, X., et\u00a0al.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: KDD. vol.\u00a096, pp. 226\u2013231 (1996)"},{"key":"6_CR18","doi-asserted-by":"crossref","unstructured":"Fang, A., Macdonald, C., Ounis, I., Habel, P.: Using word embedding to evaluate the coherence of topics from twitter data. In: Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1057\u20131060 (2016)","DOI":"10.1145\/2911451.2914729"},{"key":"6_CR19","unstructured":"Fang, X., et\u00a0al.: Large language models (LLMs) on tabular data: Prediction, generation, and understanding-a survey (2024)"},{"issue":"7","key":"6_CR20","doi-asserted-by":"publisher","first-page":"730","DOI":"10.1001\/jamainternmed.2022.1906","volume":"182","author":"A Fawzy","year":"2022","unstructured":"Fawzy, A., et al.: Racial and ethnic discrepancy in pulse oximetry and delayed identification of treatment eligibility among patients with covid-19. JAMA Intern. Med. 182(7), 730\u2013738 (2022)","journal-title":"JAMA Intern. Med."},{"key":"6_CR21","unstructured":"Funk, M., et\u00a0al.: Global burden of mental disorders and the need for a comprehensive, coordinated response from health and social sectors at the country level. Retrieved on 30 (2016)"},{"key":"6_CR22","unstructured":"G\u00e9ron, A.: Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow. O\u2019Reilly Media, Inc. (2022)"},{"issue":"1","key":"6_CR23","doi-asserted-by":"publisher","first-page":"26","DOI":"10.1037\/1040-3590.4.1.26","volume":"4","author":"LR Goldberg","year":"1992","unstructured":"Goldberg, L.R.: The development of markers for the big-five factor structure. Psychol. Assess. 4(1), 26 (1992)","journal-title":"Psychol. Assess."},{"key":"6_CR24","unstructured":"Gurnee, W., Tegmark, M.: Language models represent space and time. In: The Twelfth International Conference on Learning Representations (2024). https:\/\/openreview.net\/forum?id=jE8xbmvFin"},{"key":"6_CR25","doi-asserted-by":"publisher","first-page":"357","DOI":"10.3389\/fpubh.2020.00357","volume":"8","author":"C Iwendi","year":"2020","unstructured":"Iwendi, C., et al.: COVID-19 patient health prediction using boosted random forest algorithm. Front. Public Health 8, 357 (2020)","journal-title":"Front. Public Health"},{"issue":"4","key":"6_CR26","doi-asserted-by":"publisher","first-page":"422","DOI":"10.1145\/582415.582418","volume":"20","author":"K J\u00e4rvelin","year":"2002","unstructured":"J\u00e4rvelin, K., Kek\u00e4l\u00e4inen, J.: Cumulated gain-based evaluation of IR techniques. ACM Trans. Inf. Syst. (TOIS) 20(4), 422\u2013446 (2002)","journal-title":"ACM Trans. Inf. Syst. (TOIS)"},{"key":"6_CR27","doi-asserted-by":"publisher","unstructured":"Katevas, K., Arapakis, I., Pielot, M.: Typical phone use habits: intense use does not predict negative well-being. In: Proceedings of the 20th International Conference on Human-Computer Interaction with Mobile Devices and Services. MobileHCI \u201918, Association for Computing Machinery, New York, NY, USA (2018). https:\/\/doi.org\/10.1145\/3229434.3229441","DOI":"10.1145\/3229434.3229441"},{"key":"6_CR28","unstructured":"Laugwitz, B., Held, T., Schrepp, M.: Construction and evaluation of a user experience questionnaire. In: HCI and Usability for Education and Work: 4th Symposium of the Workgroup Human-Computer Interaction and Usability Engineering of the Austrian Computer Society, USAB 2008, Graz, Austria, November 20-21, 2008. Proceedings 4, pp. 63\u201376. Springer (2008)"},{"issue":"7553","key":"6_CR29","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y LeCun","year":"2015","unstructured":"LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436\u2013444 (2015)","journal-title":"Nature"},{"key":"6_CR30","unstructured":"Li, K., Hopkins, A.K., Bau, D., Vi\u00e9gas, F., Pfister, H., Wattenberg, M.: Emergent world representations: exploring a sequence model trained on a synthetic task. In: The Eleventh International Conference on Learning Representations (2023). https:\/\/openreview.net\/forum?id=DeG07_TcZvT"},{"key":"6_CR31","unstructured":"Liao, Q.V., Varshney, K.R.: Human-centered explainable AI (XAI): From algorithms to user experiences. arXiv preprint arXiv:2110.10790 (2021)"},{"key":"6_CR32","unstructured":"Liu, X., et al.: Large language models are few-shot health learners. arXiv preprint arXiv:2305.15525 (2023)"},{"issue":"2","key":"6_CR33","doi-asserted-by":"publisher","first-page":"442","DOI":"10.1002\/ejp.1683","volume":"25","author":"J Loetsch","year":"2021","unstructured":"Loetsch, J., Malkusch, S.: Interpretation of cluster structures in pain-related phenotype data using explainable artificial intelligence (XAI). Eur. J. Pain 25(2), 442\u2013465 (2021)","journal-title":"Eur. J. Pain"},{"key":"6_CR34","doi-asserted-by":"crossref","unstructured":"Ma, L., Thakurdesai, N., Chen, J., Xu, J., Korpeoglu, E., Kumar, S., Achan, K.: LLMs with user-defined prompts as generic data operators for reliable data processing. In: 2023 IEEE International Conference on Big Data (BigData), pp. 3144\u20133148. IEEE (2023)","DOI":"10.1109\/BigData59044.2023.10386472"},{"key":"6_CR35","doi-asserted-by":"crossref","unstructured":"Ma, Q., Ren, X., Huang, C.: XRec: Large language models for explainable recommendation. arXiv preprint arXiv:2406.02377 (2024)","DOI":"10.18653\/v1\/2024.findings-emnlp.22"},{"key":"6_CR36","unstructured":"MacQueen, J., et\u00a0al.: Some methods for classification and analysis of multivariate observations. In: Proceedings of the fifth Berkeley Symposium on Mathematical Statistics and Probability. vol.\u00a01, pp. 281\u2013297. Oakland, CA, USA (1967)"},{"key":"6_CR37","unstructured":"Mavrepis, P., Makridis, G., Fatouros, G., Koukos, V., Separdani, M.M., Kyriazis, D.: XAI for all: Can large language models simplify explainable AI? arXiv preprint arXiv:2401.13110 (2024)"},{"key":"6_CR38","unstructured":"Molnar, C.: Interpretable machine learning. Lulu. com (2020)"},{"issue":"1","key":"6_CR39","doi-asserted-by":"publisher","first-page":"13","DOI":"10.20982\/tqmp.04.1.p013","volume":"4","author":"N Nachar","year":"2008","unstructured":"Nachar, N., et al.: The Mann-Whitney U: a test for assessing whether two independent samples come from the same distribution. Tutorials Quant. Methods Psychol. 4(1), 13\u201320 (2008)","journal-title":"Tutorials Quant. Methods Psychol."},{"key":"6_CR40","first-page":"27730","volume":"35","author":"L Ouyang","year":"2022","unstructured":"Ouyang, L., et al.: Training language models to follow instructions with human feedback. Adv. Neural. Inf. Process. Syst. 35, 27730\u201327744 (2022)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"issue":"3","key":"6_CR41","doi-asserted-by":"publisher","first-page":"391","DOI":"10.1080\/10447318.2022.2153320","volume":"39","author":"O Ozmen Garibay","year":"2023","unstructured":"Ozmen Garibay, O., et al.: Six human-centered artificial intelligence grand challenges. Int. J. Hum. Comput. Interact. 39(3), 391\u2013437 (2023)","journal-title":"Int. J. Hum. Comput. Interact."},{"issue":"3","key":"6_CR42","doi-asserted-by":"publisher","first-page":"487","DOI":"10.1016\/j.patcog.2003.06.005","volume":"37","author":"MK Pakhira","year":"2004","unstructured":"Pakhira, M.K., Bandyopadhyay, S., Maulik, U.: Validity index for crisp and fuzzy clusters. Pattern Recogn. 37(3), 487\u2013501 (2004)","journal-title":"Pattern Recogn."},{"key":"6_CR43","unstructured":"Peng, Y., et al.: Uncertainty-aware explainable recommendation with large language models. arXiv preprint arXiv:2402.03366 (2024)"},{"key":"6_CR44","unstructured":"Pitler, E., Louis, A., Nenkova, A.: Automatic evaluation of linguistic quality in multi-document summarization. In: Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, pp. 544\u2013554 (2010)"},{"key":"6_CR45","unstructured":"Qin, W., Chen, Z., Wang, L., Lan, Y., Ren, W., Hong, R.: Read, diagnose and chat: Towards explainable and interactive LLMs-augmented depression detection in social media. arXiv preprint arXiv:2305.05138 (2023)"},{"key":"6_CR46","unstructured":"Ramlochan, S.: The black box problem: Opaque inner workings of large language models. Prompt Engineering (2024). https:\/\/promptengineering.org\/the-black-box-problem-opaque-inner-workings-of-large-language-models\/#what-is-the-llm-black-box-problem. Accessed 29 June 2024"},{"key":"6_CR47","doi-asserted-by":"crossref","unstructured":"Ribeiro, M.T., Singh, S., Guestrin, C.: \u201cwhy should i trust you?\u201d Explaining the predictions of any classifier. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1135\u20131144 (2016)","DOI":"10.1145\/2939672.2939778"},{"key":"6_CR48","doi-asserted-by":"crossref","unstructured":"Ribeiro, M.T., Singh, S., Guestrin, C.: Anchors: high-precision model-agnostic explanations. In: Proceedings of the AAAI Conference on Artificial Intelligence. vol.\u00a032 (2018)","DOI":"10.1609\/aaai.v32i1.11491"},{"key":"6_CR49","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1016\/0377-0427(87)90125-7","volume":"20","author":"PJ Rousseeuw","year":"1987","unstructured":"Rousseeuw, P.J.: Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J. Comput. Appl. Math. 20, 53\u201365 (1987)","journal-title":"J. Comput. Appl. Math."},{"key":"6_CR50","unstructured":"Rozario, S., \u010cevora, G.: Explainable AI does not provide the explanations end-users are asking for. arXiv preprint arXiv:2302.11577 (2023)"},{"key":"6_CR51","doi-asserted-by":"crossref","unstructured":"Sedgwick, P.: Spearman\u2019s rank correlation coefficient. BMJ 349 (2014)","DOI":"10.1136\/bmj.g7327"},{"key":"6_CR52","volume-title":"Automated readability index","author":"R Senter","year":"1967","unstructured":"Senter, R., Smith, E.A.: Automated readability index. Tech. rep, Technical report, DTIC document (1967)"},{"issue":"12","key":"6_CR53","doi-asserted-by":"publisher","first-page":"2176","DOI":"10.1038\/s41591-021-01595-0","volume":"27","author":"L Seyyed-Kalantari","year":"2021","unstructured":"Seyyed-Kalantari, L., Zhang, H., McDermott, M.B., Chen, I.Y., Ghassemi, M.: Underdiagnosis bias of artificial intelligence algorithms applied to chest radiographs in under-served patient populations. Nat. Med. 27(12), 2176\u20132182 (2021)","journal-title":"Nat. Med."},{"issue":"8","key":"6_CR54","doi-asserted-by":"publisher","first-page":"888","DOI":"10.1109\/34.868688","volume":"22","author":"J Shi","year":"2000","unstructured":"Shi, J., Malik, J.: Normalized cuts and image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 22(8), 888\u2013905 (2000)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"6_CR55","unstructured":"Spielberger, C.D., Sydeman, S.J., Owen, A., Marsh, B.J.: Measuring anxiety and anger with the state-trait anxiety inventory (STAI) and the state-trait anger expression inventory (STAXI). (1999). https:\/\/api.semanticscholar.org\/CorpusID:150086849"},{"key":"6_CR56","unstructured":"Sultanpure, K., Shirsath, B., Bhande, B., Sawai, H., Gawade, S., Samgir, S.: Hair and scalp disease detection using deep learning. arXiv preprint arXiv:2403.07940 (2024)"},{"key":"6_CR57","unstructured":"Swamy, V., Frej, J., K\u00e4ser, T.: The future of human-centric explainable artificial intelligence (XAI) is not post-hoc explanations. arXiv preprint arXiv:2307.00364 (2023)"},{"key":"6_CR58","unstructured":"Vadlapudi, R., Katragadda, R.: On automated evaluation of readability of summaries: capturing grammaticality, focus, structure and coherence. In: Proceedings of the NAACL HLT 2010 Student Research Workshop, pp. 7\u201312 (2010)"},{"key":"6_CR59","first-page":"841","volume":"31","author":"S Wachter","year":"2017","unstructured":"Wachter, S., Mittelstadt, B., Russell, C.: Counterfactual explanations without opening the black box: Automated decisions and the GDPR. Harv. JL Tech. 31, 841 (2017)","journal-title":"Harv. JL Tech."},{"issue":"S1","key":"6_CR60","doi-asserted-by":"publisher","first-page":"S266","DOI":"10.1139\/h11-062","volume":"36","author":"DE Warburton","year":"2011","unstructured":"Warburton, D.E., et al.: Evidence-based risk assessment and recommendations for physical activity clearance: consensus document 2011. Appl. Physiol. Nutr. Metab. 36(S1), S266\u2013S298 (2011)","journal-title":"Appl. Physiol. Nutr. Metab."},{"issue":"6","key":"6_CR61","doi-asserted-by":"publisher","first-page":"1063","DOI":"10.1037\/0022-3514.54.6.1063","volume":"54","author":"D Watson","year":"1988","unstructured":"Watson, D., Clark, L.A., Tellegen, A.: Development and validation of brief measures of positive and negative affect: the PANAS scales. J. Pers. Soc. Psychol. 54(6), 1063 (1988)","journal-title":"J. Pers. Soc. Psychol."},{"key":"6_CR62","doi-asserted-by":"publisher","first-page":"154","DOI":"10.1016\/j.inffus.2022.11.013","volume":"92","author":"L Weber","year":"2023","unstructured":"Weber, L., Lapuschkin, S., Binder, A., Samek, W.: Beyond explaining: opportunities and challenges of XAI-based model improvement. Inf. Fusion 92, 154\u2013176 (2023)","journal-title":"Inf. Fusion"},{"issue":"08","key":"6_CR63","doi-asserted-by":"publisher","first-page":"841","DOI":"10.1109\/34.85677","volume":"13","author":"XL Xie","year":"1991","unstructured":"Xie, X.L., Beni, G.: A validity measure for fuzzy clustering. IEEE Trans. Pattern Anal. Mach. Intell. 13(08), 841\u2013847 (1991)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"6","key":"6_CR64","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3653304","volume":"18","author":"J Yang","year":"2024","unstructured":"Yang, J., et al.: Harnessing the power of LLMs in practice: a survey on ChatGPT and beyond. ACM Trans. Knowl. Discov. Data 18(6), 1\u201332 (2024)","journal-title":"ACM Trans. Knowl. Discov. Data"},{"key":"6_CR65","doi-asserted-by":"crossref","unstructured":"Yfantidou, S., et al.: LifeSnaps, a 4-month multi-modal dataset capturing unobtrusive snapshots of our lives in the wild. Sci. Data 9(1), 663 (2022)","DOI":"10.1038\/s41597-022-01764-x"},{"key":"6_CR66","doi-asserted-by":"crossref","unstructured":"Yu, X., Chen, Z., Ling, Y., Dong, S., Liu, Z., Lu, Y.: Temporal data meets LLM\u2013explainable financial time series forecasting. arXiv preprint arXiv:2306.11025 (2023)","DOI":"10.18653\/v1\/2023.emnlp-industry.69"},{"key":"6_CR67","unstructured":"Zhang, X., Guo, Y., Stepputtis, S., Sycara, K., Campbell, J.: Explaining agent behavior with large language models. arXiv preprint arXiv:2309.10346 (2023)"}],"container-title":["Communications in Computer and Information Science","Explainable Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-08333-3_6","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,18]],"date-time":"2025-10-18T06:02:31Z","timestamp":1760767351000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-08333-3_6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,19]]},"ISBN":["9783032083326","9783032083333"],"references-count":67,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-08333-3_6","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"value":"1865-0929","type":"print"},{"value":"1865-0937","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,19]]},"assertion":[{"value":"19 October 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"xAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"World Conference on Explainable Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Istanbul","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"T\u00fcrkiye","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9 July 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 July 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"xai2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/xaiworldconference.com\/2025\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}