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While machine learning and deep learning algorithms have advanced significantly, their growing complexity presents persistent interpretability challenges. Existing maturity frameworks, such as Capability Maturity Model Integration, fall short in addressing the distinct requirements of explainability in AI systems, particularly where ethical compliance and public trust are paramount. To address this gap, we propose the Maturity Model for eXplainable Artificial Intelligence: Analysis and Evaluation (MM4XAI\u2010AE), a domain\u2010agnostic maturity model tailored to assess and guide the practical deployment of explainability in AI\u2010based applications. The model integrates two complementary components: an analysis model and an evaluation model, structured across four maturity levels\u2014operational, justified, formalized, and managed. It evaluates explainability across three critical dimensions: technical foundations, structured design, and human\u2010centered explainability. MM4XAI\u2010AE is grounded in the PAG\u2010XAI framework, emphasizing the interrelated dimensions of practicality, auditability, and governance, thereby aligning with current reflections on responsible and trustworthy AI. The MM4XAI\u2010AE model is empirically validated through a structured evaluation of thirteen published AI applications from diverse sectors, analyzing their design and deployment practices. The results show a wide distribution across maturity levels, underscoring the model\u2019s capacity to identify strengths, gaps, and actionable pathways for improving explainability. This work offers a structured and scalable framework to standardize explainability practices and supports researchers, developers, and policymakers in fostering more transparent, ethical, and trustworthy AI systems.<\/jats:p>","DOI":"10.1155\/int\/4934696","type":"journal-article","created":{"date-parts":[[2025,6,24]],"date-time":"2025-06-24T03:49:16Z","timestamp":1750736956000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["A Maturity Model for Practical Explainability in Artificial Intelligence\u2010Based Applications: Integrating Analysis and Evaluation (MM4XAI\u2010AE) Models"],"prefix":"10.1155","volume":"2025","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9393-6139","authenticated-orcid":false,"given":"Juli\u00e1n","family":"Mu\u00f1oz-Ord\u00f3\u00f1ez","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6263-1911","authenticated-orcid":false,"given":"Carlos","family":"Cobos","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1113-9003","authenticated-orcid":false,"given":"Juan C.","family":"Vidal-Rojas","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7283-312X","authenticated-orcid":false,"given":"Francisco","family":"Herrera","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2025,6,24]]},"reference":[{"key":"e_1_2_10_1_2","doi-asserted-by":"publisher","DOI":"10.1609\/aimag.v38i3.2741"},{"key":"e_1_2_10_2_2","doi-asserted-by":"publisher","DOI":"10.1002\/lt.25772"},{"key":"e_1_2_10_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2019.12.012"},{"key":"e_1_2_10_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2021.05.009"},{"key":"e_1_2_10_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2025.103133"},{"key":"e_1_2_10_6_2","unstructured":"European Commission Regulation (EU) 2024\/1689 of the European Parliament and of the Council of 13 June 2024 Laying down Harmonised Rules on Artificial Intelligence (Artificial Intelligence Act) 2025 https:\/\/eur-lex.europa.eu\/eli\/reg\/2024\/1689\/oj."},{"key":"e_1_2_10_7_2","doi-asserted-by":"publisher","DOI":"10.1145\/3593434.3593444"},{"key":"e_1_2_10_8_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2023.101805"},{"key":"e_1_2_10_9_2","first-page":"146","article-title":"Modelos De Madurez Y Su Idoneidad Para Aplicar En Peque\u00f1as Y Medianas Empresas","volume":"35","author":"P\u00e9rez-Mergarejo E.","year":"2014","journal-title":"Maturity models and the suitability of its application in small and medium enterprises"},{"key":"e_1_2_10_10_2","volume-title":"Software Engineering Institute, Carnegie Mellon University","author":"Cmmi Product Team","year":"2002"},{"key":"e_1_2_10_11_2","unstructured":"GilbertN. 12 Current AI Trends & Predictions for 2021\/2022 According to Experts: Financesonline.Com 2025 https:\/\/financesonline.com\/ai-trends\/."},{"key":"e_1_2_10_12_2","doi-asserted-by":"crossref","unstructured":"JohnM. 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