{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T08:28:21Z","timestamp":1773736101648,"version":"3.50.1"},"reference-count":19,"publisher":"World Scientific Pub Co Pte Ltd","issue":"02","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Artif. Intell. Tools"],"published-print":{"date-parts":[[2026,3]]},"abstract":"<jats:p>\n                    The rapid deployment of Artificial Intelligence (AI) in high-stakes domains necessitates robust approaches to transparency, fairness, and trustworthiness. Current advancements in AI performance are outpacing our understanding and ability to govern these systems. This special issue presents research addressing explainability, fairness, and trust as interconnected socio-technical challenges. Accepted papers demonstrate novel techniques for revealing hidden model dependencies, aligning explanations with domain expertise, rigorously benchmarking model classes for explanation robustness, and refining methods for measuring interpretability. We synthesize these contributions, situate them within current policy and standardization (EU AI Act;\n                    <jats:sup>1<\/jats:sup>\n                    NIST AI RMF;\n                    <jats:sup>2 , 3<\/jats:sup>\n                    ISO\/IEC 23894 and 42001\n                    <jats:sup>4 , 5<\/jats:sup>\n                    ), and connect them to emerging evaluation science in XAI (e.g., BEExAI,\n                    <jats:sup>9<\/jats:sup>\n                    Saliency-Bench,\n                    <jats:sup>10<\/jats:sup>\n                    F-Fidelity\n                    <jats:sup>11<\/jats:sup>\n                    ). Finally, we outline a forward-looking agenda emphasizing multi-aspect evaluation, context-sensitive trust, and the development of governance-ready AI systems.\n                  <\/jats:p>","DOI":"10.1142\/s0218213026020021","type":"journal-article","created":{"date-parts":[[2026,2,11]],"date-time":"2026-02-11T02:41:57Z","timestamp":1770777717000},"source":"Crossref","is-referenced-by-count":0,"title":["Explainable, Fair, and Trustworthy AI: Current Research, Regulatory Developments, and Future Directions"],"prefix":"10.1142","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9610-0230","authenticated-orcid":false,"given":"Sheikh Rabiul","family":"Islam","sequence":"first","affiliation":[{"name":"University at Albany, SUNY, 1400 Washington Avenue, Albany, New York, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1328-3009","authenticated-orcid":false,"given":"Ingrid","family":"Russell","sequence":"additional","affiliation":[{"name":"University of Hartford, 200 Bloomfield Avenue, West Hartford, CT, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8073-1134","authenticated-orcid":false,"given":"Douglas","family":"Talbert","sequence":"additional","affiliation":[{"name":"Tennessee Tech University, 1 William L Jones Dr, Cookeville, TN 38505, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7309-7200","authenticated-orcid":false,"given":"Md Golam Moula","family":"Mehedi Hasan","sequence":"additional","affiliation":[{"name":"Iona University, 715 North Ave, New Rochelle, NY 10804, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"219","published-online":{"date-parts":[[2026,3,3]]},"reference":[{"key":"S0218213026020021BIB001","unstructured":"European Union, Regulation (EU) 2024\/1689 of the European Parliament and of the Council of 13 June 2024 laying down harmonized rules on artificial intelligence (Artificial Intelligence Act). EUR-Lex (2024), https:\/\/eur-lex.europa.eu\/eli\/reg\/2024\/1689\/oj\/eng."},{"key":"S0218213026020021BIB002","unstructured":"National Institute of Standards and Technology, AI Risk Management Framework (AI RMF 1.0) (NIST AI 100-1) (2023), https:\/\/nvlpubs.nist.gov\/nistpubs\/ai\/NIST.AI.100-1.pdf."},{"key":"S0218213026020021BIB003","unstructured":"National Institute of Standards and Technology, Artificial Intelligence Risk Management Framework: Generative AI Profile (NIST-AI-600-1) (2024), https:\/\/www.nist.gov\/itl\/ai-risk-management-framework."},{"key":"S0218213026020021BIB004","unstructured":"ISO\/IEC, ISO\/IEC 23894:2023\u00a0\u2014 Information technology\u00a0\u2014 Artificial intelligence\u00a0\u2014 Guidance on risk management (2023a), https:\/\/www.iso.org\/standard\/77304.html."},{"key":"S0218213026020021BIB005","unstructured":"ISO\/IEC, ISO\/IEC 42001:2023\u00a0\u2014 Information technology \u2014 Artificial intelligence\u00a0\u2014 Management system (2023b), https:\/\/www.iso.org\/standard\/42001."},{"issue":"34","key":"S0218213026020021BIB006","first-page":"1","volume":"24","author":"Hedstr\u00f6m A.","year":"2023","journal-title":"J. Mach. Learn. Res."},{"key":"S0218213026020021BIB007","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2024.acl-long.329"},{"key":"S0218213026020021BIB008","doi-asserted-by":"publisher","DOI":"10.1162\/coli_a_00511"},{"key":"S0218213026020021BIB009","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-63787-2_23"},{"key":"S0218213026020021BIB010","doi-asserted-by":"publisher","DOI":"10.1145\/3711896.3737414"},{"key":"S0218213026020021BIB011","volume-title":"The Thirteenth International Conference on Learning Representations","author":"Zheng X.","year":"2024"},{"key":"S0218213026020021BIB012","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-019-0048-x"},{"key":"S0218213026020021BIB013","first-page":"4593","volume-title":"Proc. 29th Int. Conf. Computational Linguistics","author":"Mosca E.","year":"2022"},{"key":"S0218213026020021BIB014","unstructured":"M. T. Ribeiro and S. Singh and C. Guestrin, Model-agnostic interpretability of machine learning, arXiv:1606.05386 (2016)."},{"key":"S0218213026020021BIB015","unstructured":"C. Etmann, S. Lunz, P. Maass and C. B. Sch\u00f6nlieb, On the connection between adversarial robustness and saliency map interpretability, arXiv:1905.04172 (2019)."},{"key":"S0218213026020021BIB016","doi-asserted-by":"publisher","DOI":"10.1142\/S0218213026400051"},{"key":"S0218213026020021BIB017","doi-asserted-by":"publisher","DOI":"10.1142\/S0218213026400026"},{"key":"S0218213026020021BIB018","author":"Lazar A.","year":"2026","journal-title":"Int. J. Artif. Intell. 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