{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T16:14:04Z","timestamp":1776442444710,"version":"3.51.2"},"reference-count":120,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2024,11,11]],"date-time":"2024-11-11T00:00:00Z","timestamp":1731283200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>This study seeks to understand the key success factors that underpin efficiency, transparency, and user trust in automated decision support systems (DSS) that leverage AI technologies across industries. The aim of this study is to facilitate more accurate decision-making with such AI-based DSS, as well as build trust through the need for visibility and explainability by increasing user acceptance. This study primarily examines the nature of AI-based DSS adoption and the challenges of maintaining system transparency and improving accuracy. The results provide practical guidance for professionals and decision-makers to develop AI-driven decision support systems that are not only effective but also trusted by users. The results are also important to gain insight into how artificial intelligence fits into and combines with decision-making, which can be derived from research when thinking about embedding systems in ethical standards.<\/jats:p>","DOI":"10.3390\/info15110725","type":"journal-article","created":{"date-parts":[[2024,11,12]],"date-time":"2024-11-12T06:28:32Z","timestamp":1731392912000},"page":"725","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":55,"title":["AI for Decision Support: Balancing Accuracy, Transparency, and Trust Across Sectors"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3521-4757","authenticated-orcid":false,"given":"Attila","family":"Kovari","sequence":"first","affiliation":[{"name":"Institute of Digital Technology, Faculty of Informatics, Eszterh\u00e1zy K\u00e1roly Catholic University, 3300 Eger, Hungary"},{"name":"Institute of Engineering, University of Dunaujvaros, 2400 Dunaujvaros, Hungary"},{"name":"GAMF Faculty of Engineering and Computer Science, John von Neumann University, 6000 Kecskemet, Hungary"},{"name":"Institute of Electronics and Communication Systems, Kand\u00f3 K\u00e1lm\u00e1n Faculty of Electrical Engineering, \u00d3buda University, 1034 Budapest, Hungary"}]}],"member":"1968","published-online":{"date-parts":[[2024,11,11]]},"reference":[{"key":"ref_1","first-page":"666","article-title":"A Machine Learning Approach for Skin Lesion Classification on iOS: Implementing and Optimizing a Convolutional Transfer Learning Model with Create ML","volume":"46","author":"Katona","year":"2024","journal-title":"Int. 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