{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T14:56:08Z","timestamp":1776092168779,"version":"3.50.1"},"reference-count":212,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2024,11,29]],"date-time":"2024-11-29T00:00:00Z","timestamp":1732838400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Big Data"],"abstract":"<jats:p>Artificial intelligence (AI) technologies (re-)shape modern life, driving innovation in a wide range of sectors. However, some AI systems have yielded unexpected or undesirable outcomes or have been used in questionable manners. As a result, there has been a surge in public and academic discussions about aspects that AI systems must fulfill to be considered trustworthy. In this paper, we synthesize existing conceptualizations of trustworthy AI along six requirements: (1) human agency and oversight, (2) fairness and non-discrimination, (3) transparency and explainability, (4) robustness and accuracy, (5) privacy and security, and (6) accountability. For each one, we provide a definition, describe how it can be established and evaluated, and discuss requirement-specific research challenges. Finally, we conclude this analysis by identifying overarching research challenges across the requirements with respect to (1) interdisciplinary research, (2) conceptual clarity, (3) context-dependency, (4) dynamics in evolving systems, and (5) investigations in real-world contexts. Thus, this paper synthesizes and consolidates a wide-ranging and active discussion currently taking place in various academic sub-communities and public forums. It aims to serve as a reference for a broad audience and as a basis for future research directions.<\/jats:p>","DOI":"10.3389\/fdata.2024.1467222","type":"journal-article","created":{"date-parts":[[2024,11,29]],"date-time":"2024-11-29T07:14:55Z","timestamp":1732864495000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":26,"title":["Establishing and evaluating trustworthy AI: overview and research challenges"],"prefix":"10.3389","volume":"7","author":[{"given":"Dominik","family":"Kowald","sequence":"first","affiliation":[]},{"given":"Sebastian","family":"Scher","sequence":"additional","affiliation":[]},{"given":"Viktoria","family":"Pammer-Schindler","sequence":"additional","affiliation":[]},{"given":"Peter","family":"M\u00fcllner","sequence":"additional","affiliation":[]},{"given":"Kerstin","family":"Waxnegger","sequence":"additional","affiliation":[]},{"given":"Lea","family":"Demelius","sequence":"additional","affiliation":[]},{"given":"Angela","family":"Fessl","sequence":"additional","affiliation":[]},{"given":"Maximilian","family":"Toller","sequence":"additional","affiliation":[]},{"given":"Inti Gabriel","family":"Mendoza Estrada","sequence":"additional","affiliation":[]},{"given":"Ilija","family":"\u0160imi\u0107","sequence":"additional","affiliation":[]},{"given":"Vedran","family":"Sabol","sequence":"additional","affiliation":[]},{"given":"Andreas","family":"Tr\u00fcgler","sequence":"additional","affiliation":[]},{"given":"Eduardo","family":"Veas","sequence":"additional","affiliation":[]},{"given":"Roman","family":"Kern","sequence":"additional","affiliation":[]},{"given":"Tomislav","family":"Nad","sequence":"additional","affiliation":[]},{"given":"Simone","family":"Kopeinik","sequence":"additional","affiliation":[]}],"member":"1965","published-online":{"date-parts":[[2024,11,29]]},"reference":[{"key":"B1","doi-asserted-by":"crossref","first-page":"308","DOI":"10.1145\/2976749.2978318","article-title":"\u201cDeep learning with differential privacy,\u201d","volume-title":"Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security","author":"Abadi","year":"2016"},{"key":"B2","doi-asserted-by":"publisher","first-page":"52138","DOI":"10.1109\/ACCESS.2018.2870052","article-title":"Peeking inside the black-box: a survey on explainable artificial intelligence (XAI)","volume":"6","author":"Adadi","year":"2018","journal-title":"IEEE Access"},{"key":"B3","first-page":"9525","article-title":"\u201cSanity checks for saliency maps,\u201d","volume-title":"Proceedings of the 32nd International Conference on Neural Information Processing Systems, NIPS'18","author":"Adebayo","year":"2018"},{"key":"B4","unstructured":"Adilova\n              L.\n            \n            \n              B\u00f6ttinger\n              K.\n            \n            \n              Danos\n              V.\n            \n            \n              Jakob\n              S.\n            \n            \n              Langer\n              F.\n            \n            \n              Markert\n              T.\n            \n          \n          Security of AI-Systems: Fundamentals - Adversarial Deep Learning\n          \n          2022"},{"key":"B5","doi-asserted-by":"publisher","first-page":"1621","DOI":"10.1002\/spe.3216","article-title":"Trustworthy artificial intelligence: a decision-making taxonomy of potential challenges","volume":"54","author":"Akbar","year":"2024","journal-title":"Softw. 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