{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,7]],"date-time":"2026-08-07T14:40:05Z","timestamp":1786113605380,"version":"3.56.0"},"reference-count":43,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2024,5,9]],"date-time":"2024-05-09T00:00:00Z","timestamp":1715212800000},"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>This paper addresses the critical gaps in existing AI risk management frameworks, emphasizing the neglect of human factors and the absence of metrics for socially related or human threats. Drawing from insights provided by NIST AI RFM and ENISA, the research underscores the need for understanding the limitations of human-AI interaction and the development of ethical and social measurements. The paper explores various dimensions of trustworthiness, covering legislation, AI cyber threat intelligence, and characteristics of AI adversaries. It delves into technical threats and vulnerabilities, including data access, poisoning, and backdoors, highlighting the importance of collaboration between cybersecurity engineers, AI experts, and social-psychology-behavior-ethics professionals. Furthermore, the socio-psychological threats associated with AI integration into society are examined, addressing issues such as bias, misinformation, and privacy erosion. The manuscript proposes a comprehensive approach to AI trustworthiness, combining technical and social mitigation measures, standards, and ongoing research initiatives. Additionally, it introduces innovative defense strategies, such as cyber-social exercises, digital clones, and conversational agents, to enhance understanding of adversary profiles and fortify AI security. The paper concludes with a call for interdisciplinary collaboration, awareness campaigns, and continuous research efforts to create a robust and resilient AI ecosystem aligned with ethical standards and societal expectations.<\/jats:p>","DOI":"10.3389\/fdata.2024.1381163","type":"journal-article","created":{"date-parts":[[2024,5,9]],"date-time":"2024-05-09T05:14:45Z","timestamp":1715231685000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":34,"title":["Challenges and efforts in managing AI trustworthiness risks: a state of knowledge"],"prefix":"10.3389","volume":"7","author":[{"given":"Nineta","family":"Polemi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Isabel","family":"Pra\u00e7a","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kitty","family":"Kioskli","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Adrien","family":"B\u00e9cue","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2024,5,9]]},"reference":[{"key":"B1","volume-title":"Guide de recommandations pour la sp\u00e9cification et la qualification de syst\u00e8mes int\u00e9grant de l'intelligence artificielle","author":"Aufrant","year":"2020"},{"key":"B2","year":"2018","journal-title":"Supervisory Guidance on Model Risk Management"},{"key":"B3","first-page":"59","article-title":"\u201cFairness and abstraction in sociotechnical systems,\u201d","volume-title":"Proceedings of the ACM Conference on Fairness, Accountability, and Transparency","author":"Barocas","year":"2019"},{"key":"B4","first-page":"16","article-title":"\u201cCan machine learning be secure?\u201d","author":"Barreno","year":"2006","journal-title":"Proceedings of the ACM Symposium on Information, Computer and Communications Security"},{"key":"B5","doi-asserted-by":"publisher","first-page":"2154","DOI":"10.1145\/3243734.3264418","article-title":"Wild patterns: ten years afte the rise of adversarial machine learning","volume":"2018","author":"Biggio","year":"2018","journal-title":"ACM SIGSAC Conf. 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