{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,23]],"date-time":"2025-11-23T08:16:32Z","timestamp":1763885792801,"version":"3.45.0"},"reference-count":0,"publisher":"Association for the Advancement of Artificial Intelligence (AAAI)","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["AAAI-SS"],"abstract":"<jats:p>Principles-based frameworks for AI assurance have been\nproposed for various AI\/ML use cases, focusing on aspects\nsuch as ethical design, trustworthiness, and safety.\nHowever, translating these high-level principles into\nactionable, objective criteria for auditing, particularly\nby third parties, remains challenging. Our analysis shows\nthis is due to the inherent subjectivity of principles, the\nneed for vertical frameworks tailored to specific AI\/ML\napplications, and the unreliability of information gathered\nduring the assurance process. In this paper, we present a\ncase study on how to develop and operationalise a\nprinciples-based framework for AI assurance aimed at\nassessing the \u2018accuracy\u2019 of child sexual exploitation\n(CSEA) and terrorism detection technologies in the context\nof online safety. The proposed assurance framework\naddresses a requirement in the UK's 2023 Online Safety Act\nto create an 'accreditation' scheme specifically for CSEA\nand terrorism detection technologies. We discuss the\ncritical challenges for operationalising such\nprinciples-based frameworks for assurance, particularly in\nrelation to ensuring transparency, reliability, and\nconsistency in audits. We also map potential issues which\nremain for effectively assessing and auditing AI\/ML\ntechnologies, informing the development of future research\nagendas which further research and development of robust\nstandards for assurance, particularly in sociotechnical\ncontexts.<\/jats:p>","DOI":"10.1609\/aaaiss.v7i1.36860","type":"journal-article","created":{"date-parts":[[2025,11,23]],"date-time":"2025-11-23T08:15:12Z","timestamp":1763885712000},"page":"2-10","source":"Crossref","is-referenced-by-count":0,"title":["From AI Principles to AI Assurance: an Online Safety Case\nStudy"],"prefix":"10.1609","volume":"7","author":[{"given":"Miranda","family":"Cross","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andreas","family":"Gutmann","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ismini","family":"Psychoula","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pedro","family":"Friere","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"9382","published-online":{"date-parts":[[2025,11,23]]},"container-title":["Proceedings of the AAAI Symposium Series"],"original-title":[],"link":[{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI-SS\/article\/download\/36860\/38998","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI-SS\/article\/download\/36860\/38998","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,23]],"date-time":"2025-11-23T08:15:13Z","timestamp":1763885713000},"score":1,"resource":{"primary":{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI-SS\/article\/view\/36860"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,23]]},"references-count":0,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,11,23]]}},"URL":"https:\/\/doi.org\/10.1609\/aaaiss.v7i1.36860","relation":{},"ISSN":["2994-4317"],"issn-type":[{"value":"2994-4317","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,23]]}}}