{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T02:08:27Z","timestamp":1779329307458,"version":"3.51.4"},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,7]]},"abstract":"<jats:p>To improve and ensure trustworthiness and ethics on Artificial Intelligence (AI) systems, several initiatives around the globe are producing principles and recommendations, which are providing to be difficult to translate into technical solutions. A common trait among ethical AI requirements is accountability that aims at ensuring responsibility, auditability, and reduction of negative impact of AI systems. To put accountability into practice, this paper presents the Global-view Accountability Framework (GAF) that considers auditability and redress of conflicting information arising from a context with two or more AI systems which can produce a negative impact. A technical implementation of the framework for automotive and motor insurance is demonstrated, where the focus is on preventing and reporting harm rendered by autonomous vehicles.<\/jats:p>","DOI":"10.24963\/ijcai.2020\/768","type":"proceedings-article","created":{"date-parts":[[2020,7,9]],"date-time":"2020-07-09T02:12:49Z","timestamp":1594260769000},"page":"5276-5278","source":"Crossref","is-referenced-by-count":14,"title":["Putting Accountability of AI Systems into Practice"],"prefix":"10.24963","author":[{"given":"Beatriz San","family":"Miguel","sequence":"first","affiliation":[{"name":"Fujitsu Laboratories of Europe"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Aisha","family":"Naseer","sequence":"additional","affiliation":[{"name":"Fujitsu Laboratories of Europe"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hiroya","family":"Inakoshi","sequence":"additional","affiliation":[{"name":"Fujitsu Laboratories of Europe"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"10584","event":{"name":"Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}","theme":"Artificial Intelligence","location":"Yokohama, Japan","acronym":"IJCAI-PRICAI-2020","number":"28","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"start":{"date-parts":[[2020,7,11]]},"end":{"date-parts":[[2020,7,17]]}},"container-title":["Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2020,7,9]],"date-time":"2020-07-09T02:17:07Z","timestamp":1594261027000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2020\/768"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2020,7]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2020\/768","relation":{},"subject":[],"published":{"date-parts":[[2020,7]]}}}