{"title":"在医疗保健领域的人工智能部署中建立信任","authors":"Juan Cadavid, Daniela Piana, Antonin Abhervé","doi":"10.1109/ACDSA59508.2024.10467853","DOIUrl":null,"url":null,"abstract":"Artificial Intelligence (AI) has seamlessly integrated into the fabric of modern organizations, revolutionizing business processes, decision-making, and interactions with society. However, instilling trust in AI systems remains a formidable challenge, particularly in vital sectors such as healthcare. We introduce the \"Trust Octagon\" - a framework comprising eight key dimensions categorized into three critical domains. This framework serves as a guide, tailored for organizations and policymakers, to fortify trust in AI. We apply the Trust Octagon within the landscape of healthcare. Our approach yields a set of meticulously crafted checklists, strategically designed to nurture trust in AI implementations. To support the practical application of this framework, we unveil a robust toolkit integrated with the Modelio toolset for enterprise architecture and system design. This approach ensures that building trust in AI systems is not only an aspiration but a tangible reality, propelling the responsible and ethical integration of AI into critical industries like healthcare.","PeriodicalId":518964,"journal":{"name":"2024 International Conference on Artificial Intelligence, Computer, Data Sciences and Applications (ACDSA)","volume":"288 2","pages":"1-6"},"PeriodicalIF":0.0000,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Building Trust in AI deployments in Healthcare\",\"authors\":\"Juan Cadavid, Daniela Piana, Antonin Abhervé\",\"doi\":\"10.1109/ACDSA59508.2024.10467853\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Artificial Intelligence (AI) has seamlessly integrated into the fabric of modern organizations, revolutionizing business processes, decision-making, and interactions with society. However, instilling trust in AI systems remains a formidable challenge, particularly in vital sectors such as healthcare. We introduce the \\\"Trust Octagon\\\" - a framework comprising eight key dimensions categorized into three critical domains. This framework serves as a guide, tailored for organizations and policymakers, to fortify trust in AI. We apply the Trust Octagon within the landscape of healthcare. Our approach yields a set of meticulously crafted checklists, strategically designed to nurture trust in AI implementations. To support the practical application of this framework, we unveil a robust toolkit integrated with the Modelio toolset for enterprise architecture and system design. This approach ensures that building trust in AI systems is not only an aspiration but a tangible reality, propelling the responsible and ethical integration of AI into critical industries like healthcare.\",\"PeriodicalId\":518964,\"journal\":{\"name\":\"2024 International Conference on Artificial Intelligence, Computer, Data Sciences and Applications (ACDSA)\",\"volume\":\"288 2\",\"pages\":\"1-6\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-02-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2024 International Conference on Artificial Intelligence, Computer, Data Sciences and Applications (ACDSA)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ACDSA59508.2024.10467853\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2024 International Conference on Artificial Intelligence, Computer, Data Sciences and Applications (ACDSA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ACDSA59508.2024.10467853","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
摘要
人工智能(AI)已无缝融入现代组织结构,彻底改变了业务流程、决策以及与社会的互动。然而,如何让人们信任人工智能系统仍然是一项艰巨的挑战,尤其是在医疗保健等重要领域。我们引入了 "信任八边形"--一个由八个关键维度组成的框架,分为三个关键领域。该框架是为组织和政策制定者量身定制的指南,旨在加强对人工智能的信任。我们将 "信任八边形 "应用于医疗保健领域。我们的方法产生了一套精心制作的核对表,其战略设计旨在培养对人工智能实施的信任。为了支持这一框架的实际应用,我们推出了一个与用于企业架构和系统设计的 Modelio 工具集相集成的强大工具包。这种方法可确保在人工智能系统中建立信任不仅是一种愿望,而且是一种切实的现实,从而推动人工智能以负责任和合乎道德的方式融入医疗保健等关键行业。
Artificial Intelligence (AI) has seamlessly integrated into the fabric of modern organizations, revolutionizing business processes, decision-making, and interactions with society. However, instilling trust in AI systems remains a formidable challenge, particularly in vital sectors such as healthcare. We introduce the "Trust Octagon" - a framework comprising eight key dimensions categorized into three critical domains. This framework serves as a guide, tailored for organizations and policymakers, to fortify trust in AI. We apply the Trust Octagon within the landscape of healthcare. Our approach yields a set of meticulously crafted checklists, strategically designed to nurture trust in AI implementations. To support the practical application of this framework, we unveil a robust toolkit integrated with the Modelio toolset for enterprise architecture and system design. This approach ensures that building trust in AI systems is not only an aspiration but a tangible reality, propelling the responsible and ethical integration of AI into critical industries like healthcare.